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Ai in financial services

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A joint report between EY and LSE with contribution from Seldon. This report describes research undertaken by The London School of Economics and Political Science on behalf of EY Financial Services to investigate the use of Artificial Intelligence and Machine Learning and to provide one use case for each of the following sectors; Insurance, Banking & Capital Markets, and Wealth & Asset Management.

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Ai in financial services

  1. 1. AI in Financial Services LSE MiM Business Project 1
  2. 2. AI in Financial Services Prepared for EY FinTech team Executive Summary This report describes research undertaken by The London School of Economics and Political Science on behalf of EY Financial Services to investigate the use of Artificial Intelligence and Machine Learning and to provide one use case for each of the following sectors; Insurance, Banking & Capital Markets, and Wealth & Asset Management. Although there are differing goals for Artificial Intelligence, we understand the ultimate aim of AI to be the pursuit of an intelligent machine which is able to learn, plan, reason and communicate in natural language. However, we found the industry has shifted to Applied AI, where the focus lies on solving an isolated problem. Machine Learning is a branch of Applied AI, widely used and the most prominent type across industries. The chosen use case in Insurance, Cocoon, illustrates the trend in our research of partnerships between large firms and startup businesses as Machine Learning coupled with the Internet of Things increasingly becomes a reality. The chosen use case in Banking and Capital Markets, Kensho, illustrates the trend that technology investments are on the rise. In fact, Capital Market executives invest more and more of their budget in Machine Learning technologies. Building an automated investment system that evolves with the market in real-time, Aidyia demonstrates the growing influence of Machine Learning on Wealth & Asset Management. 2
  3. 3. AI in Financial Services Prepared for EY FinTech team Contents 3 Slide Slide Introduction Goals of the presentation 1 Insurance Banking & Capital Markets Wealth & Asset Management Methods Methodology Source of Information 6 Discussion Short/medium/long-term impacts of AI Conclusions from case studies 36 Taxonomy 8 Recommendations 39 Literature review AI academic background Difference in scholarships Links to management literature Debate on the impact of AI on management 11 Appendix • Project Timeline • Glossary • Detailed Definitions and Literature Review • Overview of Industries and Trends • Use Cases and Tracker 43 Results Overview of Industries and trends Criteria for Use Cases 16 Bibliography 95
  4. 4. AI in Financial Services Prepared for EY FinTech team4 Lee Baker - Seldon Jason Stockwood – Simply Business With special thanks for their contributions
  5. 5. AI in Financial Services Prepared for EY FinTech team Goals of the presentation • The goal of this presentation is to apply academic theory to practice, working with a real-life business problem to reach quality recommendations. • This research aims to investigate the use of Artificial Intelligence in Financial Services and provide one use case for each of the following sectors: Insurance, Banking & Capital Markets, and Wealth & Asset Management. • This project also aims to provide an understanding of key definitions in relation to Artificial Intelligence and Machine Learning, and consider the short, medium and long term impacts of Machine Learning on Financial Services as a whole. 5
  6. 6. AI in Financial Services Prepared for EY FinTech team Introduction • At present, the Artificial Intelligence we see within Financial Services and other industries is applied AI, mainly Machine Learning. • There is increasing augmentation and automation of manual and cognitive processes. Applications include fraud detection and chatbots. • We can already see this in every big bank and major player in the industry, such as HSBC, Barclays and Zurich Insurance. • We also see these large players partnering with tech start-ups to develop these Machine Learning solutions and build them into their own business propositions. 6
  7. 7. AI in Financial Services Prepared for EY FinTech team7 Methods
  8. 8. AI in Financial Services Prepared for EY FinTech team Methodology and Sources • Interpretative research method – analysis of different methods and technologies; assessment of their relevance and potential impact • Qualitative data – Research on definitions and use case applications • Sources used: • Computer technology blogs, most current information (2016 only) • Computer technology encyclopedias • Industry websites, company websites, blogs • Academic papers • Expert interviews (refer to appendix for complete list) • Consultancy firm reports • Conferences – Group attended Fintech live! 8
  9. 9. AI in Financial Services Prepared for EY FinTech team9 Taxonomy
  10. 10. AI in Financial Services10 John McCarthy coins term First AI Program “Logic Theorist” Negative Results for NLP Turing Test Light Hill Report published. UK Government ceases AI funding LISP machines developed and marketed MIT AI lab founded Rise of desktop computers IBMs Deep Blue beats chess champion Firms invest over 1 billion in LISP Machines Market for specialized AI hardware collapses Increasing computer power Focus placed on solving specific isolated problems Major advances in AI Emphasis on ML Natural Language used to make recommendations Self Driving cars Google ML commercially available on personal computers TimelineAIInterest
  11. 11. AI in Financial Services Prepared for EY FinTech team11
  12. 12. AI in Financial Services Prepared for EY FinTech team12 Literature Review
  13. 13. AI in Financial Services Prepared for EY FinTech team AI academic background • 500 BC: Initial interest in humans creating intelligent machines; Greek Mythology • 1854: ”Laws of thought” introduced by George Boole – introduction of logic concepts • 1956: Artificial Intelligence term officially coined by Professor John McCarthy • 1960-1980: Field branched off in multiple directions and grew rapidly • 1987-1993: “AI Winter” 13
  14. 14. AI in Financial Services Prepared for EY FinTech team Difference in scholarships Acting humanly: The Turing Test approach - Intelligent behavior: ability to achieve human- level performance on all cognitive tasks Thinking humanly: The cognitive modelling approach - Need to determine how humans think - Concern with tracing reasoning steps Thinking rationally: The laws of thought approach - From the field of logic - Translate problems in logical notation to create intelligence systems Acting rationally: The rational agent approach - Acting rationally: acting so as to achieve one’s goals given one’s beliefs 14 More detail in appendix
  15. 15. AI in Financial Services Prepared for EY FinTech team Links to management literature Christenson’s theory of disruption - Incumbents tend to act on “sustaining innovation” - Make themselves vulnerable to “disruptive innovations” at the bottom of the market - New technology ends up supraceeding existing one - E.g. Cellular phones disrupting fixed line telephone - Applying the theory: not necessarily the theory, disruptions occur from the new technology, evidence of partnership between incumbents and start-ups: why? 15 More detail in appendix
  16. 16. AI in Financial Services Prepared for EY FinTech team Debate on the impact of AI on management • Oxford University research on 702 occupations: 47% are at risk of being obsolete in 10-20 years • Harvard Business Review survey study on 1770 managers in 14 countries: AI will take over administrative work and leave time for managers to focus on judgement work, creativity, and social skills • DeepMind: No evidence that advances in AI are impacting the workforce; technology is best used to help humans rather than replace them 16 More detail in appendix
  17. 17. AI in Financial Services Prepared for EY FinTech team17 Results
