analytics strategies for insurance · 2015-07-23 · collectively, they drive profitable growth....

WHITEPAPER July 2015

www.hcltech.com

ANALYTICS STRATEGIES FOR INSuRANCE

ANALYTICS STRATEGIES FOR INSURANCE | JULY 2015

© 2015, HCl TECHNOlOGIES. REPRODuCTION PROHIBITED. THIS DOCuMENT IS PROTECTED uNDER COPyRIGHT By THE AuTHOR, All RIGHTS RESERVED. 2

ABOUT THE AUTHOR

He has twenty five years of experience in the insurance industry across a range of operational and program management roles. Extensive consulting experience globally on insurance transformation projects including large scale, multinational platform transformations, business process outsourcing, driving operational improvements through the application of focused Business Intelligence and improving risk management through the use of big data.

Peter MelvilleInsurance Domain Lead Europe, HCL



TABLE OF CONTENTS

INTRODUCTION 4

EXECUTIVE SUMMARY 5

UNDERSTANDING THE BUSINESS NEED 6

DEFINING THE MI LIFECYCLE 7

BUILDING A SOLUTION 8

CASE STUDIES 9

CONCLUSION 11



INTRODUCTIONThe obvious key to all Analytics strategies is data. Having said this, perfect data is not required to either commence work or derive significant value from what exists at any given point in time. It is a simple fact that all insurers generate copious volumes of reporting. It may be poorly structured and little used but it reflects the fact that a high volume of existing information is held in structured fields and that someone at some point has developed an extract to develop a report.

The following whitepaper looks at how to optimise existing Analytics capabilities in the short term to makes the best use of internal data as well as making effective use of external data sources to enhance benefits delivery in the future. In effect, transforming an existing reporting function can run on two separate channels with short term analytics using existing data to improve the business while a strategic EDW is developed and deployed.

While the paper is focused primarily the development of effective business intelligence and reporting it should not be taken that more advanced analytics and the development of predictive models are excluded. In practice creating a solid foundation of existing data that can be verified through the creation of accurate operational and financial reports should be considered the first phase in the development of more advanced models during a second phase of development. In simple terms predictive models and the introduction of big data is only as successful as the accuracy of the core business data. Trying to be too clever too quickly usually leads to expensive mistakes.

This document does not seek to cover every aspect of data management or provide a comprehensive study of existing tools available in the marketplace. Rather it has been developed to challenge existing thinking and sets out an alternative approach to Analytics that is focused on business results. Ensuring data compliance, effective governance and creating a sustainable roadmap for future data management will form the basis of a second white paper.



EXECUTIVE SUMMARY

IT led data management programmes are expensive, time consuming and very difficult to deploy successfully in the Insurance environment where competing demands for legacy modernisation and constant change within business units have the current data set in a state of ongoing change.

Even when a data warehouse containing every available piece of data is successfully constructed, it only achieves the gathering together of existing data. It does not mean this data will be any better utilised than it currently is. The goal of the perfect data universe appears to have got in the way of the actual goal which is providing the business information to manage defined requirements, most of which can be managed with existing data. If the data does not exist now, it will not exist in the new data warehouse either!

A strategy that can provide the business with the information it needs to improve and then monitor that improvement to ensure it is sustained can be delivered without the need for the perfect data base being delivered first.

A solution utilising existing data extracts deployed into an effective analytics tool with support from experienced analysts, business SME’s and a clearly defined goal can deliver rapid business improvement. In addition it can leave behind a well-developed monitoring and alert platform in any business area where such a “solution” team has been deployed.

The solution takes into account the typical lifecycle of business need which can be defined as:

y I have a problem; can you help me find out what it is? (MI led business review)

y I understand my problem; can you tell me why it has occurred? (Analytics)

y I understand what needs to be done; can you help me track improvement? (Measurement and monitoring)

y My problem is fixed; can you tell me if it starts to reoccur in the future (Dashboards and alerts).

Every deployment should produce an effective result determined during the initial analysis phase. Once a unit has reached the stage of monitoring and alerts having been developed the data set can be automated and the “solution” team can move to the next area of business.

Quick to implement at significantly lower cost and capable of driving rapid business improvement. MI with a purpose can create a much more powerful business strategy.



UNDERSTANDING THE BUSINESS NEED

There are a number of points to remember when defining management information and they should drive the approach to ensure that information gathered starts improving the business as quickly as possible. It cannot be just interesting. It needs to have a point and that point should speak directly to the business need which in most areas of an insurance company should come down to:

y Reducing Expense costs.

y Reducing the average cost of claim.

y Reducing claims frequency

y Increasing profitable sales

y Improving customer retention (for those customers you want to retain)

Collectively, they drive profitable growth.

Clearly, not all elements are relevant to every area of the business however, where they are relevant they must underpin the need for management information. If your MI cannot be tied back to one of the above areas in a clear line of progression then it is information for information’s sake and adds no value to the business. This is not to say that the information is the wrong thing to gather, rather it is the fact that it is not being used effectively and this becomes something to be fixed.

