a key enabler for accelerating digital transformation · into transforming to an open financial...

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Artificial Intelligence A key enabler for accelerating digital transformation

REPORT

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Vincent Bastid CEO, Efma

Monique Dahler Director Worldwide Financial Services Industry and Head of AI, Microsoft

Executive summaryBanks have seen some major changes in the past due to developments in technology. However, these could pale into insignificance when compared with the changes that are currently happening as a result of the exciting opportunities and challenges presented by the arrival of artificial intelligence (AI).

Although this has been available in some forms for some time, it’s only now that its true potential is being realised in terms of being a catalyst for digital transformation of the financial landscape.

To gain a clearer perspective of what is actually happening in the financial services sector – and also to see what still needs to happen – Microsoft and Efma joined forces to carry out a survey of senior financial executives. Although this was a relatively small study, it still provides some fascinating insights into some of the developments that have already taken place; the many challenges that banks still face in relation to implementing AI; and the tremendous possibilities that lie ahead for the future.

In addition to the survey results, this report provides a glimpse into what is happening in different banks and in different countries. The contents have been guided by two discussions sessions involving a Steering Committee composed of industry leaders who are directly involved in transforming to a digital business and developing the use of AI within their banks.

We hope that you enjoy reading the report and that it stimulates your own interest in this fascinating subject – as AI will be a key element in determining the future path of banking across the world.


The financial landscape is changing drastically with fintechs entering the market place putting enormous pressure on traditional financial institutions to transform their business models faster. Cost efficiency, Customer Experience, and Security & Trust are still top of mind challenges and in particular the larger banks are looking into transforming to an Open Financial Services Platform by taking advantage of the Open API economy, the Cloud, Data and AI. Changing operating models and shifting competitive dynamics as a result of the rapid adoption on AI is enabling banks to accelerate their transformation to a digital business.

Globally, spending on artificial intelligence and cognitive systems has exploded recently, forecasted to reach US$46 billion by 2020 – with a quarter of this coming from the financial services sector (source IDC). This dramatic increase is being driven, amongst other things, by heavily regulated markets that are looking for new innovations that will transform their capabilities and profitability. This includes the development of products that are a closer match for client needs,improvements to processes and new solutions for risk and fraud detection.

Another survey, shows that 81% of Chief Information Officers in the financial services sector said that advanced analytics is the key area that provides the most potential for driving change within their organization (source Gartner). Developments in AI have already started to transform the financial services landscape by improving decision-making; providing better information about customer behaviour; and predicting customer needs. This is all part of the evolution of banking due to digitisation.

Introduction

Financial services digital transformation

Intelligent BankIntelligent automation

API economy

Natural interaction BOTs

Predictive decisions

Personal finance manager Robo-advisory

Immersive Bank

CRM

Social

Cloud

eCommerceAnalytics

New branches and ATMs

Realtime marketingMobility

Traditional BankPayments

Deposits

Credits Loans

Predicting what’s next for the customer: using systems of intelligence to process (big) data – increased wallet share, new services and business models

Providing consistent customer experiences and launching new

campaigns quickly – higher brand loyalty, increased sales

Bank provides multiple channels for trusted interactions with clients

Deliver the best customer experience

Serve the ‘Always-On’ Customer

Become the ‘Go-to’ Platform

Strategy based on Data-AI-Cloud

Source: Microsoft

PART ONE

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The survey

Microsoft’s survey on the potential use of artificial intelligence in the financial services sector was distributed to a wide array of Efma members. The questionnaire was aimed particularly at senior financial executives who have an interest in this topic or who are responsible for related issues, such as digital transformation.

A total of 58 participants took part in the survey which provided a good indication of the overall opinions of financial institutions in relation to AI as well as some interesting insights into the key challenges and opportunities of using this technology.

For simplicity, the survey results are expressed as percentages of the number of responses given to any specific question. The participant banks ranged in size and came from a good geographical spread:

The survey questions were grouped under the following main topics:

• The impact of digital disruption and the role of AI in transformation• The role of data analysis and insights in transformation• How AI is being used by financial institutions and the specific elements employed• The degree of collaboration with fintechs• How AI is being measured (if at all)• The potential impact of AI on the future of financial institutions• The potential impact of AI on individual banks

The Steering Committee

Microsoft and Efma facilitated two deep dive sessions in order to gain some qualitative insights for the report from a Steering Committee of high-level senior financial services professionals. They were asked to look at the survey results and also to share their own experiences of AI and any best practices they had heard about. These sessions provided some valuable insights which have been incorporated within this report.

Geographical spread of survey respondents

Central and Eastern Europe

28% 26%21%

15%

5% 5%

WesternEurope

AsiaPacific

Middle Eastand Africa

Survey 58 participantsSteering Committee 8 members60% EMEA, 20% APAC, 5% LATAM

Parti

cipa

nts

Latin America

Globalbanks/others

It is this glimpse into the dramatic changes that AI could bring about that prompted Microsoft and Efma to carry out a survey of banks, their attitude towards AI, and the impact it is likely to have upon their business.


