iabe big data information paper - an actuarial perspective
TRANSCRIPT
BIG DATA: An actuarial perspective
Information Paper
November 2015
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Table of Contents
1 INTRODUCTION 3
2 INTRODUCTION TO BIG DATA 3
2.1 INTRODUCTION AND CHARACTERISTICS 3 2.2 BIG DATA TECHNIQUES AND TOOLS 4 2.3 BIG DATA APPLICATIONS 4 2.4 DATA DRIVEN BUSINESS 5
3 BIG DATA IN INSURANCE VALUE CHAIN 6
3.1 INSURANCE UNDERWRITING 6 3.2 INSURANCE PRICING 8 3.3 INSURANCE RESERVING 10 3.4 CLAIMS MANAGEMENT 11
4 LEGAL ASPECTS OF BIG DATA 13
4.1 INTRODUCTION 13 4.2 DATA PROCESSING 14 4.3 DISCRIMINATION 16
5 NEW FRONTIERS 17
5.1 RISK POOLING VS. PERSONALIZATION 17 5.2 PERSONALISED PREMIUM 18 5.3 FROM INSURANCE TO PREVENTION 18 5.4 THE ALL-‐SEEING INSURER 18 5.5 CHANGE IN INSURANCE BUSINESS 19
6 ACTUARIAL SCIENCES AND THE ROLE OF ACTUARIES 19
6.1 WHAT IS BIG DATA BRINGING FOR THE ACTUARY? 19 6.2 WHAT IS THE ACTUARY BRINGING TO BIG DATA? 20
7 CONCLUSIONS 21
8 REFERENCES 22
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1 Introduction The Internet has started in 1984 linking 1,000 university and corporate labs. In 1998 it grew to 50 million users, while in 2015 it reached 3.2 billion people (44% of the global population). This enormous user growth was combined with an explosion of data that we all produce. Every day we create around 2.5 quintillion bytes of data, information coming from various sources including social media sites, gadgets, smartphones, intelligent homes and cars or industrial sensors to name few. Any company that can combine various datasets and can entail effective data analytics will be able to become more profitable and successful. According to a recent report1 400 large companies who adopted Big Data analytics "have gained a significant lead over the rest of the corporate world." Big data offers big business gains, but also has hidden costs and complexity that companies will have to struggle with. Semi-‐structured and unstructured big data requires new skills and there is shortage of people who mastered data science and can handle mathematics and statistics, programming and possess substantive, domain knowledge. What will be the impact on the insurance sector and the actuarial profession? The concepts of Big Data and predictive modelling are not new to insurers who have already been storing and analysing large quantities of data to achieve deeper insights into customers’ behaviour or setting up insurance premiums. Moreover actuaries are data scientists for insurance and they have all the statistical training and analytical thinking to understand complexity of data combined with the business insights. We look closely on the insurance value chain and assess the impact of Big Data on underwriting, pricing and claims reserving. We examine the ethics of Big Data including data privacy, customer identification, data ownership and the legal aspects. We also discuss new frontiers for insurance and its impact on the actuarial profession. Will actuaries will be able to leverage Big Data, create sophisticated risk models and more personalized insurance offers, and bring new wave of innovation to the market? 2 Introduction to Big Data
2.1 Introduction and characteristics Big Data broadly refers to data sets so large and complex that they cannot be handled by traditional data processing software and it can be defined by the following attributes:
a. Volume: in 2012 it was estimated that 2.5 x 1018 bytes of data was created worldwide every day -‐ this is equivalent to a stack of books from the Sun to Pluto and back again. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, software logs, GPS signals from mobile devices, among others.
b. Variety and Variability: the challenges of Big Data do not only arise from the sheer volume of data but also from the fact that data is generated in multiple forms as a mix of unstructured and structured data, and as a mix of data at rest and data in motion (i.e. static and real time data). Furthermore the meaning of data can change over time or depend on the context. Structured data is organized in a way that both computers and humans can read, for example information stored in traditional databases. Unstructured data refers to data types such as images, audio, video, social media and other information that are not organized or easily interpreted by traditional databases. It includes data generated by machines such as sensors, web feeds, networks or service platforms.
c. Visualization: the insights gained by a company from analysing data must be shared in a way that is efficient and understandable to the company’s stakeholders.
d. Velocity: data is created, saved, analysed and visualized at an increasing speed, making it possible to analyse and visualize high volumes of data in real time.
e. Veracity: it is essential that the data is accurate in order to generate value.
f. Value: the insights gleaned from Big Data can help organizations deepen customer engagement, optimize operations, prevent threats and fraud, and capitalize on new sources of revenue.
1 http://www.bain.com/publications/articles/big_data_the_organizational_challenge.aspx
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2.2 Big Data techniques and tools The Big Data industry has been supported by the following technologies:
a. The Apache Hadoop software library was initially released in December 2011 and is an open source framework that allows for the distributed processing of large data sets across clusters of computers using simple algorithms. It is designed to scale up from one to thousands of machines, each one being a computational and storage unit. The software library is designed under the fundamental assumption that hardware failures are common: the library itself automatically detects and handles hardware failures in order to guarantee that the services provided by a computer cluster will stay available even when the cluster is affected by hardware failures. A wide variety of companies and organizations use Hadoop for both research and production: web-‐based companies that own some of the world’s biggest data warehouses (Amazon, Facebook, Google, Twitter, Yahoo!, ...), media groups, universities among others. A list of Hadoop users and systems is available at http://wiki.apache.org/hadoop/PoweredBy.
b. Non-‐relational databases have existed since the late 1960s but resurfaced in 2009 (under the moniker of Not Only SQL -‐ NOSQL)) as it became clear they are especially well suited to handle the Big Data challenges of volume and variety and as they neatly fit within the Apache Hadoop framework.
c. Cloud Computing is a kind of internet-‐based computing, where shared resources and information are provided to computers and other devices on-‐demand (Wikipedia). A service provider offers computing resources for a fixed price, available online and in general with a high degree of flexibility and reliability. These technologies have been created by major online actors (Amazon, Google) followed by other technology providers (IBM, Microsoft, RedHat). There is a wide variety of architecture Public, Private and Hybride Cloud with all the objective of making computing infrastructure a commodity asset with the best quality/total cost of ownership ratio. Having a nearly infinite amount of computing power at hand with a high flexibility is a key factor for the success of Big Data initiatives.
d. Mining Massive Datasets is a set of methods, algorithms and techniques that can be used to deal with Big Data problems and in particular with volume, variety and velocity issues. PageRank can be seen as a major step (see http://infolab.stanford.edu/pub/papers/google.pdf) and its evolution to a Map-‐Reduce (https://en.wikipedia.org/wiki/MapReduce) approach is definitively a breakthrough. Social Netword Analysis is becoming an area of research in itself that aim to extract useful information from the massive amount of data the Social Networks are providing. These methods are very well suited to run on software such as Hadoop in a Cloud Computing environment.
e. Social Networks is one source of Bid Data that provides a stream of data with a huge value for almost all economic (and even non-‐economic) actors. For most companies, it is the very first time in history they are capable of interacting directly with their customers. Many applications of Big Data make use of these data to provide enhanced services, products and to increase customer satisfaction.
2.3 Big Data Applications Big Data has the potential to change the way academic institutions, corporate and organizations conduct business and change our daily life. Great examples of Big Data applications include:
a. Healthcare: Big Data technologies will have a major impact in healthcare. IBM estimates that 80% of medical data is unstructured and is clinically relevant. Furthermore medical data resides in multiple places like individual medical files, lab and imaging systems, physician notes, medical correspondence, etc. Big Data technologies allow healthcare organizations to bring all the information about an individual together to get insights on how to manage care coordination, outcomes-‐based reimbursement models, patient engagement and outreach programs.
b. Retail: Retailers can get insights for personalizing marketing and improving the effectiveness of marketing campaigns, for optimizing assortment and merchandising decisions, and for removing inefficiencies in distribution and operations. For instance several retailers now incorporate
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Twitter streams into their analysis of loyalty-‐program data. The gained insights make it possible to plan for surges in demand for certain items and to create mobile marketing campaigns targeting specific customers with offers at the times of day they would be most receptive to them.2
c. Politics: Big Data technologies will improve the efficiency and effectiveness across the broad range of government responsibilities. Great example of Big Data use in politics was 2012 analytics and metrics driven Barack Obama’s presidential campaign [1]. Other examples include:
i. Threat and crime prediction and prevention. For instance the Detroit Crime Commission has turned to Big Data in its effort to assist the government and citizens of southeast Michigan in the prevention, investigation and prosecution of neighbourhood crime;3
ii. Detection of fraud, waste and errors in social programs;
iii. Detection of tax fraud and abuse.
d. Cyber risk prevention: companies can analyse data traffic in their computer networks in real time to detect anomalies that may indicate the early stages of a cyber attack. Research firm Gartner estimates that by 2016, more than 25% of global firms will adopt big data analytics for at least one security and fraud detection use case, up from 8% as at 2014.4
e. Insurance fraud detection: Insurance companies can determine a score for each claim in order to target for fraud investigation the claims with the highest scores i.e. the ones that are most likely to be fraudulent. Fraud detection is treated in paragraph 3.4.
f. Usage-‐Based Insurance: is an insurance scheme, where car insurance premiums are calculated based on dynamic causal data, including actual usage and driving behaviour. Telematics data transmitted from a vehicle combined with Big Data analytics enables insurers to distinguish cautious drivers from aggressive drivers and match insurance rate with the actual risk incurred.