An MI strategy has to be based on current reality. Many companies strive at considerable cost to develop an all-encompassing data warehouse at considerable cost, only to find that as fast that they integrate applications, the landscape changes and they have to change the model. Insurance companies are particularly vulnerable to this problem as Sales, Service, Underwriting and Claims often develop and deliver new applications in isolation from each other.

Building an MI strategy then has to be focused on something other than developing the perfect data warehouse. In the most basic terms, if you already have the data then why is bringing it all together going to mean you do something with it that could have done previously? It might cost less money when it is consolidated but the cost of consolidation is significant and the cost of maintaining this degree of consolidation is also high.

The real question therefore should be, what tools can I deploy to make the best use of the information I have? Looking at MI from this perspective puts the conversation back to a business view in which the business should be saying I think I have a problem, can you help me define it, get to the bottom of what is causing it, monitor performance while I fix it and then provide a dashboard that allows me to keep an eye on it in the future. This approach demands four essential elements:



y The ability to pull existing data. y The availability of a good analytics tool to drill in to the data y The use of a good analyst who can manipulate the data and essentially, y Someone from the business who can interpret what they see.

Brining the four elements together in a flexible fashion creates a business service (rather than an MI tool) that can develop and deliver MI to the business on demand before moving on to the next request.

DEFINING THE MI LIFECYCLE

Accepting that MI is essentially a toolset designed to fit a circumstance then the circumstances drive the tools to be used.

y Analysis: This is usually tactical and it is based on the knowledge that something could be better (productivity low, claims costs too high, sales to low, expenses to high, retention rates too low etc. )

y Measurement: Once a problem has been defined it requires a system of measurement to track improvement (Benchmarks and a dashboard that tracks progress.)

y Monitoring: A system of alerts to indicate significant changes or a decline in performance below a benchmark.

All three are relevant at different times. Assuming everything needs to be looked at to develop a systematic approach to operational and financial performance reporting then an MI lifecycle develops as follows:

Data is Extracted

Solutions Applied

Solutions Proposed

Data is Analysed

Targets Agreed

Did They Make a

Difference? No

Reporting Cycle in Place

Report

Tracking MI

Developed

Yes

Measure

Problem?

Problem Resolved Monitoring

Implemented



Reducing the lifecycle depends on the successful transition from ad hoc data collection (At the commencement of the problem solving cycle) to systemised data collection (an ongoing regular feed into the reporting data base). It is at this point that a more structured data warehouse becomes useful as each new data set is slotted in to the wider data universe. However it should be stressed that at this point no effort is being made to drive all data into a consistent format. This is only done when two data sets need to be consolidated to resolve a “problem”. This reduces the time taken and it is only undertaken when there is an actual benefit to be derived from doing it. Over time data that requires consolidation will be consolidated.

BUILDING A SOLUTION

Clearly, the approach above breaks with existing conventions. However, it has the benefit of a rapid deployment capability and the flexibility to adapt the reporting of each function as it passes through the analysis, measurement, monitoring lifecycle. It drives the technical conversation away from Master Data Management towards how can IT best support the business with analytics and reporting tools. The result should provide a significant improvement in business transparency at considerably less cost and in a much shorter timescale.

The solution needs to take into account that it will be used in many different business areas over time and the also that the available data will come in many different formats from different business units. As a result it requires more than a simple recommendation around an analytics tool as the service needs to encompass data extraction (From existing reports) data manipulation (in the chosen analytics tool) data analysis (using business SME’s who can interpret the data based on their current business knowledge) and some level of consulting experience to ensure that the recommendations are defined, delivered and acted upon.

The solution needs to be defined in terms of how it will consume information and what will be done with when it has it. The following points summaries the key components:

Data Extracts and Modelling: Data from any source needs to be consumable and put into a logical format for analysis. This is likely to be its existing reporting format in the first instance as it will be familiar to the business users.

Data Analytics: It must be capable of slicing and dicing data and it needs the facility to mine data and deploy “interesting” data relationships for further analysis.

Additional Data Sets: capabilities much exist to add additional data sets when undertaking problem analytics and they may come from both internal and external sources (existing claims data and a customer’s fleet delivery schedules for instance when looking into risk frequency).



Presentation of Data (Reporting): different styles will be required for each of the phases of MI development. The data needs to be presented in a format that will be both understand and utilised.

y Analysis

y Measurement

y Monitoring

y Alerts

Deployment across multiple media’s: deploying information may require a number of different media’s to ensure it is effective. A text alert to an operational manager to advise them that call centre performance is below minimum standards. A web solution that can provide MI to customers who need to undertake their own analysis of performance. Different levels of monitoring and reporting from data dumps to dashboards depending on the audience.

In every case, the core requirement is flexibility at the data consumption stage and the facility to manage each data set through the deployment lifecycle in a format that makes sense to the end user.