The impact of digital disruption

There is little doubt that recent advances in technology – such as AI – are changing the financial landscape rapidly. There is an increasing demand for innovation and yet many banks are hampered by the challenge of having a lot of legacy systems. At the same time, banks need to attract and retain employees who have the knowledge and expertise – or at least the ability and capacity to learn – to take advantage of the new technologies.

The survey participants were therefore asked about what digital disruption meant for their business. As might be expected, the responses were varied. However, the main areas mentioned and the opinions expressed by participants included:

The impact of technology

• New technologies (such as AI and machine learning) are emerging every day and are affecting every aspect of society, including the financial services sector.

• Some banks felt that they need to embrace this by using these technologies to transform their services and to enhance the customer experience.

• Others said that to remain competitive, banks must be innovative in terms of both their products and their processes. This would mean adapting new technologies that would help their customers and would result in the transformation of their services.

• Several respondents said that banks will need to innovate quickly in response to the changes all around. This would include a significant investment in digital transformation.

Survey results

PART TWO

The impact of digital disruption and the role of AI in transformation

The impact of digital disruption on business

How do you leverage AI as part of a digital transformation strategy?

• The impact of technology• The impact on customers• The need to adapt• The need to change the business model• The need for greater customer centricity• The need for talent

• Data analytics• AI and machine learning• Increasing competitiveness• AI at the heart of strategy• AI tools • Operational improvements• Customer service

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The impact on customers

• Some survey participants felt that digital disruption is helping to create more efficient processes that will hopefully meet evolving customer expectations.

• Banks felt that they need to nurture their relationships with their customers, who expect instant access to everything.

• As a result, banking needs to be more firmly embedded in the customer journey and the customer’s daily life.

• The need for greater customer centricity is something that banks have been talking about for a long time – it now needs to be put into practice more effectively, so that all customers feel more nurtured and have a better experience of banking.

The need to adapt

• Digital disruption should perhaps be a forerunner of digital transformation within banks. • This process needs to accelerate if banks are going to keep moving forward successfully. It will include

some IT restructuring and a greater use of agile methodologies. • However, digital transformation also opens up new business opportunities. • One participant summed it up by saying that banks need to become more agile or they will fail. Another

commented that without transformation, some banks won’t be in business in five to ten years’ time.

The need to change the business model

• Tied in with this transformation is the need to change the business model for making money. • Financial institutions need to find new ways of creating greater operational efficiencies.• This means rethinking their organizational structure and the way in which they work. Many jobs will

disappear, and new ones will be created. One big challenge is the need to address the issue of legacy architecture.

• One or two banks commented that some financial institutions – and their IT departments – aren’t really ready for the changes that lie ahead.

The results of the survey show that digital disruption has an impact on customers, on employees and on operating models. Technology is an enabler that will help to drive digital transformation.

Although most financial institutions by now have been using RPA to quickly streamline and automate many repeatable tasks, they are now wanting to immediately add the intelligence (cognitive services and ML) to create sustainable competitive advantage by providing the best product and customer service at any point of time.

Aspects of digital transformation

a) The role of AI Financial institutions are at very different stages in terms of their ability to leverage AI, with a few banks being far ahead of the rest. They know what they want to accomplish in terms of key performance indicators (KPIs) and also know how to set up the technology so that it becomes part of the culture and strategy of the bank – perhaps in the form of a centre of excellence or an AI lab.


In contrast, some banks are only just starting on their AI journey. They aren’t yet fully familiar with AI tools and resources such as machine learning. Some are trying to move from a pilot stage to a more rigorous strategy which will enable them to take advantage of the opportunities that AI can bring.

The survey participants were therefore asked how they are leveraging AI as part of their digital transformation strategy in order to improve their performance and their competitive edge. This again led to a wide array of answers:

• Data analytics. Several participants highlighted the importance of AI in improving predictive data analysis. One bank suggested that if AI is combined with location-based intelligence, it can drive accurate predictions that will help a bank to understand its clients’ real needs. This in turn will lead to the development of better, more tailored solutions.

• AI and machine learning (ML). When combined with machine learning, AI can help to streamline banking processes and reshape business models. Machine learning is also useful for activities such as credit decision-making for corporate loans.

• Increasing competitiveness. AI is seen by some as a key to building a greater competitive edge by helping banks to provide the right financial solutions and advice, with products that are scalable, cost-effective and best in class.

• AI at the heart of strategy. Larger banks commented that they have already developed an AI Lab or AI ‘Centre of Excellence’ in their banks as part of their digital transformation programme.

• AI tools. Tools such as natural language processing (NLP) and chatbots are already starting to be used effectively by some banks. One said that AI has helped it to serve ten million customers with natural language processing that provides valuable information. Another said that it has a digital bot assistant that provides insights and guidance to customers. One or two banks have started to use robotic process automation and other cognitive services such as facial recognition.

• Operational improvements. Banks have started to use a combination of RPA and AI to improve their operational processes and creating a more efficient workplace by using data analytics.

