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Big Data Meets Artificial Intelligence Challenges and Implications for the Supervision and Regulation of Financial Services

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Big Data Meets Artificial Intelligence Challenges and Implications for the Supervision and Regulation of Financial Services Big data meets artificial intelligence Challenges and implications for the supervision and regulation of financial services Big data meets artificial intelligence Challenges and implications for the supervision and regulation of financial services Disclaimer Important: The English translation of this report is not binding. The German version alone is authoritative in all respects. The following report is a speculative study conducted by the Federal Financial Supervisory Authority (BaFin). It addresses the technological developments of big data analytics and artificial intelligence; elaborates scenarios outlining the potential impact these technologies might have on the financial market and on market players; and highlights the potential implications for financial supervisory authorities. BaFin commissioned this report, working closely with external partners from the scientific and the consulting community. The aim of this study is to initiate an open discussion on the impact of digitalization on established market and supervisory structures. Under no circumstances should this report be interpreted as prescript of using any particular technology or business model described therein. The legal assessments contained in the report are not intended as a description of the administrative practices within BaFin. Please note that although the utmost care has been taken in compiling the study, BaFin does not guarantee that the contents of the study are complete and correct. The foregoing shall not affect BaFin's liability for damage arising from injury to life, limb or health based on a negligent breach of duty on the part of BaFin or any wilful or grossly negligent breach of duty on the part of its employees or persons responsible for this study. Page 2 Foreword Society and the business world – the financial Felix Hufeld sector in particular – are currently undergoing profound technological change. Digitalisation President of BaFin has reached new heights: Digital networking is increasingly prevalent and new technologies, such as the Internet of Things, are helping tackle ever more complex tasks. We have at our disposal huge, ever growing quantities of data – think big data (BD) – that can be used ever faster and ever more efficiently – think artificial intelligence (AI) and self-learning machines. The following report describes the interaction between big data and artificial intelligence; how fundamentally the BDAI phenomenon can change the financial system; and what implications this has for supervisory and regulatory bodies. It becomes clear that BDAI is not only suited to optimizing existing structures, indeed it paves the way for completely new applications, products, services and business models – with all the inherent opportunities and risks. To assess this topic as well as possible and from a variety of different angles, BaFin enlisted the help of external experts: Partnerschaft Deutschland, The Boston Consulting Group and the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS). The report uses market analyses and use cases to outline potential developments as seen from the perspective of banks, insurance companies and the capital markets. However, it also makes a point of considering the consumer angle because it is the consumers who provide the data. It must be clear to everyone involved that BDAI brings with it risks as well as opportunities. These risks must be understood and actively addressed. This also and indeed particularly applies to the supervisory and regulatory authorities and our report focuses on this aspect in the closing chapter. Faced with rapid advances in digitalization, supervisory bodies need to continually ask whether their supervisory practice is keeping apace of technological progress. The same applies to their tools and instruments of regulation. Supervisory and regulatory bodies must be technology-neutral, must embrace the principle of "same business, same risks, same rules" and all the while remain on the cutting edge – increasingly a challenge these days. This is reflected in the supervisory and regulatory key questions identified in the report. Much work is still needed to adequately answer these questions. For such a fast-paced topic as BDAI, it is impossible to make any conclusive statements – not even in an extensive report such as this. Nevertheless, we believe that intensive discussion on BDAI is imperative and we hope to make a substantial contribution with this study. We look forward to many more discussions with representatives from the financial industry, the scientific community and the international financial regulatory authorities. Page 3 Contents I. Summary 7 1.1 Introduction 7 1.2 Impact