  18. 18. AI in Financial Services Prepared for EY FinTech team Definition of the industries Insurance is a financial product sold to safeguard individuals, organisations and their property against the risk of loss, damage or theft. Banking is the financial dealings of an institution that provides business loans, credit, savings and checking accounts for companies and for individuals. Capital markets are markets for the buying and selling financial instruments, such as equity securities and debt securities. Wealth management is a high-level professional service that combines financial and investment advice, accounting and tax services. Asset management is the sector of the financial services industry that manages investment funds and segregated client accounts. 18
  19. 19. AI in Financial Services Prepared for EY FinTech team Trends across industries Insurance 1. AI and the Internet of Things aims to predict risks and improve operational efficiencies for customers 2. InsurTech: Incumbent partnerships are key Banking & Capital Markets 1. Responding to customer expectations is the rationale behind technology transformation 2. Technology investments are on the rise Wealth & Asset Management 1. Markets are changing as new techniques are being employed 2. Need to embrace AI to sustain competitive advantage 19
  20. 20. AI in Financial Services Prepared for EY FinTech team20 Criteria for Use Case Analysis
  21. 21. AI in Financial Services Prepared for EY FinTech team Criteria Further detail Scale Impact - Will it disrupt/change in the industry? (+) - Can it be applied in other industries? (+) - Are the big players in the industry interested in implementing it? (+) - Will it create cost savings for the company? (+) 0-4 Feasibility - Is there technology for the idea to be implemented? (+) - Are there regulatory or ethical roadblocks? (-) - Is the technology development in a boom phase? (+) - Data feasibility – availability of data and ease of access to it (+) 0-4 Time - Do we see it starting to appear now? - Is it a theory in development/ model in progress? - Is it a technology already in used but underused - Is it too far in the future/ relying on technological developments? Short Term: 0 – 3 yrs Medium Term: 4 – 5 yrs Long Term: 6 + yrs 21 Criteria
  22. 22. AI in Financial Services Prepared for EY FinTech team22 Insurance – deep dive into use case analysis
  23. 23. AI in Financial Services Prepared for EY FinTech team Proposition Company name Impact Feasibility Final score Time 1. Internet of Things Concirrus 2 2 4 Medium 2. AI Subsound Technology Cocoon 4 4 8 Short 3. Real-time data analytics MetLife Xcelerate 3 2 5 Short 4. Machine learning RiskGenius 2 1 3 Medium 5. Open Source Machine Learning Zurich Insurance 2 3 5 Long 6. Automating processes Genworth Financial 3 3 6 Short Insurance - Overview of use cases 23 More detail in appendix
  24. 24. AI in Financial Services Feasibility Impact 2 4 Concirrus Cocoon MetLife Xcelerate Genworth FinancialZurich Insurance Insurance – Graph against criteria 24 RiskGenius Short Term Long Term Zurich Insurance Cocoon Genworth Financial MetLife Xcelerate Concirrus RiskGenius Time: Prepared for EY FinTech team
  25. 25. AI in Financial Services Feasibility Impact 2 4 Concirrus Cocoon MetLife Xcelerate Genworth FinancialZurich Insurance Insurance – Graph against criteria 25 RiskGenius Zurich Insurance Cocoon Genworth Financial MetLife Xcelerate Concirrus RiskGenius Time: Short Term Long Term Prepared for EY FinTech team
  26. 26. AI in Financial Services Short Term Impact Medium Term Impact Long Term Impact Use Case Impact - AI Subsound Technology Cocoon is an InsurTech start-up that combines advanced Machine Learning with Internet of Things in a home security device • Changes are beginning to occur as InsurTech partners with open- minded incumbents • As IoT provides the data, Machine Learning will then extract the actionable insight for insurers • For customers, IoT devices will reduce premiums and improve their customer service 26 Prepared for EY FinTech team
  27. 27. AI in Financial Services Prepared for EY FinTech team27 Banking & Capital Markets – deep dive into use case analysis
  28. 28. AI in Financial Services Prepared for EY FinTech team Proposition Company name Impact Feasibility Final score Time 1. Fraud detection IBM 3 4 7 Short 2. Credit decisioning Logical Glue 2 2 4 Medium 3. Fraud Hub for Gaming Feature Space 4 3 7 Short 4. Financial market predictions Kensho 4 4 8 Short 5. High frequency trading RenTech 3 2 5 Long Banking & Capital Markets - Overview of use cases 28 More detail in appendix
  29. 29. AI in Financial Services Short Term Long Term Feasibility Impact 2 4 Banking & Capital Markets – Graph against criteria 29 Time: Feature Space Kensho IBM Logical Glue RenTech Logical Glue RenTech IBM Feature space Kensho Prepared for EY FinTech team
  30. 30. AI in Financial Services Short Term Long Term Feasibility Impact 2 4 Banking & Capital Markets – Graph against criteria 30 Time: Feature Space Kensho IBM Logical Glue RenTech IBM Feature space Kensho Logical Glue RenTech Prepared for EY FinTech team
  31. 31. AI in Financial Services Prepared for EY FinTech team Short Term Medium Term Long Term Seeking to replace equity analysts and thus generating significant cost cutting for firms. Making this technology more accessible to the masses. Creating new business lines in emerging sectors such as the commercialisation of space, autonomous vehicles and wearable technologies. Use Case Impact - Kensho 31 Partnered with CNBC, which is running a new series called #AskKensho Provides the tools to powerhouse investment banks to compete with the “quants” that have taken over the business for the last decade
  32. 32. AI in Financial Services Prepared for EY FinTech team32 Wealth & Asset Management – deep dive into use case analysis
  33. 33. AI in Financial Services Prepared for EY FinTech team Proposition Company name Impact Feasibility Final score Time 1. Natural Language Processing Avlien 3 3 6 Short 2. Sentiment Analysis Sensai, Sentifi, Running Alpha, Amareos 2 3 5 Short 3. Clusters in real-time AbleMarkets, AlgoDynamics 2 2 4 Medium 4. Predictive analytics Aidyia, hiHedge, FNA platform 4 3 7 Medium Wealth & Asset Management - Overview of use cases 33 More detail in appendix
  34. 34. AI in Financial Services Feasibility Impact 2 4 Wealth & Asset Management – Graph against criteria Time: Aidyia Amareos AlgoDynamix Aylien AlgoDynamix Amareos Aidyia Aylien 34 Short Term Long Term Prepared for EY FinTech team
  35. 35. AI in Financial Services Short Term Long Term Feasibility Impact 2 4 Wealth & Asset Management – Graph against criteria Short TermTime: Aidyia Amareos AlgoDynamix Aylien AlgoDynamix Amareos Aylien Aidyia 35 Prepared for EY FinTech team
  36. 36. AI in Financial Services Prepared for EY FinTech team Short Term Medium Term Long Term Use Case impact - Aidyia Decreases risks and costs for fund managers Brings differentiated market position with unbiased methodology and better performance Changes the Asset Management Industry completely as the amount of assets managed by AI increases Aidyia has been developing AI-driven strategies based on deep learning for years. It demonstrates the ultra application of machine learning and may change the whole market completely. 36
  37. 37. AI in Financial Services Prepared for EY FinTech team37 Discussion
  38. 38. AI in Financial Services Prepared for EY FinTech team Hype Cycle Framework applied to Use Cases Source:Gartner (July 2016) 38 Prepared for EY FinTech team
  39. 39. AI in Financial Services Prepared for EY FinTech team Short, Medium and Long-Term Impact Short-Term Medium-Term Long-Term • Fraud detection, Anomaly Detection, Pattern Recognition, Natural Language Processing become the norm • Rise of RegTech • Increased demand for data science talent • Predictive Analytics – Tailored customer service • High Frequency Trading will be completely automated • Change management systems – how to prepare for an Intelligent Agent • Displacement of Jobs – even non-routine and cognitive roles • Physical presence on the high street will become obsolete • Restructuring of business models 39
  40. 40. AI in Financial Services Prepared for EY FinTech team40 Summary
  41. 41. AI in Financial Services Prepared for EY FinTech team • Financial institutions may need to consider restructuring their business models in order to harness this new technology. • There will be a need to create job positions in relation to AI. • However, proceed with some caution due to the existing challenges in terms of trust, privacy and data issues. • They must also keep an eye on RegTech to better address new regulatory requirements. • Overall, firms should embrace AI now or face the possibility of being left behind. 41 Summary
  42. 42. AI in Financial Services Prepared for EY FinTech team42 Thank you. Any questions?