• Customer service. AI is helping to improve customer service in various ways, including new customer insights; treating each customer as a segment; incorporating AI into advisory services; and providing AI assistance for both employees and customers. Artificial intelligence can help a bank to understand and predict a customer’s specific transactional behaviour, which in turn can lead to more personalised products.

b) The role of data analysis and insights Virtually all financial institutions seem to agree that the real challenge in relation to data isn’t so much the collection of the data but its quality and the ability to collate it in a form that enables it to be used and analysed properly. There’s also a need to have data that’s available in real time.

This all leads to another challenge – linking the data to suitable use cases and moving from data to knowledge. How can banks translate the data they collect into knowledge that can then be used to enhance their processes, their sales and the service they provide to their customers?

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The participants were asked if they are able to collect and analyse vast amounts of structured and unstructured data, as information is one of the key drivers of transformation. The vast majority (70%) said that they could; 15% said that they couldn’t; and the remaining 15% gave other answers. Most of these said that they were working mainly on structured data at the moment, rather than unstructured. Others said that they currently had a limited capability in this area.

Moving from data to knowledge

The role of data analysis and insights in transformation

Data Estate

Ingest Store Process View Project Action

Unstructured Real-timestreaming

Devices

Events

Weather

Twitter

Connectedcustomer

Sales

Operations

Structured Historical

Sales

Marketing

Profile

Data factory

Data warehouse

Data lake

Data catalog

Event hub

Web services

Apps/Devices

Bot Framework

Knowledge

Cognitive services

Stream analytics

Machine learning

Dashboards

Power BI

Data lake analytic

Are you able to collect and analyse vast amount of structured

and unstructured data?

Are you able to use these insights to inform business decisions and

take action in real time?

Yes No

Other

70%

15%50%

25%

25%

15%

Source: Microsoft


This led to a question of whether banks are able to use the insights they gain from their data to inform their business decisions and to enable them to take action in real time. Half of the participants said that they could; 25% said that they couldn’t. Most of the remaining 25% said that that they couldn’t yet, or couldn’t do so in real time but are working on it

Members of the Steering Committee were very surprised that 70% of the banks questioned said that they could collect and analyse vast amounts of data. However, to take maximum advantage of intelligence, the quality of the data that is analysed must be high. One member pointed out that the challenge doesn’t come so much from collecting the data but from collating it together in a form in which it can be used.

A possible solution to this is to consolidate the data within a large data lake so that it can then be used for analysis and machine learning purposes. The data lake can be used to gather data from a bank’s current systems and also historical data from many years ago. This data can all be taken and ‘thrown’ into the lake. If analysed properly, it can provide some very useful insights in various areas.

In terms of collecting data, digital-only banks can have an advantage over traditional banks as it is often easier for them to capture data in every possible way. In legacy banks, it can be harder to capture data such as voice conversations with client advisors or the contact centre. An important challenge is the ability to use the data in real time.

Defining, refining and combining the dataAnother challenge lies in defining what data should be used. Banks might be able to collect data fairly easily but they then often have difficulty in combining it so that it can be used effectively. They have data in many different areas, but not all of this information is going to be useful for being processed within an AI project. Harmonising data that has different values or comes from different databases so that it can be analysed properly requires a lot of effort, involving perhaps data platforms and libraries.

Another important factor is the need to differentiate clearly between structured and unstructured data. Structured data is more easy to manage. However, when introducing AI into the equation to add value to the customer journey, the challenge lies in identifying suitable use cases.

There are other potential sources of unstructured data that could also be useful if it was possible to capture them – such as sentiment. Ultimately, although most banks don’t lack the ideas or the infrastructure to capture and analyse data, they do lack the necessary resources, such as data scientists and data engineers. It’s possible to hire data scientists but one key drawback is that they often won’t understand the bank’s culture and how the bank’s data works. Unlike legacy banks, they often come from companies that don’t have a long history.

Data collection can still be an issue for some banks. For instance, some have to collect data from several different countries. The main problem can be obtaining a comprehensive set of data so that the AI can distinguish between relevant data and ‘noise’. There needs to be sufficient data for proper machine learning. One solution is to try to gain more insights through customer surveys and interviews so that a bank can train the machine more effectively.

As an example, a bank that is based in several central and eastern European countries has been looking at the issue of collecting and analysing data for its private banking segment n relation to AI. This tends to involve creating a use case and then trying to collect and analyse the data related to that. This approach helped the bank to understand that it didn’t have enough data – some data that was needed for deep learning and the effective use of AI was missing. To make it work, the bank needed data that it hadn’t expected to be important for AI.

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As a result, it decide that a better approach would be to firstly look at the data it could collect from the different countries in which it has private banking; and then to create use cases in order to be sure that it could succeed. The different countries have different cultures, different legacies and different systems, so it’s quite difficult to collect and collate the data. In particular, the bank needed a consistent methodology for combining the data to be sure that the data history was the same from every country.