2.4 Data driven business The quantity of data is steeply increasing month after month in the world. Some argue it is time to organize and use this information: data must now be viewed as a corporate asset. In order to respond to this arising transformation of business culture, two specific C-‐level roles have thus appeared in the past years, one in the banking and the other in the insurance industry.
2.4.1 The Chief Data Officer The Chief Data Officer (abbreviated to CDO) is the first architect of this “data-‐driven business”. Thanks to his role of coordinator, the CDO will be in charge of the data that drive the company, by:
• defining and setting up a strategy to guarantee their quality, their reliability and their coherency;
• organizing and classifying them; • making them accessible to the right person at the right moment, for the pertinent need and in
the right format.
Thus, the Chief Data Officer needs a strong business background to understand how business runs. The following question will then emerge: to whom should the CDO report? In some firms, the CDO is considered part of the IT, and reports to the CTO (Chief Technology Officer); in others, he holds more of a business role, reporting to the CEO. It’s therefore up to the company to decide, as not two companies are exactly similar from a structural point of view.
Which companies have already a CDO? Generali Group has appointed someone to this newly created position in June 2015. Other companies such as HSBC, Wells Fargo and QBE had already appointed a person to this position in 2013 or 2014. Even Barack Obama appointed a Chief Data Officer/Scientist during his 2012 campaign and the metrics-‐driven decision-‐making campaign played a big role in Obama’s
2 http://asmarterplanet.com/blog/2015/03/surprising-‐insights-‐ibmtwitter-‐alliance.html#more-‐33140 3 http://www.datameer.com/company/news/press-‐releases/detroit-‐crime-‐commission-‐combats-‐crime-‐with-‐datameer-‐big-‐data-‐analytics.html 4 http://www.gartner.com/newsroom/id/2663015
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re-‐election. In the beginning, most of the professionals holding the actual job title “Chief Data Officer” were located in the United States. After a while, Europe followed the move. Also, lots of people did the job in their day-‐to-‐day work, but didn’t necessarily hold the title. Many analysts in the financial sector believe that yet more insurance and banking companies will have to do the move in the following years if they want to stay attractive.
2.4.2 The Chief Analytics Officer Another C-‐level position aroused in the past months: the Chief Analytics Officer (abbreviated to CAO). Are there differences between a CAO and a CDO? Theoretically a CDO focuses on tactical data management, while the CAO concentrates on the strategic deployment of analytics. The latter’s focus is on data analysis to find hidden, but valuable, patterns. These will result in operational decisions that will make the company more competitive, more efficient and more attractive to their potential and current clients. Therefore, the CAO is a normal prolongation of the data-‐driven business: the more analytics are embedded in the organization, the more you need an executive-‐level person to manage that position and communicate the results in an understandable way. The CAO usually reports to the CEO.
In practice, some companies put the CAO responsibilities into the CDO tasks, while others distinguish both positions. Currently, it’s quite rare to find an explicit “Chief Analytics Officer” position in the banking and insurance sector, because of this overlap. But in other fields, the distinction is often made.
3 Big Data in insurance value chain Big Data provides new insights from social networks, telematics sensors, and other new information channels and as a result it allows understanding customer preferences better, enabling new business approaches and products, and enhancing existing internal models, processes and services. With the rise of Big Data the insurance world could fundamentally change and the entire insurance value chain could be impacted starting from underwriting to claims management.
3.1 Insurance underwriting 3.1.1 Introduction In traditional insurance underwriting and actuarial analyses, for years we have been observing a never-‐ending search for more meaningful insight into individual policyholder risk characteristics to distinguish good risks from the bad and to accurately price each risk accordingly. The analytics performed by actuaries, based on advanced mathematical and financial theories, have always been critically important to an insurer’s profitability. Over the last decade, however, revolutionary advances in computing technology and the explosion of new digital data sources have expanded and reinvented the core disciplines of insurers. Today’s advanced analytics in insurance go much further than traditional underwriting and actuarial science. Data mining and predictive modelling is today the way forward for insurers for improving pricing, segmentation and increasing profitability.
3.1.2 What is predictive modelling? Predictive modelling can be defined as the analysis of large historical data sets to identify correlations and interactions and the use of this knowledge to predict future events. For actuaries, the concepts of predictive modelling are not new to the profession. The use of mortality tables to price life insurance products is an example of predictive modelling. The Belgian MK, FK and MR, FR tables showed the relationship between death probability and the explaining variables of age, sex and product type (in this case life insurance or annuity).
Predictive models have been around a long time in sales and marketing environments for example to predict the probability of a customer to buy a new product. Bringing together expertise from both the actuarial profession and marketing analytics can lead to new innovative initiatives where predictive models guide expert decisions in areas such as claims management, fraud detection and underwriting.
3.1.3 From small over medium to Big Data Insurers collect a wealth of information on their customers. In the first place during the underwriting process: by asking about the claims history of a customer for car and home insurance for example. Another source is the history of the relationship the customer has with the insurance company. While in the past the data was kept in silos by product, the key challenge now lies in gathering all this information into one place where the customer dimension is central. The transversal approach to the database also
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reflects the recent evolution in marketing: going from the 4P’s (product, price, place, promotion) to the 4C’s5 (customer, costs, convenience, communication).
On top of unleashing the value of internal data, new data sources are becoming available like for instance wearables, social networks to name few. Because Big Data can be overwhelming to start with, medium data should be considered at first. In Belgium, the strong bancassurance tradition offers interesting opportunities of combining the insurance and bank data to create powerful predictive models.
3.1.4 Examples of predictive modelling for underwriting 1° Use the 360 view on the customer and predictive models to maximize profitability and gain more business.
By thoroughly analysing data from different sources and applying analytics to gain insight, insurance companies should strive to develop a comprehensive 360-‐degree customer view. The gains of this complete and accurate view of the customer are twofold:
• Maximizing the profitability of the current customer portfolio through: o detecting cross-‐sell and up-‐sell opportunities; o customer satisfaction and loyalty actions, o effective targeting of products and services (e.g. customers that are most likely to be in
good health or those customers that are less likely to have a car accident). • Acquiring more profitable new customers at a reduced marketing cost: modelling the existing
customers will lead to useful information to focus marketing campaigns on the most interesting prospects.
By combining data mining and analytics, insurance companies can better understand which customers are most likely to buy, discover who are their most profitable customers and how to attract or retain more of them. Another use case can be the evaluation of the underwriting process to improve the customer experience during this on-‐boarding process.
2° Predictive underwriting for life insurance6
Using predictive models, in theory it is possible to predict the death probability of a customer. However, the low frequency of life insurance claims presents a challenge to modellers. While for car insurance, the probability of a customer having a claim can be around 10%, for life insurance it is around 0,1% for the first year. Not only does this mean that a significant in force book is needed to have confidence in the results, but also that sufficient history should be present to be able to show mortality experience over time. For this reason, using the underwriting decision as the variable to predict is a more common choice.
All life insurance companies hold historical data on medical underwriting decisions that can be leveraged to build predictive models that predict underwriting decisions. Depending on how the model is used, the outcome can be a reduction of costs for medical examinations, to have more customer friendly processes by avoiding asking numerous invasive personal questions or a reduction in time needed to assess the risks by automatically approving good risks and focusing underwriting efforts on more complex cases. For example, if the predictive model tells you that a new customer has a high degree of similarity to customers that passed the medical examination, the medical examination could be waved for this customer.
If this sounds scary for risk professionals, first a softer approach can be tested, for instance by improving marketing actions by targeting only those individuals that have a high likelihood to be in good health. This not only decreases the cost of the campaign, but also avoids the disappointment of a potential customer who is refused during the medical screening process.
