In collaboration with:

Machine learning in European financial institutions

REPORT

2 Machine learning in European financial institutions

05 | The survey05 | Methodology

25 | Conclusion

03 | Introduction

06 | Survey results06 | AI deployment08 | Importance of AI solutions12 | Data maturity within financial institutions14 | AI challenges15 | Build versus buy decision for AI and machine learning17 | Investment in AI

18 | How AI, machine learning and marketing intersect20 | Stage I: Reach20 | Stage II: Act20 | Stage III: Convert21 | Stage IV: Engage21 | The future of AI in marketing

22 | AI and machine learning case studies22 | Case study: Santander ‘Openbank’23 | Case study: mBank ‘mAccountant’23 | Case study: CaixaBank ‘Real-Time Analysis’24 | Case study: Bank Austria ‘Big Data Transform 2019’

SURVEY

FEEDBACK

MARKETING

CASE STUDIES

CONTENTS

3

IntroductionThe entire financial services industry is being inundated with articles and presentations about the business implications of artificial intelligence (AI) and machine learning. Financial institutions are becoming aware of the potential of these technologies and are beginning to explore how advanced analytics could enable them to streamline operations, improve product offerings and enhance customer experiences.

According to Efma, “AI presents a huge number of opportunities for retail financial services firms, who, when able to exploit their growing data repositories, can better meet regulations, increase their bottom line, improve the customer experience and more.”

Financial services organizations realize they have a head start with the application of advanced analytics, since they have large data sets and experience with analytical tools. From payment services to everyday banking, insight is captured that can make machine learning more powerful.

Banks are using advanced algorithms to assist with a variety of internal and customer-facing processes. What is helpful is that consumers indicate they are willing to share personal insight if there is a value trade-off. According to a recent study, 67% of customers will grant banks access to more personal data, but 63% want more tailored advice, and the same number demand priority services, such as expedited loan approvals, or a monetary benefit, such as more competitive pricing, in return for the information they share.

Using the components of machine learning, natural language processing and cognitive computing, there are several applications within banking.

Fraud detection: AI and machine learning have the ability to identify fraudulent behavior while it is happening, as well as identify what the next pattern of suspicious behavior will be. Location data can assist with this process.

Meeting regulatory requirements: Technology can be used to ensure that regulatory requirements are met and that data is kept with monitoring done on a real-time basis. This allows issues to be flagged a lot sooner.

Lowering costs and increasing revenue: The biggest opportunity lies in automating the frontline. The benefits of engaging with customers in a more automated and intelligent way offers significant cost savings, with the risk being spread over millions of customer interactions. Customer facing virtual assistants and back office robotics will be commonplace in the near future.

Improving the customer experience: Machine learning provides the opportunity for improved and faster decision making by deriving deep and actionable insights (e.g. customer behavior patterns). Some of these interactions will be with new voice or chatbot technology while other applications will be behind the scenes, supporting marketing communication.

Boost customer engagement: Artificial intelligence will assist in the creation of customized and intelligent products and services, with new features, more intuitive interactions (e.g. speech) and advisory skills (e.g. personal financial management).

As with any new endeavor, there are several challenges associated with the development and application of machine learning solutions. With most financial institutions in the learning phase, concerns revolve around data security, organizational impacts, the integration of new technologies and the understanding of use cases and ROI benefits.

4 Machine learning in European financial institutions

One of the biggest challenges is finding the right talent. Another ‘people’ issue is the impact on current employees of financial institutions. In some cases, current employees will not be well positioned for the ‘new age of banking.’ In other cases, the transformation of labor caused by the advances of AI will eliminate some positions entirely.

The banking industry is still in the early stages of developing strong AI solutions. While these solutions can definitely impact the cost and revenue structures of financial organizations, the real potential is with how artificial intelligence can improve the customer experience.

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We surveyed European banks to determine the extent of development of machine learning functionality in the banking industry.

AI and machine learning can improve banking in several categories, including customer personalization, identification of patterns and connections that humans can’t, and answering questions about banking issues in real-time. Financial institutions are already finding success with advanced analytic tools like AI and machine learning. This report identifies where the European banking industry is today, while providing an updated benchmark for organizations globally to follow for improved results.

The goal of this report is to provide a benchmark of how organizations are utilizing artificial intelligence to increase revenue, decrease costs and improve the customer experience. We provide case studies and interviews with financial institution leaders on how organizations are leveraging machine learning functionality.

By better understanding how banks are using advanced analytics, we can determine the level of digital AI maturity across the region.

Our research indicates that most institutions – and the industry as a whole – have not kept pace with consumer expectations around digital capabilities or digital engagement compared to other industries and what the large technology companies are providing. As a result, there is a significant amount of lost revenue and weakening trust due to mismanaged relationships and the inability to know the consumer.