on the financial system 8 1.2.1 Banks 9 1.2.2 Insurance companies 10 1.2.3 Capital markets 11 1.3 Success factors for BDAI applications and innovations 12 1.4 Supervisory and regulatory implications 13 1.5 Overarching phenomena from a societal perspective 16 II. Introduction: value creation from data with the aid of artificial intelligence 17 2.1 Big data and artificial intelligence as drivers of fundamental change 17 2.2 A new momentum generated by big data and artificial intelligence 18 2.3 Objectives and structure of this study 22 III. Technological prerequisites for the use of big data and artificial intelligence 24 3.1 From big data to artificial intelligence 26 3.2 Machine learning and its algorithms 26 3.2.1 Supervised learning 27 3.2.2 Unsupervised learning 28 3.2.3 Reinforcement learning 28 3.2.4 Deep learning: machine learning with big data 28 3.3 Evaluation and implementation of machine learning 29 3.3.1 Evaluation of models 30 3.3.2 Data analysis as a process 32 3.4 Machine learning-based systems and applications 33 3.4.1 Automated document analysis 33 Page 4 3.4.2 Speech recognition 34 3.4.3 Question answering 35 3.4.4 Intelligent agents 35 3.5 Technical approaches as a solution to the social demands from big data analytics 36 3.5.1 Intelligibility of models 36 3.5.2 Transparency and explainability 36 3.5.3 Privacy-preserving data mining 37 3.5.4 Infrastructures for data sovereignty 38 3.5.5 Non-discriminating data analysis 39 IV. Strategic prerequisites 41 4.1 Fostering customer and consumer trust 41 4.1.1 The customer perspective 41 4.1.2 Relevance of consumer trust in the context of financial services 44 4.1.3 Increase in complexity in the context of BDAI 49 4.1.4 Conclusion: ensure consumer sovereignty to build trust 50 4.2 Success factors concerning IT strategy 52 4.2.1 Data quality and scope, IT architecture and cloud computing as success factors for the use of BDAI 53 4.2.2 Technological changes require new skills and structures as well as agile ways of working 56 4.2.3 The importance of information security 57 V. Market analyses 61 5.1 Introduction 61 5.2 Banks 63 5.2.1 Introduction and status quo 63 5.2.2 The impact of BDAI on the banking sector 65 5.2.3 The impact of BDAI on the customer interface 71 5.2.4 The impact of BDAI on the core processes of the product platform 74 5.2.5 New business models through BDAI 76 5.2.6 Use cases in the banking sector 77 5.3 Insurance companies 94 5.3.1 Introduction and status quo 94 5.3.2 The impact of BDAI on the insurance sector 96 Page 5 5.3.3 The impact of BDAI on the customer interface 101 5.3.4 The impact of BDAI on the core processes 102 5.3.5 New business models through BDAI 105 5.3.6 Relevance of external data 107 5.3.7 Use cases in the insurance sector 108 5.4 Capital markets 134 5.4.1 Introduction and status quo 134 5.4.2 Likely developments: “more of the same, only faster and better” 137 5.4.3 Further developments: higher connectivity and complexity 140 5.4.4 Use cases 145 VI. Supervisory and regulatory implications 164 6.1 Introduction 164 6.2 Supervisory and regulatory implications 165 6.2.1 Financial stability and market supervision 166 6.2.2 Firm supervision 170 6.2.3 Collective consumer protection 176 6.3 Effects on society as a whole 181 VII. Appendix 184 7.1 In-depth description of machine learning 184 7.1.1 Supervised learning 185 7.1.2 Unsupervised learning 190 7.2 Summary of the papers of the European supervisory authorities and the Financial Stability Board 193 Picture credits: Cover design: iStock/spainter_vfx, Executive Board photos by Bernd Roselieb Photography Page 6 I. Summary 1.1 Introduction Big data and artificial intelligence have triggered profound changes | Society and the business world are currently undergoing profound technological changes. Digital networking is increasingly prevalent and new technologies are helping tackle ever more complex tasks. This trend is driven in particular by the availability of large quantities of data – big data (BD) – and by the improved opportunities for using this data – artificial intelligence (AI)1. This report examines how these phenomena work together within the financial system and refers to them collectively as "BDAI". Applying BDAI mostly means using methods of machine learning, where algorithms give computers the ability to learn from existing data and then apply what they have learned to new data. Successful BDAI applications are self-reinforcing and can spread rapidly | The relevance of BDAI is growing as technology, companies and consumers interact. First, current technological progress facilitates the extensive and practical use of BDAI. Second, companies are increasingly relying on data and the value they extract from it to optimise their business models and processes. Third, consumer behaviour is increasingly shaped by digital applications, which in turn boosts the generation and availability of data. The last two points in particular can have a strong, self-enhancing effect on one another.
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