However, it is easier for the bank to collect external data such as macroeconomics, which are imported for the use cases. The bank tried to create use cases where at least 20 to 30% of the data needed comes from external sources where it knows that the quality history is quite long.

Another example comes from a bank whose key objectives currently include the creation of a significant collection of data in a big enterprise data hub or data lake that will enable the bank to have a wealth of information that is readily available. It has therefore embarked upon a huge effort to bring in data from all of its different areas so that it can eventually become available both for machine learning and highly analytical processing and also for transactional processing.

This is one of the main strategic initiatives in which the bank will be investing over the next three years. As an increasing number of feeds are brought into these centralised stores, more and more of the data will become available.

c) The role of regulationPerhaps surprisingly, regulation can be a welcome help in the collection and analysis of data. In most countries, it’s now mandatory to collect and keep data, so banks can now do something with it, and they can also know that the quality of the data is becoming better.

A bank observed that, in connection with data collection and analysis, AI is perhaps one of the few elements within the banking sector where banks are happy to be implementing the MiFID II regulations. This is because of all the data records that banks now need, including information from all of the meetings, which have to be recorded. This effectively creates some real data for use cases, which can be very helpful. So, it’s interesting to know that regulations can sometimes support banks in terms of their future understanding of customer behaviour and in building use cases that can be integrated into AI.

Another bank is using some systems to collect all of its MiFID II data, including meeting minutes. In preparation for all of this, it immediately set up the meeting minutes in a way that enabled it to gain as much structured information as possible from them, so that the bank can then leverage the data. Other preparations involved how the bankers are inputting the data; what the clients are doing with the technology tools; and exploring ways of tracking the behaviour of the clients in order to collect as much data as possible for use by AI in the future.

The new General Data Protection Regulation (GDPR) also has an influence on the collection and analysis of data. Financial institutions – like many other organizations – have to be very careful about respecting consumer rights and about not disclosing their personal data. They have therefore had to look closely at how this can be achieved, and how GDPR compliance can be integrated int projects, including those relating to AI and the use of large volumes of data.

One bank is tackling this by having a specific step within its AI projects where it identifies the risk areas in terms of confidentiality. It has built a team that is dedicated to GDPR and this team makes sure that this is now part of the overall process. The bank has to ensure that the data it handles remains anonymous. It has also trained some of its AI technology by using mock data.


It seems that GDPR could also have some positive benefits in terms of data collection and analysis, as it means that in some instances, the quality of the data that is collated is higher. So, although the data sample might be smaller, the data itself is more reliable.

Not all countries are subject to GDPR. One bank, for instance, has reported that its country has very strict and advanced regulations relating to customer data, which affects its collection and analysis. Although it isn’t covered by regulations such as GDPR, its own legislation has been in place for a long time. Banks are obliged to collect and keep data, so they enjoy the advantages of having a lot of transactional and other kinds of customer data.

The role of fintechs

Many financial institutions are now seeking to develop their vision in relation to having an open financial services platform. This can involve banking as a service, the use of API, the open economy and also collaboration with fintechs, many of which are exploring the possibilities and opportunities afforded by AI. Most of the banks that participated in the survey already work with fintechs. In fact with many fintechs. So what is the role of fintechs within a bank?

From the responses from survey participants and the Steering Committee members, it seems that the level of co-operation with fintechs can vary greatly. One member said that although his bank considers fintechs, it doesn’t co-operate with them at the moment as it has been working closely with a vendor who developed robo advisors for the bank. The vendor has ideas about how to develop AI and has a lot of people focusing on this topic.

Another member said that his bank is primarily working on its own although it does rely on external parties for a couple of aspects. This contrasts with another financial institution that is seeing many benefits from understanding more about what fintechs are doing. The bank is engaging strongly with fintechs to learn as much as it can from the ideas they are generating – and is also engaging with them at the proof of concept level. It is trying to understand how the fintechs’ solutions would help the bank to accelerate some of its own thinking.

However, it believes that the real challenge comes at the point of deciding to take this further: to create something that will be fully operational. One key issue is the lack of maturity of the fintechs in terms of some of their thinking, processes and policies and in the completeness of their solutions. It can be hard to proceed further without both sides having to spend a lot of time and effort on creating something that will meets the requirements of the bank.

One bank is looking for the best partner that could help it to deal with or address various problems or ideally several partners who can try to tackle the same problem and then it can select the best solution. What is an enabler for that is making sure that the data being used is really secure for the bank. But at the same time, it doesn’t want to limit the insights or the depth of the information that it can get out of a partnership.

The bank can clearly see a role where the fintechs could position themselves to represent a real added value for the organization. This might include the provision of sub-components of AI and machine learning, such as algorithms that are employed in financial services use cases. Value could also come from working on aspects such as conversational interfaces. Basically, fintechs have the opportunity to provide enrichment in places where there is a gap in the bank’s capabilities.

There are a range of other approaches when it comes to working with fintechs in different ways. For instance, one financial institution is participating in external forums but also has internal initiatives with both regional and global fintechs. At the same time, the bank is also looking at partnering with companies that are developing AI, with a specific focus on financial services.