Methodology

The analysis in this report is based on a survey of European banks. No responses from non-financial organizations were included in the results and only completed surveys were included.

Among survey respondents, 32% are from organizations with more than US $50B in assets. Another 21% were from organizations with assets of US $10-50B in assets, with 47% of the organizations participating having less than US $10B in assets.

SURVEY

The survey

Respondents by asset size (in US$)What is the asset size of your institution? (n=168)

Source: Efma and Google Cloud, Machine learning in European financial institutions

More than $50 billion

$10 billion to $50 billion

$1 billion to $10 billion

Less than $1 billion

32%

21%22%

25%

6 Machine learning in European financial institutions

To better understand the current state of machine learning technology use in banking, we asked European financial institutions about the timing of machine learning deployment, what functionalities will be implemented, the benefits and importance of machine learning and the challenges currently being faced. We also asked about the data readiness and skillset preparedness at their organization.

Banking consumers are demanding more individualized experiences as they become increasingly accepting of new technologies. In the era where tech giants like Google, Apple, Facebook and Amazon dominate, people have become accustomed to seeing personalized offers built on data that they voluntarily provide, and now it’s a common expectation.

This affords financial institutions the opportunity to meet their customers’ needs and set themselves apart. A few leading banks are doing just that – expanding on the artificial intelligence system used by voice-powered devices like the Amazon Echo, Google Home and Apple’s Siri to improve service and enhance the customer experience. For instance, Barclays Bank is developing an AI system to let customers talk to a device and get information they need for vital transactions. And the Swiss Bank UBS recently announced that it is using robots on the trading floor to boost traders’ performance.

Other banks are looking at using machine learning to help customers make investment choices in a modeling approach similar to what UBS is doing for its traders. These special kinds of machine learning models are developed to ascribe human intuition, experience and intelligence – untethered from the actual humans who have traditionally managed assets – to digital platforms that can be placed directly in the consumer’s hands.

Behind these developments are machine learning algorithms that model the characteristics of consumer behaviors – for example, incomes and typical investments which can then be used to predict investment preferences and patterns of choice. The machine learning algorithm runs in the background while another engine handling “speech-to-text” gives advice.

While these algorithms can learn, the “machine” element does not make them self-sufficient and self-sustaining. They must be fed the right data to the right models at the right intervals, typically by real live human beings – “data scientists” who are now playing pivotal roles in the digital transformation strategies of traditional financial institutions.

Machine learning has potential to make banks exponentially smarter. “Smarter” in this case means delivering better customer insights and intelligence, and thus a better customer experience – something most in the banking industry now believe is the key to differentiation, growth and increased profits.

AI deployment

In the research done for Efma and sponsored by Google Cloud, we found that 35% of European financial organizations have deployed at least one machine learning solution. This number is quite a bit higher than other recent studies done in the industry. For instance, in a survey done by the Digital Banking Report in the Fall of 2017, only 15% of financial services organizations globally had implemented an AI solution. Part of this variance may be that the size of organization skewed smaller in the earlier study.

For those organizations that have yet to deploy a solution, 23% believed they would have an AI solution in place in the next 6 months to a year, with another 13% believing they would have a machine learning solution in place within 18 months.

Survey results

FEEDBACK

7

While 17% indicated a machine learning solution was on their roadmap in the next 18 months, 12% of organizations surveyed had no plans to implement any artificial intelligence solution in the next 18 months. Given that almost half of executives surveyed in another report thought that AI would be mainstream in the next 2 years, many organizations may be caught off guard from a technology perspective.

When we evaluated the state of AI deployment by the size of organization, it is not surprising that significantly more of the largest financial institutions (over $50B) have deployed at least one AI or machine learning solution.

When we asked financial organizations which AI or machine learning solution they have deployed or were planning to deploy in the next 18 months, fraud and credit scoring solutions were the most likely to be in place or on the short-term horizon. In fact, the category of fraud, security and biometrics represented 3 of the top 5 solutions in place or being considered.

The next most likely functionality to be in place, or in near-term plans, were related to communication (chatbot/robo-advisor) and personalization solutions. Interestingly, of all of the solutions listed, chatbots had the lowest ‘no plans’ response.

Likelihood of deploying a machine learning solutionAre you considering deploying a machine learning solution in the next 18 months? (n=130)

Source: Efma and Google Cloud, Machine learning in European financial institutions

Yes we have deployed one or more solutions

We plan to implement a solution in the next six months to a year

We plan to implement a solution in the next 12 to 18 months

We have it on our roadmap to consider within the next 18 months

We have no plans to consider an AI solution in the next 18 months

35%

23%

13%

17%

12%

27

A global non-profit organisation, 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.

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About us

www.efma.com

In collaboration with:

Machine learning in European financial institutions

December 2018

www.efma.com

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