  43. 43. AI in Financial Services Prepared for EY FinTech team43 Stefanie Lieberherr Olivia HotchkissMarta Oliveira Chemsi BennisSophia Wu The LSE Team
  44. 44. AI in Financial Services Prepared for EY FinTech team44 Appendix • Project Timeline • Glossary • Detailed Definitions • Detailed Literature Review • Overview of Industries and Trends • Insurance Use Cases • Banking & Capital Markets Use Cases • Wealth & Asset Management Use Cases • Use Case Tracker
  45. 45. AI in Financial Services Prepared for EY FinTech team Project Timeline 45
  46. 46. AI in Financial Services Prepared for EY FinTech team Project Timeline 46
  47. 47. AI in Financial Services Prepared for EY FinTech team Glossary 47
  48. 48. AI in Financial Services Prepared for EY FinTech team Term Definition Artificial Intelligence AI is a subfield of computer science that aims to emulate human intelligence in a machine. The way of achieving intelligence in a machine and what is meant by intelligence in a machine is where the definitions and goals deviate. There are two approaches to achieving ‘intelligence’ in a machine, Strong AI and Weak AI. Strong AI aka. Artificial General Intelligence (AGI) aims to create a machine that can fully perform any action a human kind. Weak AI aims to use certain aspects of human reasoning but does not represent the human mind within a machine. AI Winter The field of AI research follows the patterns of “hype cycles”, where disappointment and criticism follows the height of enthusiasm, resulting in funding cuts and then starting the cycle over again with renewed interest years or decades later.” 1 2 Those periods after the hype experiencing funding cuts are known as the AI Winters. There have been two prominent AI winters, the first spanning 1974-1980 and the second in 1987-1993. Artificial Neural Network Artificial Neural Network (ANN) is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.3 Batch data Analytics Where data is collected over a period of time, the batch of data is inputted into the system, the data is then processed, analyzed and an output written 4 Big Data Very large data sets that need to be analyzed through computational methods to reveal patterns, trends and behavior. Binary Logic aka. Boolean Logic This is the simplest type of formal logic where there are only ever two values. Usually in the form of ‘True’ or ‘False’ Cognitive Computing The field of cognitive computing is specialised in using algorithms that are based on the way the mind works. Computational Intelligence (CI) CI is a subset of Artificial Intelligence, more specifically weak Artificial intelligence, based on soft computing. It is often used as a synonym for soft computing. 1 Crevier, Daniel. The Tumultuous History Of The Search For Artificial Intelligence. 1st ed. New York, NY: Basic Books, 1993. Print. 2 "AI Winter". En.wikipedia.org. N.p., 2017. Web. 5 Jan. 2017. 3 Stergiou, Christos and Dimitrios Siganos. "Neural Networks". Doc.ic.ac.uk. N.p., 2017. Web. 21 Jan. 2017. 4 Moise, Izabela, Dirk Helbing, and Evangelos Pournaras. Realtime Data Analytics. Zurich: EZH, 2015. Web. 28 Dec. 2016. 48 Prepared for EY FinTech team
  49. 49. AI in Financial Services Prepared for EY FinTech team Term Definition Data Mining aka. Knowledge Discovery “It is the process of analyzing data from different perspectives and summarizing it into useful information” Data Science “It is an interdisciplinary field about scientific processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, [ which is a continuation of some of the data analysis fields such as statistics, machine learning, data mining, and predictive analytics” 1 Decision Tree Learning An algorithm for predictive modelling in machine learning. 2 “This is the most popularly used algorithm for inductive inference which has been successfully applied to a range of tasks such as learning to assess credit risk of loan applicants. “ Deep Learning “Deep learning is a sub field and set of algorithms in machine learning that attempt to learn in multiple levels, corresponding to different levels of abstraction. It typically uses artificial neural networks. The levels in these learned statistical models correspond to distinct levels of concepts, where higher-level concepts are defined from lower-level ones, and the same lower-level concepts can help to define many higher-level concepts. 3 These levels of representation and abstraction help to make sense of data such as images, sound, and text. 4 These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics.” 5 Expert System “A computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice” it is distinguished from other programs in that it “simulates human reasoning, performs reasoning over representation of human knowledge and solves problems by heuristic or approximate methods” 6 Expert systems are believed to be among the first successful Artificial Intelligence software. 1 Dhar, Vasant. "Data Science And Prediction". Communications of the ACM 56.12 (2013): 64-73. Web. 21 Jan.2017. 2 Brownlee, Jason. "Classification And Regression Trees For MachineLearning - Machine Learning Mastery". Machine Learning Mastery. N.p., 2016. Web. 23 Dec. 2016. 3 Deng, Li and Dong Yu. "Deep Learning: Methods And Applications".Foundations and Trends® inSignal Processing 7.3-4 (2014): 197-387. Web. 28 Dec. 2016. 4 "Deep Learning Tutorials — Deeplearning 0.1 Documentation". Deeplearning.net. N.p., 2016. Web. 28 Dec. 2016. 5 LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep Learning".Nature 521.7553 (2015): 436-444. Web. 27 Dec. 2016. 6 Jackson, Peter. Introduction To Expert Systems. 3rd ed. Addison-Wesley,1998. Print. 49 Prepared for EY FinTech team
  50. 50. AI in Financial Services Prepared for EY FinTech team Term Definition Fuzzy Logic “Is a set of mathematical principles for knowledge representation based on degrees of membership rather than on crisp membership of classical binary logic.1 “Fuzzy logic provides a way of taking our common-sense knowledge that most things are a matter of degree into account when a computer is automatically making a decision.” 2 Genetic Algorithms “A genetic algorithm is a method for solving both constrained and unconstrained optimization problems based on a natural selection process that mimics biological evolution”3 Hard Computing (Conventional Computing) Hard computing or Conventional Computing as it is more commonly known is based on binary logic, meaning two valued outputs such as yes/no, true/false etc. Inductive Logic Programming “Inductive Logic Programming (ILP) is a research area formed at the intersection of Machine Learning and Logic Programming. ILP systems develop predicate descriptions from examples and background knowledge.” 4 Instance Based Learning Aka. Memory based learning “is a family of learning algorithms that, instead of performing explicit generalization, compares new problem instances with instances seen in training, which have been stored in memory” 5 Internet of Things “Simply put, this is the concept of basically connecting any device with an on and off switch to the Internet (and/or to each other). The analyst firm Gartner says that by 2020 there will be over 26 billion connected devices...” 6 LISP Machines These are expert systems that would run the LISP programming Machine Learning Machine Learning is a subfield of soft computing and a branch of (weak) Artificial intelligence which "gives computers the ability to learn without being explicitly programmed (Arthur Samuel 1959)" 7 There are five types of machine learning; Supervised Learning, Unsupervised Learning, Semi supervised Learning, Deep Learning and Reinforcement Learning. Machine learning has now become somewhat synonymous with AI, and concerning business applications data scientists really only refer to machine learning as this is the most prevalent and most applied type of weak AI. 1 Negnevitsky, Michael. Artificial Intelligence. 1st ed. New York: Addison Wesley, 2002. Print. 2 "Glossary - Stottler Henke Associates, Inc.". Stottlerhenke.com. N.p., 2016. Web. 19 Nov. 2016. 3 "Genetic Algorithm". Mathworks.com. N.p., 2016. Web. 3 Jan. 2017. 4 Muggleton, Stephen. "Inductive Logic Programming". N.p., 2016. Web. 27 Dec. 2016. 5 "Instance-Based Learning". En.wikipedia.org. N.p., 2016. Web. 26 Dec. 2016. 6 Morgan, Jacob. "A Simple Explanation Of 'The Internet Of Things'". Forbes.com. N.p., 2014. Web. 3 Jan. 2017. 7 Simon, Phil. Too Big To Ignore. 1st ed. Hoboken, New Jersey: John Wiley & Sons, Inc., 2013. Print. 50 Prepared for EY FinTech team