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Similarly, a different member reported that a bank is using established vendors with whom it is building some items in-house – or it is sometimes using its own internal capabilities to do this. At the same time, it is busy engaging with fintechs, although the biggest issue is finding one with which the bank feels it can work in the long term. This involves issues of trust and sustainability, but the bank is at the proof of concept stages with many fintechs across AI and many other areas. However, it is having a much higher rate of success by working with larger established companies, as these have a better understanding of its needs.

A different approach involves working much more closely in the innovation space with a number of fintechs. One of the key challenges facing banks that want to progress is the ability to develop a fintech partnership and collaboration so that it moves beyond the initial proof of concept stage. One of the Steering Committee banks has recently published a report together with some other large banks about fintech collaboration and a fintech collaboration toolkit that is now publicly available. This aims to help the fintechs and to develop the debate about what needs to be done so that both parties can progressively start to exploit much more of their abilities.

Yet another bank is developing its AI strategy internally, and is using hired data scientists who are highly qualified. At the same time, in other areas it is working in close partnerships with some fintechs in which it has invested. It also has a start-up engagement case with the idea of reducing the time to market and hopefully through this kind of initiative, developing a better and smoother collaboration with its legal and with compliance sections.

Finally, another bank is collaborating with fintechs internally. Fintechs develop solutions based on big data via applying new algorithms allowing advanced analysis for risk management and marketing. Although the banks favour fintechs, they remain consistently cautious and compliant with regards to data privacy rules and regulation: that remains of utmost importance for all financial market players.

When it comes to choosing fintechs and start-ups, often those are the companies who can offer advanced algorithms and models that can be integrated by a bank in-house – installed within the bank’s perimeter. In Eastern Europe, the wealthier and larger (especially state-owned) banks tend to employ teams of data analysts or adopt ready-made solutions from fintechs and integrate those into the bank’s IT infrastructure. This is one of the popular approaches, especially because banks have accumulated plenty of data.

Some countries integrate innovation and fintech co-operation in financial markets into government policy on the state level. For example, there have been numerous state-led initiatives in Russia in 2017-2018. Fintech initiative and innovation in banking are being actively supported by the Central Bank of the country, which is a unique situation allowing a wide range of opportunity for both the banks and fintechs.

There is a Fintech Association sponsored by the Central Bank of Russia, its key initiatives launched in co-operation with key market players. Biometrics for remote customer identification, Regulatory sandbox to support legislation change to meet new technology requirements, Masterchain (a block chain technology that will be used in many areas – digital mortgage guarantees and letters of credit as first pilots) and other initiatives and projects have all been launched in the country in 2017-2018.

There has also been a significant development in fintechs in different areas in Russia. A number of state and private accelerators and associations provide the platforms and mechanisms to support new innovative ideas and allow easier access to investors to develop technologies.

Fintechs will continue to play an important role in AI as they are highly agile and cloud-enabled bringing faster innovation by using computer algorithms that improve financial products and services. Most banks will expand this collaboration while others prefer to develop the solutions themselves or in partnership with larger, more experienced companies.


The use and impact of artificial intelligence

a) How AI is being used by financial institutionsIt’s clear that AI is being used in different ways in different financial institutions. Banks have developed a wide range of use cases for exploring the potential of AI and for seeing the type of impact it could have upon their business.

So, what are the key AI elements that banks are using to provide a relevant and personalised customer experience? Survey participants were given a list of the most common ones. Their responses indicate the percentage of participants using each element:

The Steering Committee didn’t find these results surprising – particularly the importance of predictive analysis and data mining. These underpin aspects such as how banks want to interact and personalise their engagements with their customers to enable them to understand the next steps in the journey of spending or savings in relation to the customer lifecycle. This will also help them to develop a long-term relationship with the client in areas such as wealth management, retail banking and insurance.

Other elements mentioned by individual banks include deep learning; underwriting; transactional classification and insights; and a reasoning tree. Of those using cognitive services, nearly all (93%) were using this approach for mapping complex information and data for tasks such as intelligent recommendations. Most (85%) were using it to process natural language or (77%) to convert speech into text, use voice for verification, or to add speaker recognition to an app. Nearly 70% were using it for image processing.

How artificial intelligence is being used by financial institutions

0%

20%

40%

60%

80%

100%

Chatb

ot

Cogn

itive s

ervic

es NLP RPA

Robo

adv

isory

Mac

hine l

earn

ing

Pred

ictive

ana

lysis

Data

mining

What are the key AI elements that banks are using to provide a relevant and personalised customer experience?

How artificial intelligence is being measured?

• Customer satisfaction• Customer retention• Customer aquisition• Employee satisfaction• Sales• Improvements in efficiency• Improvements in productivity• Risk• Fraud measures

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Virtually all of the participants believe that AI is being used to accelerate the development of a more customer-centric experience and to achieve digital transformation, although one person added that it seems to be used mainly to enhance efficiency.