5 http://www.customfitonline.com/news/2012/10/19/4-‐cs-‐versus-‐the-‐4-‐ps-‐of-‐marketing/ 6 Predictive modeling for life insurance, April 2010, Deloitte
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3.1.5 Challenges of predictive modelling in underwriting7 Predictive models can only be as good as the input used to calibrate the model. The first challenge in every predictive modelling project is to collect relevant, high quality data of which a history is present. As many insurers are currently replacing legacy systems to reduce maintenance costs, this can be at the expense of the history. Actuaries are uniquely placed to prevent the history being lost, as for adequate risk management; a portfolio’s history should be kept. The trend of moving all policies from several legacy systems into one modern single policy administration system is an opportunity that must be seized so in the future data collection will be easier.
Once the necessary data are collected, some legal or compliance concerns need to be addressed as there might be boundaries to using certain variables in the underwriting process. In Europe, if the model will influence the price of the insurance, gender is no longer allowed as an explanatory variable. And this is only one example. It is important that the purpose of the model and the possible inputs are discussed with the legal department prior to starting the modelling.
Once the model is built, it is important that the users realize that no model is perfect. This means that residual risks will be present and this should be put in the balance against the gains that the use of the model can bring.
And finally, once a predictive model has been set up, a continuous reviewing cycle must be put in place that collects feedback from the underwriting and sales teams and collects data to improve and refine the model. Building a predictive model is a continuous improvement process, not a one-‐off project.
3.2 Insurance pricing 3.2.1 Overview of existing pricing techniques The first rate-‐making techniques were based on rudimentary methods such as univariate analysis and later iterative standardized univariate methods such as the minimum bias procedure. They look at how changes in one characteristic result in differences in loss frequency or severity.
Later on insurance companies moved to multivariate methods. However, this was associated with a further development of the computing power and data capabilities. These techniques are now being adopted by more and more insurers and are becoming part of everyday business practices. Multivariate analytical techniques focus on individual level data and take into account the effects (interactions) that many different characteristics of a risk have on one another. As it was explained in the previous section, many companies use predictive modelling (a form of multivariate analysis) to create measures of the likelihood that a customer will purchase a particular product. Banks use these tools to create measures (e.g. credit scores) of whether a client will be able to meet lending obligations for a loan or mortgage. Similarly, P&C insurers can use predictive models to predict claim behaviour. Multivariate methods provide valuable diagnostics that aid in understanding the certainty and reasonableness of results.
Generalized Linear Models are essentially a generalized form of linear models. This family encompasses normal error linear regression models and the nonlinear exponential, logistic and Poisson regression models, as well as many other models, such as log-‐linear models for categorical data. Generalized linear models have become the standard for classification rate-‐making in most developed insurance markets—particularly because of the benefit of transparency. Understanding the mathematical underpinnings is an important responsibility of the rate-‐making actuary who intends to use such a method. Linear models are a good place to start as GLMs are essentially a generalized form of such a model. As with many techniques, visualizing the GLM results is an intuitive way to connect the theory with the practical use. GLMs do not stand alone as the only multivariate classification method. Other methods such as CART, factor analysis, and neural networks are often used to augment GLM analysis.
In general the data mining techniques listed above can enhance a rate-‐making exercise by:
• whittling down a long list of potential explanatory variables to a more manageable list for use within a GLM;
• providing guidance in how to categorize discrete variables;
7 Predictive modelling in insurance: key issues to consider throughout the lifecycle of a model
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• reducing the dimension of multi-‐level discrete variables (i.e., condensing 100 levels, many of which have few or no claims, into 20 homogenous levels);
• identifying candidates for interaction variables within GLMs by detecting patterns of interdependency between variables.
3.2.2 Old versus new modelling techniques The adoption of GLMs resulted in many companies seeking external data sources to augment what had already been collected and analysed about their own policies. This includes but is not limited to information about geo-‐demographics, sensor data, social media information, weather, and property characteristics, information about insured individuals or business. This additional data helps actuaries further improve the granularity and accuracy of classification rate-‐making. Unfortunately this new data is very often unstructured and massive, and hence the traditional generalized linear model (GLM) techniques become useless.
With so many unique new variables in play, it can become a very difficult task to identify and take advantage of the most meaningful correlations. In many cases, GLM techniques are simply unable to penetrate deeply into these giant stores. Even in the cases when they can, the time constraints required to uncover the critical correlations tend to be onerous, requiring days, weeks, and even months of analysis. Only with advanced techniques, and specifically machine learning, can companies generate predictive models to take advantage of all the data they are capturing.
Machine learning is the modern science of finding patterns and making predictions from data based on work in multivariate statistics, data mining, pattern recognition, and advanced/predictive analytics. Machine learning methods are particularly effective in situations where deep and predictive insights need to be uncovered from data sets that are large, diverse and fast changing — Big Data. Across these types of data, machine learning easily outperforms traditional methods on accuracy, scale, and speed.
3.2.3 Personalized and Real-‐time pricing – Motor Insurance In order to price risk more accurately, insurance companies are now combining analytical applications – e.g. behavioural models based on customer profile data – with a continuous stream of real time data – e.g. satellite data, weather reports, vehicle sensors – to create detailed and personalized assessment of risk.
Usage-‐based insurance (UBI) has been around for a while – it began with Pay-‐As-‐You-‐Drive programs that gave drivers discounts on their insurance premiums for driving under a set number of miles. These soon developed into Pay-‐How-‐You-‐Drive programs, which track your driving habits and give you discounts for 'safe' driving.
UBI allows a firm to snap a picture of an individual's specific risk profile, based on that individual's actual driving habits. UBI condenses the period of time under inspection to a few months, guaranteeing a much more relevant pool of information. With all this data available, the pricing scheme for UBI deviates greatly from that of traditional auto insurance. Traditional auto insurance relies on actuarial studies of aggregated historical data to produce rating factors that include driving record, credit-‐based insurance score, personal characteristics (age, gender, and marital status), vehicle type, living location, vehicle use, previous claims, liability limits, and deductibles.
Policyholders tend to think of traditional auto insurance as a fixed cost, assessed annually and usually paid for in lump sums on an annual, semi-‐annual, or quarterly basis. However, studies show that there is a strong correlation between claim and loss costs and mileage driven, particularly within existing price rating factors (such as class and territory). For this reason, many UBI programs seek to convert the fixed costs associated with mileage driven into variable costs that can be used in conjunction with other rating factors in the premium calculation. UBI has the advantage of utilizing individual and current driving behaviours, rather than relying on aggregated statistics and driving records that are based on past trends and events, making premium pricing more individualized and precise.
3.2.4 Advantages UBI programs offer many advantages to insurers, consumers and society. Linking insurance premiums more closely to actual individual vehicle or fleet performance allows insurers to price premiums more accurately. This increases affordability for lower-‐risk drivers, many of whom are also lower-‐income drivers. It also gives consumers the ability to control their premium costs by encouraging them to reduce
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miles driven and adopt safer driving habits. The use of telematics helps insurers to more accurately estimate accident damages and reduce fraud by enabling them to analyse the driving data (such as hard breaking, speed, and time) during an accident. This additional data can also be used by insurers to refine or differentiate UBI products.
3.2.5 Shortcomings/challenges 3.2.5.1 Organization and resources Taking advantage of the potential of Big Data requires some different approaches to organization, resources, and technology. As in many new technologies that offer promise, there are challenges to successful implementation and the production of meaningful business results. The number one organizational challenge is determining the business value, with financing as a close second. Talent is the other big issue – identifying the business and technology experts inside the enterprise, recruiting new employees, training and mentoring individuals, and partnering with outside resources is clearly a critical success factor for Big Data. Implementing the new technology and organizing the data are listed as lesser challenges by insurers, although there are still areas that require attention.
3.2.5.2 Technology challenges The biggest technology challenge in the Big Data world is framed in the context of different Big Data “V” characteristics. These include the standard three V’s of volume, velocity, and variety, plus two more – veracity and value. The variety and veracity of the data presents the biggest challenges. As insurers venture beyond analysis of structured transaction data to incorporate external data and unstructured data of all sorts, the ability to combine and input the data into an analytic analysis may be complicated. On one hand, the variety expresses the promise of Big Data, but on the other hand, the technical challenges are significant. The veracity of the data is also deemed as a challenge. It is true that some Big Data analyses do not require the data to be as cleaned and organized as in traditional approaches. However, the data must still reflect the underlying truth/reality of the domain.
3.2.5.3 Technology Approaches Technology should not be the first focus area for evaluating the potential of Big Data in an organization. However, choosing the best technology platform for your organization and business problems does become an important consideration for success. Cloud computing will play a very important role in Big Data. Although there are challenges and new approaches required for Big Data, there is a growing body of experience, expertise, and best practices to assist in successful Big Data implementations.