  51. 51. AI in Financial Services Prepared for EY FinTech team Term Definition Natural Language Processing (NLP) “NLP applications attempt to understand natural human communication, either written or spoken, and communicate in return with us using similar, natural language.”1 Real Time data Analytics “Continually input, process and output data. Data must be processed in a small time period. The analytics ie the output is always being run and the data input does not stop. This type of processing is important in areas such as fraud detection.” 2 Reinforcement learning “Reinforcement learning is a type of machine learning… which allows machines and software agents to automatically determine the ideal behaviour within a specific context, in order to maximize its performance. Simple reward feedback is required for the agent to learn its behaviour; this is known as the reinforcement signal.” 3 Representation learning “Representation learning is a set of methods that allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification” 4 Soft Computing Soft Computing is one of two branches of Weak Artificial Intelligence, the other being Hard Computing. Soft computing is commonly characterised by the following collection of methodologies: Evolutionary computing, Machine Learning, Probabilistic Reasoning and Fuzzy Logic. In contrast to conventional/hard computing, Soft Computing allows for partial truths, imprecision, approximation and uncertainty. Supervised Machine Learning “Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.” 5 The output data sets are provided. “The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.”6 7 1 Marr, Bernard. "What Is The DifferenceBetween Artificial Intelligence And Machine Learning?". Forbes.com. N.p., 2016. Web. 8 Dec. 2016. 2 Moise, Izabela,Dirk Helbing, and Evangelos Pournaras.Realtime Data Analytics. Zurich: EZH,2015.Web. 28Dec. 2016. 3 Champandard, Alex J. "Reinforcement Learning Introduction". Reinforcementlearning.ai-depot.com. N.p., 2016. Web. 27 Dec. 2016. 4 LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep Learning".Nature 521.7553 (2015): 436-444. Web. 27 Dec. 2016. 5 Brownlee, Jason. "Classification And Regression Trees For MachineLearning - Machine Learning Mastery". Machine Learning Mastery. N.p., 2016. Web. 23 Dec. 2016. 6 Brownlee, Jason. "Classification And Regression Trees For MachineLearning - Machine Learning Mastery". Machine Learning Mastery. N.p., 2016. Web. 23 Dec. 2016. 7 Chapelle, Olivier, Bernhard Schölkopf, and Alexander Zien. Semi-Supervised Learning. 1st ed. Cambridge, Mass.:MIT Press, 2006. Print. 51 Prepared for EY FinTech team
  52. 52. AI in Financial Services Prepared for EY FinTech team Term Definition Support Vector Machines “A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyper plane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyper plane which categorizes new examples.”1 Unsupervised Machine Learning “Unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. These are called unsupervised learning because unlike supervised learning there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.” 2 1 "Introduction To Support Vector Machines — Opencv 2.4.13.2 Documentation".Docs.opencv.org. N.p., 2017. Web. 3 Jan.2017. 2 Brownlee, Jason. "Classification And Regression Trees For MachineLearning - Machine Learning Mastery". Machine Learning Mastery. N.p., 2016. Web. 23 Dec. 2016. 52 Prepared for EY FinTech team
  53. 53. AI in Financial Services Prepared for EY FinTech team Detailed Definitions 53
  54. 54. AI in Financial Services Prepared for EY FinTech team The Taxonomy Source: Mitchell-Guthrie, Polly. "Looking Backwards, Looking Forwards: SAS, Data Mining, And Machine Learning". SAS Blog - Subconscious Musings. N.p., 2014. Web. 17 Dec. 2016. 54
  55. 55. AI in Financial Services Prepared for EY FinTech team Artificial Intelligence is a term, which has to this day found little consensus amongst academics on its definition. It is a term that has evolved in parallel to the evolution of its application and implementation which mirrored the evolution of technological capabilities. The primary goal of AI has however always stayed the same; to create an intelligent machine that would among other things be able to Learn, Plan, Reason, Communicate in natural language and Represent Knowledge. What we see being applied to financial services today is more commonly known as Applied AI. Alan Turing known as the father of AI, and was the first person to invent a test for intelligence in a machine when he invented the Turing Test in 1950. John McCarthy who successfully set up the first conference for AI in 1956 coined Artificial Intelligence as a term. By the 1960’s AI research was at its prime and was heavily funded by the Department of Defence in the United States (US). Other founders of AI such as Herbert Simon were extremely optimistic at the time about AI’s future stating "machines will be capable, within twenty years, of doing any work a man can do.”1 55 Artificial Intelligence
  56. 56. AI in Financial Services Prepared for EY FinTech team The AI boom was in full swing up until around 1970, and by 1974 the UK and US governments started cutting funding for AI research due to lack of progress in the field. The years that followed became known as the first AI Winter. Throughout this first AI winter, research about AI had to be published under a different name to receive funding. Terms that emerged at the time were pattern recognition, knowledge based systems, informatics, cognitive systems, computational intelligence and even machine learning. The 1980’s saw the end of the first AI winter with the rise of expert systems. However the collapse of the billion dollar LISP machine industry sunk the AI field into another AI winter at the end of the 1980’s. The early 21st Century then saw another boom in AI due to increasing computational power and more focus on solving specific problems. Advanced statistical techniques (loosely known as deep learning), access to large amounts of data and faster computers enabled advances in machine learning and perception. By 2010 machine learning was widely applied all over the world.2 56 Artificial Intelligence
  57. 57. AI in Financial Services Prepared for EY FinTech team Soft Computing: Soft Computing is one of two branches of Weak Artificial Intelligence, the other being Hard Computing. Soft computing is commonly characterised by the following collection of methodologies: Evolutionary computing, Machine Learning, Probabilistic Reasoning and Fuzzy Logic. In contrast to conventional/hard computing, Soft Computing allows for partial truths, imprecision, approximation and uncertainty. Machine Learning: The term was coined by Arthur Samuel in 1959 and is a subfield of soft computing – a branch of (weak) Artificial intelligence – which "gives computers the ability to learn without being explicitly programmed” (Arthur Samuel 1959) There are five types of machine learning; Supervised Learning, Unsupervised Learning, Semi supervised Learning, Deep Learning and Reinforcement Learning 57 Further definitions
  58. 58. AI in Financial Services Prepared for EY FinTech team Applications of ML / where is ML today • Data Security • Personal Security • Financial Trading • Healthcare • Marketing Personalization • Fraud Detection • Recommendations • Online searches • Natural Language Processing (NLP) • Smart Cars/Self Driving Cars Source: Marr, Bernard. "Forbes Welcome". Forbes.com. N.p., 2017. Web. 6 Jan. 2017. 58
  59. 59. AI in Financial Services Prepared for EY FinTech team Detailed Literature Review 59
  60. 60. AI in Financial Services Prepared for EY FinTech team AI academic background • Interest by humans in creating intelligent machines traces back all the way from Greek mythology • Pygmalion myth: A statue brought to life for the love of her sculptor • 1800s: ”Laws of thought” introduced by George Boole – introduction of logic concepts • Artificial intelligence term officially coined by Professor John McCarthy in 1956 • Field branched off in multiple directions, and grew rapidly in the 60s and 70s • Mid 1980s: “AI Winter” 60