CASE STUDIES

The two case studies below serve to illustrate the potential for business improvement utilising existing data sets with targeted analytics and SME’s who can interpret what they see and find root cause issues for improvement. Each case study takes the development of a set of MI through a typical lifecycle from initial data extraction to a final state when dashboards and alerts are in place. Each business unit under study will in effect go through three separate iterations of an MI suite which can be summarised as:

Initial Analysis: Taking the existing data and shaping it into a series of reports which lay out a view of current performance with indicators around what key drivers exist, which ones are performing well or badly and how they will be monitored going forwards.

Transition: A repeating report to a stakeholder group documenting progress against plan with a series of pre-defined reporting elements and a dashboard monitoring each of the key business drivers for the business unit under review.

Business As Usual: The final stage where reporting has been minimised, the dashboard has been finalised and alerts have been developed to trigger should performance dip below agreed baselines.



PROVIDING CUSTOMER FOCUSED MI

A customer with TPO cover on a very large fleet, running a significant excess per claim (£450k) was experiencing high levels of fraud and a deteriorating reserve position. They managed most of the claims process in-house but it became clear that control had been lost and they requested the support of their insurer to help them determine why. The deterioration in performance was putting the company under significant pressure to radically increase the degree of funding to the captive.

The initial phase of analysis took data from existing claims from both the customer and insurers platforms and consolidated the information. The resulting analysis identified a series of process control failures as well as some early indications as to both cause and effect of the growing fraud problem. A series of reports were developed to monitor proposed changes and further work was undertaken on the fraud issue.

During the transition phase, processes changes were reported against plan monthly and a dashboard of indicators emerged and proved effective in driving the improvement process forwards. Further data collaboration with the customer utilised additional data sets to profile the fraudsters and led to a series of risk mitigation recommendations which the customer adopted. The net effect was an 80% reduction in new suspect claims and a dramatic decline in reserves held against suspect claims as dormancy rules took effect.

The final phase embedded the dashboard, the clients Actuaries agreed to reduce the captive funding demands and credited eight points off the prior year’s loss development as the reduction in fraud reserves became apparent.

The client renewed with the insurer for three years.

ADDING DEPTH TO CLAIMS DEVELOPMENT CURVES

An actuarial function raised a series of red flags with regards to the performance of a book of business and a determination needed to be made with regards to continuing to write the business or not. A series of ongoing business improvements were not mature enough to show in the development curves and the requirement for new MI centred on a way to convince that Actuarial function that benefits could be both tracked and related accurately to the development of the current and prior year.

The initial analysis developed a series of tracking metrics which Actuarial accepted as key drivers to development. Past and current performance was benchmarked and monitoring MI developed to provide a monthly view on how business improvement was impacting development.

The final MI state captured a series of reconciliation reports between the actuarial triangles and business views on claims performance providing Actuarial with an accurate guide to current trends. The net result was a 10 point improvement in the loss ratio.



CONCLUSION

A business led focus on the creation of a data management strategy is essential to ensure that work on transforming existing BI and Analytical capabilities can be managed effectively. While IT must execute the strategy and ensure that it is compliant and secure and capable of meeting future requirements, the business must provide direction, scope and measures to prove the benefits of the work undertaken. Understanding the BI lifecycle and using it to create early business benefit delivery will also drive the roadmap for migrating data into a sustainable data solution. Making effective use of data will become an ever increasing necessity for insurers to survive in a rapidly changing world and adopting the right approach from the start can create a springboard for success.

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ABOUT HCL

About HCL Technologies

HCL Technologies is a leading global IT services company working with clients in the areas that impact and redefine the core of their businesses. Since its emergence on the global landscape, and after its IPO in 1999, HCL has focused on ‘transformational outsourcing’, underlined by innovation and value creation, offering an integrated portfolio of services including software-led IT solutions, remote infrastructure management, engineering and R&D services and business services. HCL leverages its extensive global offshore infrastructure and network of offices in 31 countries to provide holistic, multi-service delivery in key industry verticals including Financial Services, Manufacturing, Consumer Services, Public Services and Healthcare & Life sciences. HCL takes pride in its philosophy of ‘Employees First, Customers Second’ which empowers its 104,184 transformers to create real value for customers. HCL Technologies, along with its subsidiaries, had consolidated revenues of US$ 5.8 billion, for the Financial Year ended as on 31st March 2015 (on LTM basis). For more information, please visit www.hcltech.com

About HCL Enterprise

HCL is a $6.8 billion leading global technology and IT enterprise comprising two companies listed in India – HCL Technologies and HCL Infosystems. Founded in 1976, HCL is one of India’s original IT garage start-ups. A pioneer of modern computing, HCL is a global transformational enterprise today. Its range of offerings includes product engineering, custom & package applications, BPO, IT infrastructure services, IT hardware, systems integration, and distribution of information and communications technology (ICT) products across a wide range of focused industry verticals. The HCL team consists of over 109,643 professionals of diverse nationalities, who operate from 31 countries including over 505 points of presence in India. HCL has partnerships with several leading global 1000 firms, including leading IT and technology firms. For more information, please visit www.hcl.com

analytics strategies for insurance · 2015-07-23 · collectively, they drive profitable growth....

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