3.3 Insurance Reserving Loss reserving is a classic actuarial problem encountered extensively in motor, property and casualty as well as in health insurance. It is a consequence of the fact that insurers need to set reserves to cover future liabilities related to the book of contracts. In other words the insurer has to hold funds aside to meet future liabilities attached to incurred claims. In non-‐life insurance, most policies run for a period of 12 months. However the claims payment process can take years or even decades. In particular, losses arising from casualty insurance can take a long time to settle and even when the claims are acknowledged, it may take time to establish the extent of the claims settlement costs. A well-‐known and costly example is provided by the claims from asbestos liabilities. Thus it is not a surprise that the biggest item on the liabilities side of an insurer’s balance sheet is often the provision of reserves for future claims payments. It is the job of the reserving actuary to predict, with maximum accuracy, the total amount necessary to pay those claims that the insurer has legally committed to cover for. Historically, reserving was based on deterministic calculations with pen and paper, combined with expert judgement. Since the 1980s, the arrival of personal computers and ‘spreadsheet’ software packages induced a real change for the reserving actuaries. The use of spreadsheets does not only result in gain of calculation time but allows also testing different scenarios and the sensitivity of the forecasts. The first simple models used by actuaries started to evolve towards more developed ideas through the evolution of the IT resources. Moreover the recent changes in regulatory requirements, such as Solvency II in Europe, have showed the need of stochastic models and more precise statistical techniques.
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3.3.1 Classical methods There are a lot of different frameworks and models used by reserving actuaries to compute the technical provisions, and it is not the goal of this paper to review them in an exhaustive way but rather to show that they share the central notion of triangle. A triangle is a way of presenting data in the form of a triangular structure showing the development of claims over time for each origin period. An origin period can be the year the policy was written or earned, or the loss occurrence period. After having used deterministic models, reserving generally switches to stochastic models. These models allow for quantifying reserve risk. The use of models based on aggregated data used to be convenient in the past when IT resources were limited but is more and more questionable nowadays when we have huge computational power at hand at an affordable price. Therefore there is a need to move to models that fully use data available in the insurers’ data warehouses. 3.3.2 Micro-‐level reserving methods Unlike aggregate models (or macro-‐level models), micro-‐level reserving methods (also called individual claim level models) use individual claims data as inputs and estimate outstanding liabilities for each individual claim. Unlike the models detailed in the previous section, they model very precisely the lifetime development process of each individual claim, including events such as claim occurrence, reporting, payments and settlement. Moreover they can include micro-‐level covariates such as information about the policy, the policyholder, claim, claimant and transactions. When well specified, such models are expected to generate reliable reserve estimates. Indeed the ability to model the claims development at the individual level and to incorporate micro-‐level covariate information allows micro-‐level models to handle heterogeneities in claims data efficiently. Moreover the large amount of data used in modelling can help to avoid issues of over-‐parameterization and lack of robustness. As a consequence, micro-‐level models are especially significant under changing environments, as these changes can be indicated by appropriate covariates. 3.4 Claims Management Big Data can play a tremendous role in the improvement of claims management. It provides access to data that was not available before, and makes the claims processing faster. Therefore it enables improved risk management, reduces loss adjustment expenses and enhances quality of service resulting in increased customer retention. Below we present details of how Big Data analytics improves fraud detection process.
3.4.1 Fraud detection It is estimated that a typical organization loses 5% of its revenues to fraud each year8. The total cost of insurance fraud (non-‐health insurance) in the US is estimated to be more than $40 billion per year9. The advent of Big Data & Analytics has provided new and powerful tools to fight fraud.
3.4.2 What are the current challenges in fraud detection? The first challenge is finding the right data. Analytical models need data and in a fraud detection setting this is not always that evident. Collected fraud data are often very skew, with typically less than 1% fraudsters, which seriously complicates the detection task. Also the asymmetric costs of missing fraud versus harassing non-‐fraudulent customers represent important model difficulties. Furthermore, fraudsters try to constantly outperform the analytical models such that these models should be permanently monitored and re-‐configured on an ongoing basis.
3.4.3 What analytical approaches are being used to tackle fraud? Most of the fraud detection models in use nowadays are expert based models. When data becomes available, one can start doing analytics. A first approach is supervised learning which analyses a labelled data set of historically observed fraud behaviour. It can be used to both predict fraud as well as the amount thereof. Unsupervised learning starts from an unlabelled data set and performs anomaly detection. Finally, Social network learning analyses fraud behaviour in networks of linked entities. Throughout our research, it has been found that this approach is superior to all others! 8 www.acfe.com 9 www.fbi.gov
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3.4.4 What are the key characteristics of successful analytical models for fraud detection? Successful fraud analytical models should satisfy various requirements. First, they should achieve good statistical performance in terms of recall or hit rate, which is the percentage of fraudsters labelled by the analytical model as suspicious, and precision, which is the percentage of fraudsters amongst the ones labelled as suspicious. Next, the analytical models should not be based on complex mathematical formulas (such as neural networks, support vector machines,...) but should provide clear insight into the fraud mechanisms adopted. This is particularly important since the insights gained will be used to develop new fraud prevention strategies. Also the operational efficiency of the fraud analytical model needs to be evaluated. This refers to the amount of resources needed to calculate the fraud score and adequately act upon it. E.g., in a credit card fraud environment, a decision needs to be made within a few seconds after the transaction was initiated.
3.4.5 Use of social network analytics to detect fraud10 Research has proven that network models significantly outperform non-‐network models in terms of accuracy, precision and recall. Network analytics can help improve fraud detection techniques. Fraud is present in many critical human processes such as credit card transactions, insurance claim fraud, opinion fraud, social security fraud... Fraud can be defined by the following five characteristics. Fraud is an uncommon, well-‐considered, imperceptibly concealed, time-‐evolving and often carefully organized crime, which appears in many types and forms. Before applying fraud detection techniques, these five issues should be resolved or counterbalanced. Fraud is an uncommon crime and this means that it is an extremely skewed class distribution. Rebalancing techniques could be used such as the SMOTE to counterbalance this effect. SMOTE consists in under sampling the majority class of data (reduce the number of legitimate cases) and oversampling the minority class of data (duplicate of fraud cases or create artificial fraud cases).
Complex fraud structures are well-‐considered, this implies that there will be changes in behaviour over time so not every time period will have the same importance. A temporal weighting adjustment should put an emphasis on the more important periods (more recent data periods) that could be explanatory of the fraudulent behaviour.
Fraud is imperceptibly concealed meaning that it is difficult to identify fraud. One could leverage on expert knowledge to create features and help identify fraud.
Fraud is time-‐evolving. The period of study should be selected carefully taking into consideration that fraud evolves over time. How much of previous time periods could explain or affect the present? The model should incorporate these changes over time. Another question to rise is in what time-‐window the model should be able to detect fraud: short, medium or long term.
The last characteristic of fraud is that it is most of the time carefully organized. Fraud is often not an individual phenomenon, in fact there are many interactions between fraudsters. Often there are fraud sub-‐networks developing in a bigger network. Social network analysis could be used to detect these networks.
Social Network analysis helps deriving useful patterns and insights by exploiting the relational structure between objects.
A network consists of two set of elements: the objects of the network which are called nodes and the relationships between nodes which are called links. The links connect two or more nodes. A weight could be assigned to the nodes and links to measure the magnitude of the crime or the intensity of the relationship. When constructing such networks, focus will be put on the neighbourhood of a node which is a subgraph of network around the node of interest (fraudster).
Once a network has been constructed, how could this network be used as an indicator of fraudulent activities? Fraud could be detected by answering following question: Does the network contain statistically significant patterns of homophily? Detection of fraud relies on a concept often used in sociology which is called homophily. Homophily in networks consists in people have a strong tendency to 10 based on the research of Véronique Van Vlasselaer (KULeuven)
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associate with other whom they perceive as being similar to themselves in some way. This concept could be translated in fraud networks: fraudulent people are more likely to be connected to other fraudulent people. Clustering techniques could be used to detect significant pattern of homophily and thus could spot fraudsters.
Given a homophilic network with evidence of fraud clusters then it is possible to extract features from the network around the node(s) of interest (fraud activity) which is also called the neighbourhood of the node. This process is called the featurization process: extracting features for each network object based on its neighbourhood. Focus will be put on the first-‐order neighbourhood (first-‐degree links) also known as the “egonet”. (ego: node of interest surrounded by its direct associates known as alters). Featurization extraction happens at two levels: egonet generic features (how many fraudulent resources are associated to that company, is there relationships between resources...) and alter specific features (how similar are the alter to the ego, is the alter involved in many fraud cases or not).