  61. 61. AI in Financial Services Prepared for EY FinTech team Difference in scholarships Acting humanly: The Turing Test approach - Intelligent behavior defined as the ability to achieve human-level performance on all cognitive tasks - Capabilities necessary: natural language processing, knowledge representation, automated reasoning and machine learning Thinking humanly: The cognitive modelling approach - Need to determine how humans think– through introspection or psychological experiments - Concern with tracing reasoning steps and comparing with human subjects - Field of cognitive science combining computer models and psychology Thinking rationally: The laws of thought approach - From the field of logic (first introduced through Aristotle and his attempt to codify “right thinking”) - Builds on programs that translate problems in logical notation to create intelligence systems - Obstacles: Not easy to state informal knowledge in formal terms; “in principle” vs. in practice Acting rationally: The rational agent approach - Acting rationally: acting so as to achieve one’s goals given one’s beliefs = AI becomes the study and construction of rational agents - Advantages: more general than “laws of thought” approach (focus on outcome rather than process), more amenable to scientific development due to the clear definition of rationality61
  62. 62. AI in Financial Services Prepared for EY FinTech team Links to management literature • Christenson’s theory of disruption • Incumbents tend to act on “sustaining innovation” (making their products more sophisticated and complex) • Make themselves vulnerable to “disruptive innovations” at the bottom of the market • E.g. Cellular phones disrupting fixed line telephony • Theory: disruptor starts with inferior technology, but as it improves it ends up supraceeding the existing one • Applying to ML in FS: not necessarily the theory, disruptions occur from the new technology, incumbents are trying to partner with them to harness these skills and not be replaced– change may be due to historical examples of these obsolete technologies, increasing awareness. 62
  63. 63. AI in Financial Services Prepared for EY FinTech team Debate on the impact of AI on management • Oxford University research on 702 occupations: 47% are at risk of being obsolete in 10-20 years • Jobs traditionally automated are manual, we are already seeing cognitive and non- routine ones being replaced = Management (E.g. Uber, Lyft) • Harvard Business Review survey study on 1770 managers in 14 countries: argue AI will take over administrative work and leave time for managers to focus on judgement work, creativity, and social skills • DeepMind (as reported by Fortune): No evidence that advances in AI are impacting the workforce; technology is best used to help humans rather than replace them • ”Humans remain the ultimate controller of the systems” 63
  64. 64. AI in Financial Services Prepared for EY FinTech team Overview of Industries and Trends 64
  65. 65. AI in Financial Services Prepared for EY FinTech team Definition of the industry and trends • Insurance is a financial product sold by insurance companies to safeguard individuals, organisations and / or their property against the risk of loss, damage or theft • In the mature insurance industry AI is predicted to impact three main areas: • There are two key trends highlighted in the literature: 1. AI and the Internet of Things is moving Insurance towards predicting risks before they occur, reducing claims and improving operational efficiencies for customers 2. Incumbents are partnering with InsurTech with the biggest threats coming from open-minded firms who are embracing smart startups Efficiency Competitiveness Risk assessment 65
  66. 66. AI in Financial Services Prepared for EY FinTech team Definition of the industry and trends • Banking is the financial dealings of an institution that provides business loans, credit, savings and checking accounts for companies and for individuals. • Capital markets are markets for the buying and selling financial instruments. These are equity securities, which are often known as stocks, and debt securities, which are often known as bonds. Capital markets involve the issuing of stocks and bonds . • There are three key trends highlighted in the literature: 1. Nearly ¾ of capital market executives invest more than 11% of their capital budget in technology. 2. 33% say lack of technology is a main obstacle to business transformation in their organization. 3. Nearly 70% of hedge fund traders now use algorithms for 40% of their trading 66 Minimise risk Increase efficiency Reduce cost of workforce Sustain competitive advantage
  67. 67. AI in Financial Services Prepared for EY FinTech team • Wealth management is a high-level professional service that combines financial and investment advice, accounting and tax services, retirement planning and legal or estate planning for one set fee. • Asset management is the sector of the financial services industry that manages investment funds and segregated client accounts. • In the mature insurance industry AI is predicted to impact three main areas: • There are two key trends highlighted in the literature: • Market are changing as techniques being employed: Techniques, such as natural language processing, sentiment analysis, clusters in real time and predictive analytics, start to make a difference • The need to embrace AI: The markets are getting complex and more sophisticated, and tools with better robustness are needed Definition of the industry and trends 67 Reduce Costs Increase Performance Improve Market Efficiency
  68. 68. AI in Financial Services Prepared for EY FinTech team Insurance Use Cases 68
  69. 69. AI in Financial Services Prepared for EY FinTech team 1. Internet of Things: Concirrus • A platform that is a series of digital insurance underwriting tools that reduce risk • Concirrus was founded in 2012 to enable businesses to take advantage of connected technologies and the Internet of Things • For example, the platform collates and analyses multiple streams of shipping data, giving marine insurers new insight into both cargo and hull and their associated risks. This intelligence allows commercial insurers to adjust their risk portfolio and minimise claims costs as a result Impact IoT has the potential to disrupt the industry and others, however this application has not been taken up widely yet Time We are starting to see IoT appear in the industry but further development is needed Feasibility The technology has yet to reach a boom phase however data should not be a large roadblock with these applications 69
  70. 70. AI in Financial Services Prepared for EY FinTech team 2. AI Subsound Technology: Cocoon • An InsurTech start-up that combines Machine Learning with Internet of Things • Cocoon uses unique Subsound® technology to listen for infrasound - subtle, inaudible vibrations in the air caused by movement – which is then linked to an app that monitors the real-time alerts • Using advanced Machine Learning, Cocoon is continually learning the unique sound signature of a home. Out of the box it resembles a blank brain which then learns in 7 – 10 days the ‘normal’ sounds of your home Impact Can reduce customer premiums and improve service, building on trends around customer-centricity. It also reduces risk for the insurer Time Technology is ready to be implemented in the market and is beginning to do so Feasibility Implemented now by two large insurers - no data feasibility restrictions or regulatory roadblocks at present 70
  71. 71. AI in Financial Services Prepared for EY FinTech team 3. Real-time data analytics: MetLife • MetLife Xcelerate is a new product allowing insurance quotes in 2 minutes (instead of 20) for Home and Auto • It uses public records and consumer reports to gather information about a household's drivers and vehicles, reducing the amount that needs to be filled into an application form • "We’re exploring a lot of our access to unstructured data and we’re using machine learning for that” - MetLife VP of Enterprise Analytics, Malene Haxholdt Impact There is technology available and it could introduce efficiencies and cost savings across the entire industry and others Time This technology is ready to be implemented now Feasibility There will be regulatory and data roadblocks when trying to access and use customers’ data in this way 71