Use casesAs mentioned previously, banks have created a range of different use cases relating to AI projects. Some progressive banks have been developing use cases for some years. For instance, one organization has been investing in machine learning and natural language processing projects since 2013. Its initial projects involved the use of AI in the optimisation of back-office operations and the elimination of manual data entry. This included, for example, extracting data from images (e.g. customer instructions) and automatically processing the data entry functions. It has recently been applying deep learning techniques in various projects, and is focusing on areas such as customer relationships, risk and fraud detection, financial advice and portfolio management.

Another example of a use case comes from a financial institution that firstly identifies the best client for a specific product. Its main area is a focus on investments within private banking and wealth management. To understand the key products for a particular client at a particular moment of time, it tries to understand the behaviour of its clients and then clusters them.

In another use case, the bank looks more closely at the relationship manager who is serving the client, the financial market situation, and the client’s investment portfolio and risk profile. All of these areas that involve the psychology of the client are covered in the MiFID questionnaire. This helps the bank to understand the key clusters of clients; the best product to approach them with at a particular moment, based on their history; and the potential relationship manager profile for these different clusters.

The bank has noticed very different performance levels amongst its relationship managers. It has trained its AI technology using all of the relationship managers and their clients, along with the historical performance figures, budget, revenues, how much they manage, and the portfolio evolution using those external data. The bank then uses AI to try to understand what would happen to a client portfolio if the relationship manager was replaced by the highest-performing relationship manager, in terms of the performance, the revenues, and what the bank can do with the client.

Based on these results, the bank also tries to highlight the best relationship managers for concrete clients. It has already had some results but is still playing with the data and with the AI to try and gain an even greater understanding – and to also see if the AI can be used for budgeting purposes.

One particular aspect that the bank explores is the investment penetration of the client portfolio and how this could change. The bank operates in central and eastern Europe, where the investment penetration is much lower than in western Europe. In the west, clients in private banking or wealth management are typically investing on average 70% of their portfolio. The average investment penetration by central and eastern European banks is about 30%. The bank’s aim is to try and increase this so that it approaches the level in western European banks – and to see how much movement would be possible if a client was served by the best relationship manager. It then set up a benchmark based on this.

A different use case that involved co-operation with a fintech had a specific focus on fraud detection. A bank is working with a supplier whose solution is looking particularly at the behaviour of customers and interactions with them. This includes details such as the behaviour of how people click on items on a website, and also how they move their finger on a touchscreen and how long they hold it in a particular spot. This gives an interesting insight into fraud detection and has proved to be a very successful method for the bank.


Another financial institution has had a slightly different approach to fraud detection, by focusing mainly on voice. The bank tried to analyse anomalies and fraud detection based on the customer’s voice and way of speaking and their psychological portrait. This approach also turned out to be quite successful – as well as being very interesting.

Yet another approach has been used by a bank that has been developing AI algorithms internally, using its own technology company. The bank believes that the main role for fintechs will come from developing applications based on open-source platforms and libraries. It believes that in the future, it will have AI-based applications in its core banking activities, in customer relations, in operations and in risk areas. However, for fraud, the bank is currently creating an in-house application that uses existing platforms and libraries.

Combining technologiesDifferent AI technologies can be also be combined effectively in several ways. At the moment, it appears that many market leaders are combining robotic process automation with AI to help them to change their operating models more quickly and to improve the customer experience. Each participant was asked if their bank has a similar strategy in place. Although a few banks are still moving slowly towards this, most of the participants had already started to combine RPA and AI, often called intelligent automation. This seems to be more powerful than using just one of these factors. The two together can automate mundane and repetitive tasks to improve operational efficiency, improve the customer experience and drive business growth.

One example comes from a bank that has found that a combination of a number of new tools actually provide it with the most value, rather than looking at some of those new tools and capabilities individually. For instance, the combination of AI and machine learning with RPA, together with the ability to analyse and understand unstructured text, is enabling the bank to create solutions that are much more holistic in addressing a business use case.

The bank’s ability to automatically process inbound emails or even inbound letters that it receives from customers for specific topics is a good example of this. It can also scan documents, using optical character recognition (OCR) to automatically convert the documents into text that can then be analysed. This enables the bank to understand the meaning of what is coming in; to automatically query systems using APIs; and to classify and produce either responses or resolutions to some of those queries.

Similarly, the bank is looking at areas such as commercial lending and the ability to interpret significant documents. It can also start to use a lot of data that goes into its credit risk models that comes from statements or reports from the various companies to which it is trying to lend. It can automate this not only from the aspect of understanding the characters in the text but also by identifying and classifying information using AI.

Overall, the bank is using a combination of AI, RPA and cognitive tools to provide a range of useful information. It has other elements that are currently being developed internally, such as direct virtual assistants and bots, and the ability to resolve customer queries. It is also exploring ways of understanding patterns of call volumes, and queries that customers have in its contact centres as historical trends. In addition, it’s looking at a combination of events that might be happening across the industry and that could enable the bank to predict future events and to help it to plan its resources more effectively in long, medium and short-term contact centres. This will enable it to be more proactive rather than reactive.