Once these first-‐order neighbourhood features for each subject of interest (companies) have been extracted such as degree of fraudulent resources, the weight of the fraudulent resources, it is then easy to derive the propagation effect of these fraudulent influences through the network.
To conclude, network models always outperform non-‐network models as they are able to better distinguish fraudsters from non-‐fraudsters. They are also more precise in generating high-‐risk companies and smaller list and better detect more fraudulent corporates.
3.4.6 Fraud detection in motor insurance – Usage-‐Based Insurance example In 2014, Coalition Against Insurance Fraud11, with assistance of business analytics company SAS, has published a report in which it stresses that technology plays a growing role in fighting fraud. “Insurers are investing in different technologies to combat fraud, but a common component to all these solutions is data,” said Stuart Rose, Global Insurance Marketing Principal at SAS. “The ability to aggregate and easily visualize data is essential to identify specific fraud patterns.” “Technology is playing a larger and more trusted role with insurers in countering growing fraud threats. Software tools provide the efficiency insurers need to thwart more scams and impose downward pressure on premiums for policyholders,” said Dennis Jay, the Coalition’s executive director.
In motor insurance, a good example is Usage-‐Based Insurance (UBI), where insurers can benefit from the superior fraud detection that telematics can provide. It equips an insurer with driving behaviour and driving exposure patterns including information about speeding, driving dynamics, driving trips, day and night driving patterns, garaging address or mileage. In some sense UBI can become a “lie detector” and can help companies to detect falsification of the garaging address, annual mileage or driving behaviour. Thanks to recording vehicle’s geographical location and detecting sharp braking and harsh acceleration during an accident, an insurer can analyse accident details and estimate accident damages. The telematics devices used in the UBI can contain first notice of loss (FNOL) services, providing very valuable information for insurers. Analytics performed on this data provide additional evidence to consider when investigating a claim, and can help to reduce fraud and claims disputes.
4 Legal aspects of Big Data 4.1 Introduction Data processing lies at the very heart of the insurance activities. Insurers and intermediaries collect and process vast amounts of personal data about their customers. At the same time they are dealing with a particular type of ‘discrimination’ among their insureds. Like all businesses operating in Europe, insurers are subject to European and national data protection laws and anti-‐discrimination rules. The fast technological evolution and globalization has activated a comprehensive reform of the current Data Protection laws. The EU hopes to complete a new General Data Protection Regulation at the end of this year. Insurers are concerned that this new Regulation could introduce unintended consequences for the insurance industry.
11 http://www.insurancefraud.org/about-‐us.htm
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4.2 Data processing 4.2.1 Legislation: an overview Insurers collect and process data to analyse risks that individuals wish to cover, to tailor products accordingly, to valuate and pay claims and benefits, and detect and prevent insurance fraud. The rise of Big Data presents opportunities to offer more creative, competitive pricing and, importantly, predict customers’ behavioural activity. As insurers continue to explore this relatively untapped resource, evolutions in data processing legislation need to be followed very closely. The protection of personal data was -‐ as a separate right granted to an individual -‐ for the first time guaranteed in the Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data (Convention 108). It was adopted by the Council of Europe in 1981.
The current, principal EU legal instrument establishing rules for fair personal data processing is the Data Protection Directive (95/46/EC) of 1995, which regulates the protection of individuals with regard to the processing of personal data and the free movement of such data. As a framework law, the Directive had to be implemented in EU Member States through national laws. This Directive has set a standard for the legal definition of personal data and regulatory responses to the use of personal data. The provisions includes principles related to data quality, criteria for making data processing legitimate and the essential right not to be subject to automated individual decisions.
The Data Protection Directive was complemented by other legal instruments, such as the E-‐Privacy Directive (2002/58/EC), part of a package of 5 new Directives that aim to reform the legal and regulatory framework of electronic communications services in the EU. Personal data and individuals’ fundamental right to privacy needs to be protected but at the same time the legislator must take into account the legitimate interests of governments and businesses. One of the innovative provisions of this Directive was the introduction of a legal framework for the use of devices for storing or retrieving information, such as cookies. Companies must also inform customers of the data processing to which their data will be subject and obtain subscriber consent before using traffic data for marketing or before offering added value services with traffic or location data. The EU Cookie Directive (2009/136/EC), an amendment of the E-‐Privacy Directive, aims to increase consumer protection and requires websites to obtain informed consent from visitors before they store information on a computer or any web connected device.
In 2006 the EU Data Retention Directive (2006/24/EC) was adopted as an anti-‐terrorism measure after the terrorist attacks in Madrid and London. However on 8 April 2014, the European Court of Justice declared this Directive invalid. The Court took the view that the Directive does not meet the principle of proportionality and should have provided more safeguards to protect the fundamental rights with respect to private life and to the protection of personal data.
Belgium has established a Privacy Act or Data Protection Act in 1992. Since the introduction of the EU Data Protection Directive (1995) the principles of that directive has been transposed into Belgian law. The Privacy Act consequently underwent significant changes introduced by the Act of 11 December 1998. Further modifications have been made in the meantime, including those of the Act of 26 February 2006. The Belgian Privacy Commission is part of a European task force, which includes data protection authorities from the Netherlands, Belgium, Germany, France and Spain. In October 2014, a new Privacy Bill was introduced in the Belgian Federal Parliament. The Bill mainly aims at providing the Belgian Data Protection Authority (DPA) with stronger enforcement capabilities and ensuring that Belgian citizens regain control over their personal data. To achieve this, certain new measures are being proposed to be included in the existing legislation, adopted already in 1992, as inspired by the proposed European data protection Regulation.
At this moment the current data processing legislation needs an urgent update. Rapid technological developments, the increasingly globalized nature of data flows and the arrival of cloud computing pose new challenges for data protection authorities. In order to ensure a continuity of high level data protection, the rules need to be brought in line with technological developments. The Directive of 1995 has also not prevented fragmentation in the way data protection is implemented across the Union.
In 2012 the European Commission has proposed a comprehensive, pan-‐European reform of the data protection rules to strengthen online privacy rights and boost Europe's digital economy. On 15 June 2015, the Council reached a ‘general approach’ on a General Data Protection Regulation (GDPR) that
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establishes rules adapted to the digital era. The European Commission is pushing for a complete agreement between Council and European Parliament before the end of this year. The twofold aim of the Regulation is to enhance data protection rights of individuals and to improve business opportunities by facilitating the free flow of personal data in the digital single market. The Regulation must be appropriately balanced in order to guarantee a high level of protection of the individuals and allow companies to preserve innovation and competitiveness. In parallel with the proposal for a GDPR, the Commission adopted a Directive on data processing for law enforcement purposes (5833/12).
4.2.2 Some concerns of the insurance industry The European insurance and reinsurance federation, Insurance Europe, is concerned that the proposed Regulation could introduce unintended consequences for the insurance industry and their policyholders. The new legislation must correctly balance an individual’s right to privacy against the needs of businesses. The way insurers process data must be taken into account appropriately so that they can perform their contractual obligations, assess consumers’ needs and risks, innovate, and also combat fraud. There is also a clear tension between Big Data, the privacy of the insured’s personal data and its availability to business and the State.
An important concern is that the proposed rules concerning profiling do not take into consideration the way that insurance works. The Directive of 1995 contains rules on 'automated processing' but there is not a single mention of 'profiling' in the text. The new GDPR aims to provide more legal certainty and more protection for individuals with respect to data processing in the context of profiling. Insures need to profile potential policyholders to measure risk, any restrictions on profiling could, therefore, translate not only into higher insurance prices and less insurance coverage, but also into an inability to provide consumers with appropriate insurance. Insurance Europe recommends that the new EU Regulation should allow insurance-‐related profiling at pre-‐contractual stage and during the performance of the contract. There is also still some confusion in defining profiling, in the Council approach profiling means solely automated processing while Article 20(5) proposed by the European Parliament, could, according to Insurance Europe, be interpreted as prohibiting fully automated processing, requesting human intervention for every single insurance contract offered to consumers.
The proposal of the EU Council (June 2015) stipulates that the controller should use adequate mathematical or statistical procedures for the profiling. He must secure personal data in a way which takes account of the potential risks involved for the interests and rights of the data subject and which prevents inter alia discriminatory effects against individuals on the basis of race or ethnic origin, political opinions, religion or beliefs, trade union membership, genetic or health status, sexual orientation or that result in measures having such effect. Automated decision-‐making and profiling based on special categories of personal data should only be allowed under specific conditions.