  72. 72. AI in Financial Services Prepared for EY FinTech team 4. Machine learning: RiskGenius Impact Efficiency savings and compliance applications of Machine Learning have huge potential for the industry Time This is still a small startup organisation Feasibility There could be data extraction issues and regulation regarding the handling of customer data • RiskGenius have written algorithms that can break down and understand an Insurance policy • This algorithm categorizes and structures the content of the policy documents so that they can be reviewed easily • The result of this is increased efficiency of sales agents who can quickly review and understand gaps or compliance issues in a policy 72
  73. 73. AI in Financial Services Prepared for EY FinTech team 5. Open Source Machine Learning: Zurich • Zurich are harnessing Machine Learning with the aim of getting to a more accurate pricing of risk, increasing the efficiency of claims processes, catching fraud more often and preventing more and more losses • “Advanced analytics is one of the top key investments for Zurich because it’s the key differentiator for insurance companies going into the next couple of decades.” — Conor Jensen, Analytics Program Director Impact It is unlikely to disrupt the industry, however Machine Learning and predictive analytics are being implemented by the big players Time The impact of this technology has not been seen yet and is still being developed internally Feasibility There is technology available however there could be regulation regarding the data usage and difficulties extracting it 73
  74. 74. AI in Financial Services Prepared for EY FinTech team 6. Automating processes: Genworth • Genworth Financial has automated the underwriting of long-term care (LTC) and life insurance applications • A fuzzy logic rules engine encodes the underwriter guidelines and an evolutionary algorithm optimizes the engine’s performance • Relying heavily on Artificial Intelligence techniques, the system has been in production since December 2002 and in 2004 completely automated the underwriting of 19 percent of the LTC applications Impact Automating processes has huge cost saving potential for this industry as well as others Time There is technology available to implement this now into organisations Feasibility The technology is entering a boom phase as underwriting is increasingly automated 74
  75. 75. AI in Financial Services Prepared for EY FinTech team Banking & Capital Markets Use Cases 75
  76. 76. AI in Financial Services Prepared for EY FinTech team 1. Fraud detection: IBM Research • IBM Research is using Machine Learning and stream computing to create virtual “data detectives” in order to detect financial fraud • The solution analyses historical transaction data to build a model that can detect fraudulent patterns. The model is then used to process and analyse a large amount of financial transactions as they happen in real time, also known as stream computing Impact This combination of ML and stream computing has the potential to disrupt the industry and others, however this application has not been taken up widely yet. Time Already in use by a large U.S bank. Results: 15% increase in fraud detection, a 50% reduction of false alarms and a 60% increase in total savings. Feasibility The model is first customised to the client’s data and then updated periodically to cover new fraud patterns. 76
  77. 77. AI in Financial Services Prepared for EY FinTech team 2. Real-time predictive analytics for credit decisioning: Logical Glue • Logical Glue is a user-friendly software platform for building and deploying predicting models. It lets businesses use data to automate decision making and increase profitability • Provides consumer and commercial credit by correctly assessing applicants’ probability of default • In 2013, Logical Glue were awarded a SMART grant to develop its platform for the creation and deployment of superior predictive analytics, making it easier than ever before to go from raw data to accurate models that can be used in real-time Impact The platform is designed to predict customer behavior for many types of markets, particularly financial lending, insurance and marketing. Time Technology is already being used. Clients include lenders, challenger banks, some of Europe’s leading insurers, a big four bank and firms in other verticals. Feasibility Implemented already by lenders and banks- no data feasibility restrictions or regulatory roadblocks at present 77
  78. 78. AI in Financial Services Prepared for EY FinTech team 3. Fraud Hub for Gaming: Feature Space • ARIC Fraud Hub understands player behaviour during real-time game play, to detect the anomalies which indicate a potential fraud attack. • Real-time Machine Learning software system for organisations in financial services, including retail banks, payment providers and card issuers as well as companies in insurance and gaming • Featurespace was created by a Cambridge University Professor and his PhD student, Dave Excell, at the forefront and confluence of two academic fields: Data Science and Computer Science Impact Resulted in 77% reduction in genuine transactions declined, 54% reduction in chargebacks and 50% reduction in operation costs Time Already used in some of the world’s largest banks, insurance companies and gaming industries such as Betfair, William Hill and KPMG. Feasibility Featurespace’s ARIC platform is live and deployed. No real obstacles 78
  79. 79. AI in Financial Services Prepared for EY FinTech team 4. The financial answer machine: Kensho • A data analytics and Machine Intelligence company • Combines latest Big Data and ML techniques to analyse how real-world events affect markets • Offers up a simple Google-style box where you can pose very complex questions in plain English • Generates original answers to 65 million questions by analysing relationships among more than 90,000 events with graphs and charts within minutes Impact Significant opportunities to apply their technology beyond financial services, with applications in government, retail, healthcare and pharmaceuticals. Time Already in use by Goldman Sachs and the CIA’s venture capital arm. Feasibility The technology development is in a boom phase by creating new business lines. 79
  80. 80. AI in Financial Services Prepared for EY FinTech team 5. The most successful hedge fund: Renaissance Technologies • New York-based hedge fund founded in 1982 by James Simons, an award-winning mathematician • Specializes in systematic trading using only quantitative models derived from mathematical and statistical analyses; employs mathematical and statistical models for high frequency trading • Renaissance is one of the first highly successful hedge funds using quantitative trading – known as “quant hedge funds” Impact The hedge fund is famed for offering one of the highest return in the hedge-fund investment world (70%) Time The technology is there; however no hedge funds or powerhouse investment banks have been able to replicate it. Feasibility This technology have been in use for the last 30 years only by the quants 80
  81. 81. AI in Financial Services Prepared for EY FinTech team Wealth & Asset Management Use Cases 81
  82. 82. AI in Financial Services Prepared for EY FinTech team 1. Natural language processing: Aylien • Aylien Text Analysis API is a package of Natural Language Processing, Information Retrieval and ML tools for extracting meaning and insight from textual and visual content with ease. • Text Analysis API: Flexible API that allows one to build ground-breaking Text Analysis solutions • News API: Search and source news and content from around the web in real time. Stay ahead of the curve by using the power of Machine Learning and NLP to understand content at scale while extracting the data that matters to clients. Impact Make it possible to for machine to understand the sentences and news. Allows many techniques and service to come true, for example, sentiment analysis Time Technology is ready and widely adopted in many scenarios. Feasibility No real obstacles 82
  83. 83. AI in Financial Services Prepared for EY FinTech team 2. Sentiment analysis: Amareos • A leader in Financial News Intelligence. Combining the unique crowd-sourced sentiment data with in-depth research, it gives clients the edge by providing them with innovative insight into the drivers of global markets. • Uses more than 50,000 global news sources, blogs, forums and social media platforms. More than 2 millions articles are analyzed daily. • Wide Variety of Sentiment Indicators Impact Makes it possible for machine to understand the feeling and attitude behind words, which was very difficult before Time Technology is ready to be implemented now Feasibility It may have regulatory issues 83