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A bank in another area is focusing on specific business cases where it has developed virtual assistants or digital workers. It is finding out how it can manage different areas by combining these items with OCR. For instance, it has a mortgage use case where all the controls were carried out by the back office. It is now looking to see if, by scanning and using OCR, it is satisfied with the level of accuracy produced and if there are positive responses. The bank has tried many proofs of concept in different areas and if any are successful, it hopes to then move on to industrialisation. Rather than focusing on the technology, it is focusing on the business case. From there, it can see what it can use, and from the proof of concept it can move on to industrialisation.

One bank made the point that RPA and AI should be seen as a temporary solution until a completely digital user experience has been established. However, another commented that RPA isn’t completely appropriate for the banking business. Others disagreed and said that RPA and AI can be game changers in the banking world. In other areas, one bank is using robotics to automate simple processes and to reduce manual work. At the moment, this is being achieved without the help of AI.

The next question in the survey related to heavily regulated markets that are seeking to use AI innovatively to address risk and fraud and to ensure that their products and services meet customer expectations more effectively. Many of the banks surveyed said that they were already using AI in all of these areas. Other are still exploring its uses or haven’t yet started.

The Steering Committee discussed the potential use cases where the different technologies could be employed to provide a relevant and personalised customer experience. For instance, robo advisors can be helpful in wealth management. In terms of AI and predictive analysis, customer behaviour can be analysed for aspects such as channel management, next product to buy, and pricing, by looking at aspects such as conversational interfaces.

A member observed that in the market, there is a growing interest in the use of natural language processing for issues such as fraud prevention. It can be used in the preliminary stages when someone is contacting the call centre. Some banks see voice analysis as an important topic for their future development. One member has been using natural language learning to analyse emails and voice messages to find sources of discontent and hopes to evolve this approach to include elements such as sentiment analysis in the future.

A member gave two examples of how his bank has been using new technologies. One is the use of RPA for chargeback processes. As Visa and Mastercard have their own separate systems, migrating data from his bank’s core system to the Visa and Mastercard systems with RPA has been really successful. The other example involves machine learning in the financial analysis of commercial and corporate companies. This can be carried out without even starting a credit allocation file. This is helping the bank both in teaching relationship managers how to conduct financial analysis and also in monitoring companies in terms of credit.

b) How AI is measuredBanks were asked which Key Performance Indicators (KPIs) they use to measure the impact of AI. Some haven’t yet decided on these, but of those that have, the key KPIs they prefer include customer satisfaction/retention/acquisition/employee satisfaction; sales (including upselling and cross-selling); improvements in efficiency or productivity; and risk or fraud measures.

However, in relation to KPIs, not all banks are really developing a business case perspective. They need to realise that if they use AI, there needs to be a clear business case and from a marketing/sales/talent/risk perspective, they also need to determine what the expected business outcome needs to be.


c) The potential impact of AI on financial institutionsThe final two survey questions focused on how banks are likely to be changed by the use of AI and the significant areas that will be affected in the future. The responses included:

Relationships with customers• Many of the participants believe that the over-riding change brought about by AI will be to improve the

customer experience by increasing efficiency. • A key aspect of this move towards greater customer centricity is that AI will enable banks to develop

products and services that are more relevant and more personalised.• Along with machine learning, AI will provide banks with proactive alerts and recommendations for meeting

customer needs.• The automation of tasks – and the use of chat bots and robo advisors – will also enhance the overall

customer experience and will enable banks to give a personalised service to more customers.• Ultimately, AI could help banks to not only reach and attract customers but to prevent customer churn.

Better services• Financial institutions believe that AI will herald significant improvements in business efficiency, data analytics

and productivity. • It will also improve the quality and consistency of banking services. • Many manual and repetitive jobs could be automated and some people replaced by chat bots and robots in

the future. Although the trend is to augment an employee for example a bank advisor with an intelligent bot.

Structural and organizational changes• Some participants believe that there will be more radical changes, including differences in the basic structure

of banking. The whole industry will become leaner and will be driven more by knowledge and data.• This includes a move away from more expensive channels, such as branches and contact centres. • The back office will change significantly, with fewer people required – but those who remain will need

higher technical skills to operate and manage the robots.• There will also be massive changes in the ways in which products and services are delivered. • Employees will have to work in a different way and different skill sets will be needed.• AI is expected to cut costs dramatically in the future.

Finally, participants were asked which areas are likely to be affected by AI in the future – including employees, products and services, client retention, the business model, operational processes and reporting. Perhaps surprisingly, nearly 30% of the banks felt that ALL of these areas would be affected. A further 35% believed that all but one of these would be significantly affected.

The Steering Committee explored this question in greater detail. Across the survey, banks said that AI will enable them to serve their customers better and to reduce their costs dramatically. AI will change the whole industry and will affect how banks and their employees work. However, AI can also facilitate integration between different organizations and can help banks to provide their customers with a more personalised service.