According to the Article 29 Working Party12 the proposals of the Council according to profiling are still unclear and do not foresee sufficient safeguards which should be put in place. In June 2015 it renews its call for provisions giving the data subject a maximum of control and autonomy when processing personal data for profiling. The provisions should clearly define the purposes for which profiles may be created and used, including specific obligations on controllers to inform the data subject, in particular on his or her right to object to the creation and the use of profiles. The academic Research Group IRISS remarks that the GDPR does not clarify whether or not there is an obligation on data controllers to disclose information about the algorithm involved in profiling practices and suggest clarification on this point.
Insurance Europe also request that the GDPR should explicitly recognise insurers’ need to process and share data for fraud prevention and detection. According to the Council and the Article 29 Working Party fraud prevention may fall under the non-‐exhaustive list of ‘legitimate interests’ in Article 6(1) (f) and will provide the necessary legal basis to allow processes for combatting insurance fraud.
The new Regulation proposes also a new right to data portability, enabling easier transmission of personal data from one service provider to another. This would allow policyholders to obtain a copy of any of their data being processed by an insurer and insurers could be forced to disclose confidential and
12 Article 29 Working Party is an independent advisory body on data protection and privacy, set up under Data Protection Direction of 1995. It is composed of representatives from the national data protection authorities of the EU Member States, the European Data Protection Supervisor and the European Commission.
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commercially sensitive information. Insurance Europe believes that the scope of the right to data portability should be narrowed down, to make sure that insurers would not be forced to disclose actuarial information to competitors.
Insurers also need to retain policyholder information. It should clearly state that the right to be forgotten should not apply where there is a contractual relationship between an organisation and an individual or where a data controller is required to comply with regulatory obligations to retain data or where the data is processed to detect and prevent fraudulent activities.
The implementation of more stringent, complex rules will require insurance firms to review their compliance programmes. They will have to take account of increased data handling formalities, profiling, consent and processing requirements and the responsibilities and obligations of controllers and processors.
4.3 Discrimination 4.3.1 Legislation: an overview In 2000 two important EU directives have provided a comprehensive framework for European anti-‐discrimination law. The Employment Equality Directive (2000/78/EC) prohibits discrimination on the basis of sexual orientation, religion or belief, age and disability in the area of employment while the Racial Equality Directive (2000/43/EC) combats discrimination on the grounds of race or ethnicity in the context of employment, the welfare system, social security, and goods and services. The Gender Goods and Services Directive (2004/113/EC) has expanded the scope of sex discrimination and requires that differences in treatment may be accepted only if they are justified by a legitimate aim. Any limitation should nevertheless be appropriate and necessary in accordance with the criteria derived from case law of the ECJ. As regards the insurance sector, the Directive, in principle, imposes ‘unisex’ premiums and benefits for contracts concluded after 21 December 2007. However, it provides for an exception to this principle in Article 5(2), with the possibility to permit differences in treatment between women and men after this date, based on actuarial data and reliable statistics. In its Test-‐Achats judgment, the ECJ invalidated this exception because it was incompatible with Articles 21 and 23 of the EU’s Charter of Fundamental Rights. A proposal for a Council Directive (COM 2008 426-‐(15)) stipulates that actuarial and risk factors related to disability and to age can be used in the provision of insurance. These should not be regarded as constituting discrimination where the factors are shown to be key factors for the assessment of risk. The recent proposal of the Council on the new General Data Protection Regulation (June 2015) states that the processing of special categories of personal (sensitive) data, revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade-‐union membership, and the processing of genetic data or data concerning health or sex life shall be prohibited. Derogations from this general prohibition should be explicitly provided inter alia where the data subject gives explicit consent or in respect of specific needs, in particular where the processing is carried out in the course of legitimate activities by certain associations or foundations the purpose of which is to permit the exercise of fundamental freedoms. In Belgium the EU Directive 2000/78/EC is transposed to the national legislation with the anti-‐discrimination Law of 10 May 2007 (BS 30.V.2007). This law has been amended by the law of 30 December 2009 (BS 31.XII.2009) and by the law of 17 Augustus 2013 (BS 5.III.2014). Due to the federal organization of Belgium, laws prohibiting discrimination are complex and fragmented because they are made and implemented by six different legislative bodies, each within its own sphere of competence. 4.3.2 Tension between insurance and anti-‐discrimination law Insurance companies are dealing with a particular type of ‘discrimination’ among their insureds. They attempt to segregate insureds into separate risk pools based on their differences in risk profiles, first, so that they can charge different premiums to the different groups based on their risk and, second, to incentivize risk reduction by insureds. They openly ‘discriminate’ among individuals based on observable characteristics. Accurate risk classification and incentivizing risk reduction provide the primary justifications for why we let insurers ‘discriminate’. [30]
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Regulatory restrictions on insurers’ risk classifications can produce moral hazard and generate adverse selection. Davey [31] remarks that insurance and anti-‐discrimination law are defending a fundamental different perspective to risk assessment. Insurance has often defended its practices as ‘fair discrimination’. They assert that they are not discriminating in the legal sense by treating similar cases differently, they rather are treating different cases differently. This clash between the principal of insurance and anti-‐discrimination law is fundamental: whether differential treatment based on actuarial experience is ‘discrimination’ in law or justified differential treatment. This tension is felt in both the national and supranational levels as governments and the EU seek to regulate underwriting practices. A good, illustrative example is the already mentioned Test-‐Achats case. Tension between insurance and the Charter of Fundamental Rights is also clearly felt in the debate on genetic discrimination in the context of life insurance. Insurers might wish to use genetic test results for underwriting, just as other medical or family history data. The disclosure of genetic data for insurance risk analysis will present complex issues that overlap those related to sensitive data in general. Canada, the US, Russia, and Japan have chosen not to adopt laws specifically prohibiting access to genetic data for underwriting by life insurers. In these countries, insurers treat genetic data like other types of medical or lifestyle data [32]. Belgium, France, and Norway have chosen to adopt laws to prevent or limit insurers' access to genetic data for life insurance underwriting. The Belgian Parliament has incorporated in the Law of 25 June 1992 legislative dispositions that prohibits the use of genetic testing to predict the future health status of applicants for (life) insurances. Since EU member states have adopted different approaches on the use of genetic data, a pan-‐European regulation is needed. The recent proposal of the Council on a new General Data Protection Regulation (June 2015) does not solve this problem. It prohibits the processing of genetic data but recognises explicit consent as a valid legal basis for the processing of genetic data and leaves to Member States (Article 9(2) (a)) the decision on not admitting consent for legitimising the processing of genetic data. 5 New Frontiers
5.1 Risk pooling vs. personalization With an introduction of Big Data in insurance, insurance sector is opening up to new possibilities, new innovative offers and personalized services for their customers. As a result we might see the end of risk pooling and the rise of individual risk assessment. It is said that these personalized services will provide new premiums that will be “fairer” for the policyholder. Is it indeed true that the imprudence of others will have less impact on your own insurance premium? This way of thinking holds for as long as the policyholder does not have any claim. In the world of totally individualised premium, the event of a claim would increase the premium of that policyholder enormously. And that seems in contradiction with the way we think about insurance i.e. that in the event of a claim, your claim is paid by the excess premium of the other policyholders. It seems that with the introduction of Big Data, the social aspect of insurance is gone.
However, which customer would like to subscribe to such an insurance offer? One could then argue that it is better to save the insurance premium on your own and put it aside for the possibility of a future claim. So in order to talk about insurance, risk pooling will always be necessary. Big Data is just changing the way we pool the risks.
For example, until recently, the premium for car insurance was only dependant on a handful of indicators (personal, demographic and car data). Therefore, an insurance portfolio needed to be big enough to have risk pools with enough diversification on the other indicators that could not be measured.
In recent years more and more indicators can be measured and used as data. This means that risk pools don’t have to be as big as before because the behaviour of each individual of the risk pool is becoming more and more predictable. Somebody who speeds all the time is more likely to have an accident. Previously this was assumed to be people with car with high horsepower. Nowadays, this behaviour can be exactly measured, removing the need for assumptions.
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However, as long as there is a future event that is uncertain, risk pooling still makes sense. The risk pools are just becoming smaller and more predictable. In the example given, even a driver who does not speed can still be involved in an accident.
5.2 Personalised Premium Personalisation of risk pricing relies upon an insurer having the capacity to handle a vast amount of data. A big challenge is linked with data collection, making sure it is reliable and that it can in fact be used for insurance pricing. They will have to be careful not to be overwhelmed by Big Data.
We stated above that the use of Big Data will make insurance pricing fairer. In this case fair is defined as taking into account all members of society. However, this does not mean that everyone in society should be treated in exactly the same way. Every individual should have an equal opportunity to what is on offer. However, it can appear that the offer does not meet the requirements of the customer, or vice versa. It that case, an insurance cover will not be possible.