  84. 84. AI in Financial Services Prepared for EY FinTech team • Provides portfolio risk analytics solutions for asset managers, based on sophisticated ‘deep data’ agent-based algorithms scanning in real-time multiple quantitative primary data sources. • These algorithms analyse the dynamic behaviour of market participants – i.e. buyers and sellers – and cluster them based on common feature sets. Noise classification, cluster identification and behavioural finance theory are part of the unique core capabilities. 3. Clusters in real-time: AlgoDynamix Impact Identifies patterns and helps investors figure out the problems before it is too late Time Technology is ready to be implemented Feasibility Patterns and clusters are not self-explanatory, which requires expertise to do further analysis 84
  85. 85. AI in Financial Services Prepared for EY FinTech team 4. Predictive analytics: Aidyia Impact AI-based trading strategy. Those built on deep learning and neuro network may grant investors strong and unique positions in the market, which may change the whole market Time Many funds are already managed by algorithms. More may adopt in the future Feasibility Intensive optimisation and maintenance are needed • AI-driven trading strategies: using cutting edge Artificial General Intelligence (AGI) technology to identify patterns and predict price movements in global financial markets. • Result is financial prediction and trading systems with a human-like ability to not only recognise mathematical patterns in market data, but understand what these mean in a broader context. • Unbiased (No human emotional bias), Optimised (Constantly learning from the market to increase profits and lower risks), Affordable (Partially replaces experts) 85
  86. 86. AI in Financial Services Prepared for EY FinTech team Use Case Tracker 86
  87. 87. AI in Financial Services Use Case Tracker (1) 87 Company Industry Function Expertise Contacts Location Impact details Impact on industry Rainbird (bought by Mastercard) Banking To create an automated, virtual sales assistant. The AI salesperson will have the work experience gleaned from the entire sales team and the thousands of customer conversations, and predict exactly which calls might convert to sales. Predictive Analytics http://rainbird.a i/the- platform/key- features/ Worldwide Rainbird helps you actually model your business world, resulting in a repository of knowledge which is both scaleable and reusable.You can add ‘probabilistic’ rules throughout your map, enabling you to create nuanced models that cope well with uncertainty. Low Concirrus Insurance IoT applications for Insurance IoT https://concirru s.com UK IoT will impact the whole industry but Concirrus itself may not lead these applications Medium Paypal Banking PayPal uses three types of machine learning algorithms for risk management: linear, neural network, and deep learning. Online payments system Worldwide Fraud prevention will impact the entire banking industry. However, Paypal's technology is only being used internally. Medium IBM Banking By using machine learning and stream computing, IBM creates virtual "data detectives" to detect financial fraud. The technology analyzes historical transaction data to build a model that can detect fraudulent patterns. This model is then used to process and analyze a large amount of financial transactions as they happen in real time. Computer hardware company https://www.res earch.ibm.com/f oiling-financial- fraud.shtml Worldwide Fraud costs the financial industry approximately $80 billion annually U.S. IBM research can help companies save billions. A large U.S. bank used IBM machine learning technologies to analyse credit card transactions and it resulted in an increase of 15% in fraud detection, a 50% reduction of false alarms and and a 60% increase in total savings. High Prepared for EY FinTech team
  88. 88. AI in Financial Services Use Case Tracker (2) 88 Company Industry Function Expertise Contacts Location Impact details Impact on industry Atom Bank Banking Machine learning technology from the WDS Virtual Agent software tool use analytics to give customers the power to self-serve by getting immediate answers directly from the app. App-based bank http://www.ato mbank.co.uk/ United Kingdom Machine learning makes banking more personal. The machine can provide "human" touch and all customers are equal in the eyes of the machine. Medium Kensho Capital Markets The Financial Answer Machine: Combines latest big data and machine learning techniques to analyse how real-world events affect markets. Data analytics and Machine learning company https://www.ke nsho.com/#/ USA Significant cost savings by reducing the number of traders on the trading floor. This software computes answers with graphs and charts in just a few minutes which would have taken days, probably 40 man-hours, from people who were making an average of $350,000 to $500,000 a year. High Rennaissan ce Technology (RenTech) Capital Markets Most successful hedge fund using quantitative data. RenTech employs mathematical and statistical models for high frequency trading and tries to exploit market inneficiencies when large transaction takes place. Investment management firm https://www.re ntec.com/Home .action?index=tr ue USA Offering to its clients the highest return in the hedge-fund investment world - 70%. However, none of its competitors managed to come up with the same technology. High Aylien Wealth & Asset Management Text Analysis API, a package of Natural Language Processing, Information Retrieval and Machine Learning tools for extracting meaning and insight from textual and visual content with ease Natural language processing (NLP) http://aylien.co m/ Ireland Essential tools for analysing natural language and extract meaning and insight Medium Prepared for EY FinTech team
  89. 89. AI in Financial Services Use Case Tracker (3) 89 Company Industry Function Expertise Contacts Location Impact details Impact on industry Sensai Wealth & Asset Management Data Analytics (Consulting), Sensai combines artificial intelligence, open source technologies and a new business analyst friendly query language to reimagine the way the enterprise interacts with unstructured data Sentiment analysis http://sens.ai/index.ht ml USA Contributes to financial decision making based on sentiment analysis (heatmaps and data visualization etc.) with a tilt towards analytics and indicators used in trading systems and risk monitoring. Medium Sentifi Wealth & Asset Management Crowd-based financial market intelligence, the Sentifi Engine is based on artificial intelligence, machine learning and semantic methodologies. It is able to structure Sentifi Signals from millions of unstructured data shared by the Sentifi Crowd. It also identifies, profiles, benchmarks and helps engage financial market participants in the Sentifi Crowd. Sentiment analysis https://sentifi.com/ Switzerland Medium Running Alpha Wealth & Asset Management Visual summaries of global financial markets, powered by the next evolution of investor sentiment intelligence and alpha idea generation; Designed for helping investors build intelligent portfolios that know where the global influencers will be turning into next Sentiment analysis https://www.runningalp ha.com/ Canada Medium Amareos Wealth & Asset Management Visual summaries of global financial markets, Combining its unique crowd-sourced sentiment data with in-depth research, it gives its clients the edge by providing them with innovative insight into the drivers of global markets Sentiment analysis https://www.amareos.c om/ Hong Kong Medium Prepared for EY FinTech team