All of the new technologies have a huge potential in terms of cost cutting and process streamlining and also improving the customer journey. The advantages of using artificial intelligence are vast – it could benefit virtually all areas of the financial services industry. Banks do see AI as a catalyst in accelerating the digital transformation.

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Conclusions

PART THREE

AI maturity roadmap is critical to success

FoundationalQuestioning what AI is and how to apply it• Wrong expectations or disappointment• Low digitisation• Basic analytical capabilities

ApproachingHopeful on AI and its promise

• Digitisation underway• Looking to increase or optimise processes• Cautious about disruption

AspirationalExperimented and applied AI• High digitisation• Desires new business models• Achieved a data culture

MatureEmerging data science and operational capability• Understands model lifecycle and management• Building a foundational data architecture

Source: Microsoft

The financial landscape is transforming drastically with fintechs and startups entering the market place. They are highly agile and cloud-enabled and putting enormous pressure on traditional financial institutions to transform their business models and move faster to a digital business. In particular global banks are looking into how to become an Intelligent Bank, an open FSI platform, levering the open API economy. Changing operating models, cultural shift, secure digital workplace, talent, and elevating customer experiences by using cognitive services, ML and advanced analytics are top of mind whereas trust still remains the biggest challenge. Significant growth in spending in AI and in particular heavily regulated markets are seeking the fastest innovation in fraud and risk detection as well as better matching products and services to the client’s needs. In particular advanced analytics has the most potential to change an organization. The quality of data and the ability to collate it in a form that enables it to be used and analyzed properly is key. Linking data to suitable use cases and thus translating data collected into knowledge that can then be used to enhance processes, sales and services provided to customers.


AI maturity governance is critical to success

Strategy

• Bold, long-term orientation

• Linked to business strategy

• Centered around customer needs

Culture

• Risk appetite

• Speed/agility

• Test and learn

• Internal collaboration

• External orientation

Organization

• Roles and responsibilities

• Talent and leadership

• Governance/KPIs

• Digital investment

Capabilities

• Connectivity

• Content

• Customer experience

• Data-driven decision-making

• Automation

• IT architecture

Source: Microsoft

To take advantage of AI as a key enabler in digital transformation it is important to set up a maturity journey that enables to move faster from siloed piloting to placing AI at the center of the business. Most of the banks are somewhat in the middle where AI is connected to digital transformation and some are moving to where they need to be in creating new business models and putting a data driven culture in place. In order to drive the AI maturity roadmap to a success a company needs to have a bold strategy and appetite for risk, invest in talent and develop the right capabilities for a data-driven culture, to make the right decisions for their customers.

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The digital feedback loop

Transformproducts

Optimiseoperations

Empoweremployees

Engagecustomers

Analytics and

intelligence

Product telemetry

Operational data

Employee feedback

Customer signal

More effective employees Better products

More efficient opsDeeper relationships

Source: Microsoft

Banks will need to ensure that they can use analytics and intelligence effectively in the future.This will help them to: build deeper relationships in terms of engaging with their customers; optimise their operations; empower their employees to be more effective; and transform their products so that they truly meet their customers’ needs. This all becomes part of a ‘digital feedback loop’ that will eventually help banks to build a more successful and prosperous future.

Although the joint Microsoft/Efma survey involved a relatively small number of banks, it clearly suggests that exciting new technological developments such as artificial intelligence aren’t likely to be just a passing phase. Indeed, the vast majority of the banks that took part in the survey believe that AI is going to have a dramatic and positive impact on the future of banking – although this will also mean that many new challenges lie ahead before these changes are achieved. Ultimately, for AI to be trustworthy, it must be ‘human-centered’ – designed in a way that augments human ingenuity and capabilities – and that its development and deployment must be guided by ethical principles that are deeply rooted in timeless values. Building a foundation for the development and deployment of AI powered solutions that will put humans at the center and provides a better future for all.

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A global non-profit organization, established in 1971 by banks and insurance companies, Efma facilitates networking between decision-makers. It provides quality insights to help banks and insurance companies make the right decisions to foster innovation and drive their transformation.

Over 3,300 brands in 130 countries are Efma members. Headquarters in Paris. Offices in London, Brussels, Andorra, Stockholm, Bratislava, Dubai, Milan, Montreal, Istanbul, Beijing and Singapore.

Learn more www.efma.com.

Microsoft (Nasdaq “MSFT” @microsoft) enables digital transformation for the era of an intelligent cloud and an intelligent edge. Its mission is to empower every person and every organization on the planet to achieve more. © 2019 Microsoft Corporation. All rights reserved. This document is provided “as-is.” Information and views expressed in this document, including URL and other Internet Web site references, may change without notice. You bear the risk of using it. Some examples are for illustration only and are fictitious. No real association is intended or inferred. This document does not provide you with any legal rights to any intellectual property in any Microsoft product. You may copy and use this document for your internal, reference purposes.

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Artificial Intelligence A key enabler for accelerating digital transformation

April 2019

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