5.3 From Insurance to Prevention One of the big advantages of the gathering of Big Data by Insurance companies or other companies is that this data can in a certain way be shared with its customers. In that way, a constant interaction can arise between the insurer and the policyholder. When consumers understand better how their behaviour can impact their insurance premium, they can make changes in their live that can beneficial both parties.
A typical example of this is the use of telematics in car insurance. A box in the insured car automatically saves and transmits all driving information of the vehicle. The insurance company uses this data to analyse the risk the policyholder is facing during driving. When for example the driver is constantly speeding and braking heavily, the insurance company can take this as an indication to increase the premium. On the other hand, someone who drives calmly and outside the busy hours and only outside the city will be rewarded with a lower premium.
In this way insurers will have an impact on the driving behaviour of people. Once this communication between policyholder and insurer is transparent, the policyholder will act in a way to decrease his premium. The insurer has played the role of a prevention officer.
Another example is “e-‐Health”. As the health cost is rising rapidly, insurers are trying to lower the claim costs. It is found that everyday living habits of people, for example, eating behaviour, the amount of sleep you get, or the number of hours you do sport has a large influence on health claims.
The Internet of Things will have an impact on the way the pricing is done for each individual. Thanks to modern sensors, insurer will be able to acquire data at the individual/personal level. Each policyholder will in that way be encouraged to sleep enough, sport enough and eat healthy. All in all, it is the consumer that benefits from less car accidents, a healthy lifestyle and … lower premiums.
5.4 The all-‐seeing Insurer Insurance companies have always been interested in gathering as much information possible on the risk being assured and the people insuring them. With the possibilities of Big Data, this interest in people’s everyday life increases enormously. Therefore insurance is becoming more and more an embedded part of the everyday life of people and businesses. Previously, consumers just needed to fill in some form at the beginning of an insurance contract and the impact of that insurance was more or less stable and predictable during the whole duration of the contract, whatever the future behaviour of the consumer. With the introduction of Big Data, insurers have influence on every aspect of everyday life. The way you drive, what you buy, what you don’t buy, the way you sleep, etc., can have a big impact on your financial situation. Indeed, insurers are moving into a position of a central tower, observing our everyday life through the bias of smartphones and all other devices and sensors.
The future will tell us how far the general public will allow this influence of insurance companies. Sharing your driving behaviour with insurers will probably not be a problem for most of us, but sharing what we eat and how we sleep is a bigger step. Every person will have to make a trade-‐off between privacy and better insurance offer. Currently, for instance in case of car insurance telematics, drivers have an opt-‐in option and they can decide whether they are interested in the telematics-‐based offer. However in the future data collection might be default and you will have to pay extra to be unlisted and keep your life private.
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5.5 Change in Insurance business From an actuarial point of view we tend to focus on the opportunities big data hold for managing and pricing risk. But the digital transformation that is at the basis of big data (cfr. the increased data flow: the V’s from section 2.1 and the increased computational power: section 2.2) has also led to a change in customer’s expectations and behaviour. The ease at which the end customer can access information and interact with companies and the way the digital enterprises have developed their services to enhance this ease of use, has set a new standard in customer experience. Customers are used to getting quick and online reactions from the companies they buy goods and services from. Industries that do not adapt to this new standard can quickly get an image of old fashion, traditional and simply not interesting. We already have seen new distribution models changing the insurance market in surrounding countries, i.e. aggregator websites in the UK, that are a result (or play into) this trend. It is in this new customer experience that big data plays an important role and can be a real element of competitive advantage as it gives access to a new level of personalization. Getting this personalization right can give a company the buy-‐in into future customer interactions and therefore the opportunity for expanding the customer wallet or relation. This has led to the evolution where some big digital retailers have continuously expanded their offer to a wide and loyal customer base, even into the insurance business (e.g. Alibaba Insurance). If these players get it right they can change the insurance distribution landscape, monopolizing the customer relation and leaving traditional insurers the role of pure risk carriers. For now this evolution is less noticeable in Belgium where the traditional Insurance distribution model (brokers and banks) still firmly holds its ground giving the Belgium Insurance Industry an opportunity to modernize (read digitalize) and personalize the customer experience before newcomers do so.
6 Actuarial sciences and the role of actuaries Big Data opens a new world for insurance and any other activity based on data. The access to the data, the scope of the data, the frequency of the data, the extension of the samples of the data, are important elements that determine to what extend the final decision is inspired by the statistical evidence. As Big Data changes those properties drastically, it also changes the environment of those who use these data drastically. The activity of the actuary is particularly influenced by the underlying data, and therefore it is appropriate to conclude that the development of the Big Data world has a major impact on the education and training of the actuary, the tools used by the actuary, the role of the actuary in the process. Data science aiming to optimise the analytics in function of the volume and diversity of the data is an upcoming and fast developing field. The combination of the actuarial skills and research allows for an optimal implementation of the insights and tools offered by the data science world.
6.1 What is Big Data bringing for the actuary? 6.1.1 Knowledge gives power Big data gives access to more information than before: this gives the actuary a richer basis for actuarial mathematical analysis. When data are more granular and readily available, actuaries can extend their analysis and identify better the risk factors and the underlying dependencies. Best estimate approaches are upgraded to stochastic evidence. Christophe Geissler13 states that big data will progressively stimulate the actuary to abandon purely explicative models for more complex models aiming to identify sub groups with heterogenic subgroups. The explicative models are based on the assumption that there exists a formula that explains the behaviour of all persons. Big data and the calculation power, allow developing innovative algorithms to detect visible and verifiable indicators for a different risk profile.
6.1.2 Dynamic risk management “Even if an actuary uses data to develop an informed judgement, that type of estimate does not seem sufficient in today’s era of Big Data”, a statement that can be read on a discussion forum of actuaries. Instead, dynamic risk management is considered to be an advanced form of actuarial science. Actuarial science is about collecting all pertinent data, using models and expertise to factor risks, and then making a decision. Dynamic risk management entails real-‐time decision-‐making based on a stream of data.
Scope and resources Big data opens the horizon of the actuary. The applications of Big Data go far beyond the insurance
13 Christophe Geissler, Le nouveau big bang de l’actuariat, L’Argus de l’Assurance, November 2013
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activity and relate to various domains where the statistical analysis and the economic/financial implications are essential. Jobert Koomans14, board member of the Actuarial Genootschap, refers to estimates that Big Data will create a big number of jobs (“1,5 million new data analysts will be required in the US in 2018”). Given that actuaries have very string analytical skills combined with business knowledge thanks to being involved from pricing to financial reporting, gives them a lot of new opportunities across different industries.
6.2 What is the actuary bringing to Big Data? 6.2.1 The Subject Matter Expert Data are a tool to quantify the implications of events and behaviour. The initial modelling and analysis nevertheless are defining the framework and the ultimate outcome. Deductive and inductive approaches can be used in this context. Kevin Pledge15 refers in the to the role of the Subject Matter Expert. “Understanding the business is a critical factor for analytics, understanding does not come from a system, but from training and experience. … Not only do actuaries have the quantitative skills to be data scientists of insurance, but our involvement in everything from pricing to financial reporting gives us the business knowledge to make sense of this. This business knowledge is as important as the statistical and quant skills typically thought of when you think data scientist”. Actuaries are well placed to combine the data analytics and the business knowledge. The specific education of the actuary as well as the real life experience in the insurance industry and other domains with actuarial roots are essential for a successful implementation of the big date approach. 6.2.2 Streamlining the process The actuary formulates the objectives and framework for the quantitative research and by this initiates the Big Data process. Big data requires the appropriate technology and the use of advanced data science. Actuaries can help to optimise this computer science driven analysis with their in depth understanding of the full cycle. Streamlining the full process from detecting the needs and defining the models, over using the appropriate data, to the monitoring of the outcome taking into account general interest and specific stakeholder interest, is the key of success of data science in hands of the actuary.
6.2.3 Simple models with predictive power Esko Kivisaari16: “The real challenge of Big Data for actuaries is to create valid models with good predictive power with the use of a lots of data. The value of a good model is not that it is just adapted to the data at hand but it should have predictive power outside experience. There will be the temptation to create complicated models with lots of parameters that closely replicate what is in the data. The real challenge is to have the insight to still produce simple models that have real predictive power.”