  90. 90. AI in Financial Services Use Case Tracker (4) 90 Company Industry Function Expertise Contacts Location Impact details Impact on industry AbleMarkets Wealth & Asset Management Real-time trading, utilizes Big Data techniques applied to market data, social media and news to help identify and manage real-time risks for investment professionals, including Execution, Portfolio Management and Risk Management Clusters in real-time http://www.ablema rkets.com/ USA It uses real-time data from markets, looks for patterns and searches for clusters of traders who are bailing out of an investment. High AlgoDynamics Wealth & Asset Management Portfolio risk analytics solutions, risk analytics engine is based on sophisticated ‘deep data’ agent-based algorithms scanning in real-time multiple quantitative primary data sources. These algorithms analyse the dynamic behaviour of market participants – i.e. buyers and sellers – and cluster them based on common feature sets. Noise classification, cluster identification and behavioural finance theory are part of the unique core capabilities Clusters in real-time http://www.algody namix.com/ UK High Aidyia Wealth & Asset Management AI-driven investment strategies, cutting edge artificial general intelligence (AGI) technology to identify patterns and predict price movements in global financial markets. AGI is a branch of artificial intelligence aimed at learning mimicking the human brain’s breadth, depth and generality of understanding. Applied to financial markets, the result is financial prediction and trading systems with a human like ability to not only recognize mathematical patterns in market data, but to understand what these patterns mean in a broader context. Predictive analytics http://www.aidyia.c om Hong Kong AI-based trading strategy and risk analysis platform. Those built on deep learning and neuro network may grant investors strong and uniquepositions in the market. High Prepared for EY FinTech team
  91. 91. AI in Financial Services Use Case Tracker (5) 91 Company Industry Function Expertise Contacts Location Impact details Impact on industry hiHedge Wealth & Asset Management AI-driven trading strategies, using deep reinforcement learning, the AI trader constantly learn and generate trading strategies to advance investment goal. Predictive analytics http://www.hihedg e.com/ Singapore AI-based trading strategy and risk analysis platform. Those built on deep learning and neuro network may grant investors strong and unique positions in the market. High FNA platfrom Wealth & Asset Management FNA Platform features a real-time graph analytics engine and an advanced client side dashboard that can be configured for a wide array of use cases within financial services. Predictive analytics http://www.fna.fi/p latform UK High Sybenetix Wealth & Asset Management Sybenetix provides multi-award winning Market Surveillance and Compliance Monitoring software. Sybenetix Compass dramatically reduces false positives whilst also increasing the speed of investigation. Conduct Monitoring http://www.sybene tix.com UK By learning each trader’s personality and being more precise in flagging up suspicious trading, its system avoids a lot of costly false alarms. High Cocoon Insurance Cocoon uses unique Subsound® technology to listen for infrasound - subtle, inaudible vibrations in the air caused by movement – as a home security device AI Subsound Technology https://cocoon.life UK High MetLife Insurance MetLife Xcelerate is a new product allowing insurance quotes in 2 minutes (instead of 20) for Home and Auto Real-time Data Analytics https://www.metlif e.co.uk UK Medium Prepared for EY FinTech team
  92. 92. AI in Financial Services Use Case Tracker (6) 92 Company Industry Function Expertise Contacts Location Impact details Impact on industry RiskGenius Insurance RiskGenius takes an insurance policy they’ve written algorithms that can break it down and understand an insurance policy Machine Learning http://riskgenius. com UK Medium Zurich Insurance Zurich are harnessing Machine Learning with the aim of getting to a more accurate pricing of risk, increasing the efficiency of claims processes, catching fraud more often and preventing more and more losses Open Source Machine Learning https://www.zuri ch.co.uk/en/pers onal UK Medium Logical Glue Banking Logical Glue is a user-friendly software platform for building and deploying predicting models. It provides consumer and commercial credit depends by correctly assessing applicants’ probability of default Real-time predictive analytics company http://www.logic alglue.com/ UK The platform is designed to predict customer behavior for many types of markets in different sectors, particularly financial lending, insurance and marketing. Medium Genworth Insurance Genworth Financial has automated the underwriting of long-term care (LTC) and life insurance applications Automating processes https://www.gen worth.com UK High Feature Space Gaming ARIC Fraud Hub understands player behaviour during real-time game play, to detect the anomalies which indicate a potential fraud attack. Featurespace’s ARIC platform is a real-time machine learning software system for organisations in financial services Fraud hub for gaming https://www.feat urespace.co.uk UK Already used in some of the world’s largest banks, insurance companies and gaming industries such as Betfair, William Hill and KPMG. High Prepared for EY FinTech team
  93. 93. AI in Financial Services Prepared for EY FinTech team List of Interviewees 93
  94. 94. AI in Financial Services Prepared for EY FinTech team List of Interviewees 94 Name Title Company Expertise Contact Information Initial contact Interview Date Jeff Hawkins CEO Numenta Biological Neural Networks N/A contacted 14/12/16 No response Dr Edgar Whitley Associate Professor The London School of Economics Information Systems e.a.whitley@lse.ac.uk N/A Unavailable Gatsby Computational Neuroscience Unit Research Centre University College London Machine Learning & theoretical Neuroscience N/A N/A N/A Jason Stockwell CEO Simply Business Insurance jason.stockwood@simplybusin ess.co.uk contacted - no reply yet 19/01/17 Dr Aysha Chaudhary Professor University College London N/A N/A N/A N/A FinTech Connect Live Conference ExCeL London UK’s largest Fintech event www.fintechconnectlive.com N/A 6/7 December Frank Gorringe Government bond trader UBS Swiss global financial services company N/A N/A No response Gah-Yi Ban Professor London Business School Big Data analytics gban@london.edu +44 (0)20 7000 8847 N/A N/A Paolo Cuomo Professional Instech London Innovation in the Insurance Industry paolo@instech.london contacted 12/12/16 22 December 2016 Sabrina Mcewen Professional Cocoon IoT/ Machine Learning in Insurance sabrina@cocoon.life Contacted – email Unavailable Eddie Litonjua Project manager University College London eduardo.litonjua.14@ucl.ac.uk contacted 13/12 14/12 Stephen H. Muggleton Professor Imperial College Machine Learning s.muggleton@imperial.ac.uk N/A N/A
  95. 95. AI in Financial Services Prepared for EY FinTech team95 Bibliography
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  97. 97. AI in Financial Services Prepared for EY FinTech team Bibliography • Burton, More. "Inside A Moneymaking Machine Like No Other". Bloomberg.com. N.p., 2016. Web. 27 Dec. 2016. • "Business Banking". Investopedia. N.p., 2016. Web. 27 Dec. 2016. • Christensen, Clayton M. (1997), The innovator's dilemma: when new technologies cause great firms to fail, Boston, Massachusetts, USA: Harvard Business School Press, ISBN 978-0-87584-585-2. • Cwalinski, Kristin. "What Is Kensho?". CNBC. Web. 27 Dec. 2016. • Deloitte,. Regtech Is The New Fintech. 2016. Print. • euromoneythoughtleadership, (2016). GHOSTS IN THE MACHINE: Articial intelligence, risks and regulation in financial markets. [online] Available at: http://www.euromoneythoughtleadership.com/ghostsinthemachine • Fintech, D. (2017). AI in Digital Wealth Management: Sniffing out investment opportunities | Bank Innovation. [online] Bankinnovation.net. Available at: http://bankinnovation.net/2016/03/ai-in-digital-wealth-mgt-sniffing-out-investment-opportunities/ • "Forbes Welcome". Forbes.com. N.p., 2015. Web. 27 Dec. 2016. • Fortune.com. (2017). [online] Available at: http://fortune.com/2016/06/27/five-hottest-fintechs/ • Gary Richardson, KPMG. 2015. Transforming the insurance sector: How machines will change the game for insurers. Available at: http://www.kpmg.com/Global/en/IssuesAndInsights/ArticlesPublications/Frontiers-in-Finance/Documents/how-machine-learning-fs.pdf • Gorsht, Reuven (2015): When Machines Replace Middle Management; Forbes BrandVoice. Available at: http://www.forbes.com/sites/sap/2015/04/12/when-machines-replace-middle-management/#56d6283e63db 97
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