The added value of the actuary can be found in the modelling skills and the ability to use the professional judgement. The organisation of the profession and the interaction with peers creates the framework allowing to exercise this judgement. Actuaries’ focus also goes to an appropriate communication of the results so that the contribution to the value creation can be optimized. 6.2.4 Information to the individual customer Big Data can help to find answers on the needs of consumers and society. Customers will be informed on their behaviour so that they will be able to correct, influence and change the risk behaviour. Actuaries will be in the perfect position to bring the data back to the customer, be it through the pricing of insurance products or through helping in establishing awareness campaigns. 14 Jobert Koomans, Big Data – Kennis maakt macht, De Actuaris (Actuarieel Genootschap), May 2014 15 Kevin Pledge, Newsletters of the Society of Actuaries, October 2012 16 Esko Kivisaari, Big Data and actuarial mathematics, working paper Insurance Committee of the Actuarial Association of Europe, March 2015
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7 Conclusions The rise of technology megatrends like ubiquitous mobile phones and social media, customer personalization, cloud computing and Big Data have enormous impact on our daily lives but also on business operations. There are plenty very successful businesses, across different industries that regard Big Data as very important and central to their strategy. In this information paper we wanted to understand what would be the impact of Big Data on insurance industry and the actuarial profession. We asked ourselves whether insurers are immune to these recent changes? Will they be able to leverage on huge volumes of new available data coming from various sources (mobile phones, social media, telematics sensors, wearables) and power of Big Data? We think that Big Data will have various effects. It will demand from companies to adopt new business culture and become data-‐driven businesses. It will have an impact on the entire insurance value chain, ranging from underwriting to claims management. Today’s advanced analytics in insurance go much further than traditional underwriting and actuarial science. Machine learning and predictive modelling is the way forward for insurers for improving pricing, segmentation and increasing profitability. For instance direct measurement of driving behaviour provides new rating factors and transforms auto insurance underwriting and pricing processes. Big Data can also play a tremendous role in the improvement of claims management by for instance providing very efficient fraud detection models. We would note that there are few inhibitors that could block these changes with legislation being one of the main concerns. The EU is currently working on General Data Protection Regulation (GDPR) that updates the data processing, protection privacy and establishes legislation adapted to the digital era. It is still unclear what will be the final agreement but the Regulation must be appropriately balanced in order to guarantee a high level of protection of the individuals and allow companies to preserve innovation and competitiveness. Finally we discussed new frontiers of insurance. Big Data gives us huge amount of information and allows creating “fairer”, more personalized insurance premium being at odds with solidarity aspect of insurance. However we think that Big Data will not revolutionize it and risk pooling will remain core, it will just become better. Big Data opens a lot of new possibilities for actuaries. Data science and actuarial science do mutually reinforce each other. More data allow for a richer basis for actuarial mathematical analysis, big data leads to a dynamic risk management approach; the application of Big Data goes far beyond the insurance activity and therefore offers a lot of new opportunities. The implementation of Big Data in insurance and the financial services industry requires the input of the actuary as the subject matter expert who also understands the complex methodology. For Big Data to be successful, understandable models with predictive power are required for which the professional judgement of the actuary is essential. We hope that the paper will be a good starting point for the discussion about the interplay between Big Data and insurance and the actuarial profession. The Institute for Actuaries in Belgium will further develop the subject and prepare the Belgian actuaries.
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8 References 8.1 Section 3.1 [1] Predictive modeling for life insurance, Mike Batty, 2010, Deloitte (https://www.soa.org/files/pdf/research-‐pred-‐mod-‐life-‐batty.pdf ) [2] Predictive modeling in insurance: key issues to consider throughout the lifecycle of a model, Chris Homewood, 2012, Swiss Re, (http://www.swissre.com/library/archive/?searchByType=1010965&searchByType=1010965&sort=descending&sort=descending&search=yes&search=yes&searchByLanguage=851547&searchByLanguage=851547&m=m&m=m&searchByCategory=1023505&searchByCategory=1023505&searchByYear=872532&searchByYear=872532#inline ) [3] Data analytics in life insurance: lessons from predictive underwriting, Willam Trump, 2014, Swiss Re(http://cgd.swissre.com/risk_dialogue_magazine/Healthcare_revolution/Data_Analytics_in_life_insurance.html ) [4] Advanced analytics and the art of underwriting, transforming the insurance industry, 2007, Deloitte (https://www.risknet.de/fileadmin/.../Deloitte-‐Underwriting-‐2007.pdf) [5] Data Management: Foundation for a 360-‐degree Customer View-‐ White Paper, 2012?, Pitney Bowes Software (http://www.retailsolutionsonline.com/doc/data-‐management-‐foundation-‐for-‐a-‐degree-‐customer-‐view-‐0001) [6] Unleashing the value of advanced analytics in insurance, Richard Clarke and Ari Libarikian, 2014, McKinsey (http://www.mckinsey.com/insights/financial_services/unleashing_the_value_of_advanced_analytics_in_insurance) 8.2 Section 3.2 [7] Ptolemus USAGE-‐BASED INSURANCE Global Study 2013 [8] Milliman: Usage-‐based insurance: Big data, machine learning, and putting telematics to work -‐ Marcus Looft, Scott C. Kurban [9] Capitalizing on Big Data Analytics for the Insurance Industry [10] Driving profitability and lowering costs in the Insurance Industry using Machine Learning on Hadoop -‐August 6, 2015 / Amit Rawlani / Big Data Ecosystem, Business, Machine Learning / Leave a Reply [11] HSBC -‐ Big data opens new horizons for insurers 8.3 Section 3.3: [12] Antonio, K., & Plat, R. (2014). Micro-‐level stochastic loss reserving in general insurance. Scandinavian Actuarial Journal , 649-‐669. [13] Arjas, E. (1989). The Claims Reserving Problem in Non-‐Life Insurance: Some Structural Ideas. Astin Bulletin 19 (2), 140-‐152. [14] England, P., & Verrall, R. (2002). Stochastic Claims Reserving in General Insurance. British Actuarial Journal (8), 443-‐544. [15] Gremillet, M., Miehe, P., & Trufin, J. (n.d.). Implementing the Individual Claims Reserving Method, A New Approach in Non-‐Life Reserving. Working Paper . [16] Haastrup, S., & Arjas, E. (1996). Claims Reserving in Continuous Time -‐ A Nonparametric Bayesian Approach. ASTIN Bulletin (26), 139-‐164. [17] Jewell, W. (1989). Predicting IBNYR Events and Delays, Part I Continuous Time. ASTIN Bulletin (19), 25-‐56. [18] Jin, X., & Frees, E. W. (n.d.). Comparing Micro-‐ and Macro-‐Level Loss Reserving Models. Working Paper . [19] Larsen, C. R. (2007). An Individual Claims Reserving Model. ASTIN Bulletin (37), 113-‐132. [20] Mack, T. (1993). Distribution-‐free calculation of the standard error of Chain Ladder reserve estimates. ASTIN Bulletin (23), 213-‐225. [21] Mack, T. (1999). The standard error of Chain Ladder reserve estimate: recursive calculation and inclusion of a tail factor. ASTIN Bulletin (29), 361-‐366. [22] Norberg, R. (1999). Prediction of Outstanding Liabilities II: Model Variations and Extensions. ASTIN Bulletin (29), 5-‐25. [23] Norberg, R. (1993). Prediction of Outstanding Liabilities in Non-‐Life Insurance. ASTIN Bulletin (23), 95-‐115.
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[24] Pigeon, M., Antonio, K., & Denuit, M. (2014). Individual loss reserving using paid-‐incurred data. Insurance: Mathematics & Economics (58), 121-‐131. [25] Pigeon, M., Antonio, K., & Denuit, M. (2013). Individual Loss Reserving with the Multivariate Skew Normal Model. ASTIN Bulletin (43), 399-‐428. [26] Wüthrich, M., & Merz, M. (2008). Modelling the claims development result for Solvency purposes. ASTIN colloquium . [27] Wüthrich, M., & Merz, M. (2008). Stochastic Claims Reserving Methods in Insurance. New York: Wiley. [28] Zhao, X., & Zhou, X. (2010). Applying Copula Models to Individual Claim Loss Reserving Methods. Insurance: Mathematics and Economics (46), 290-‐299.[29] Zhao, X., Zhou, X., & Wang, J. (2009). Semiparametric Model for Prediction of Individual Claim Loss Reserving. Insurance: Mathematics and Economics (45), 1-‐8.
8.4 Section 4 [30] Avraham, R., Logue, K. D., and Schwarcz, D. B., Understanding Insurance Anti-‐Discrimination Laws, Law & Economics Working Papers 52, University Michigan, 2013. [31] Davey, James, Genetic discrimination in insurance: lessons from test achats in De Paor, A., Quinn, G. and Blanck, P. (eds.), Genetic Discrimination -‐ Transatlantic Perspectives on the Case for a European Level Legal Response, Abingdon, 2014. [32] Yann Joly et al., Life insurance: genomic stratification and risk classification in European Journal of Human Genetics, 2014 May; 22(5), 575–579, p. 575