book

The AI Book

by Susanne Chishti, Ivana Bartoletti, Anne Leslie, Shân M. Millie

June 2020

Intermediate to advanced

304 pages

10h 40m

English

Wiley

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The Promise of Deep LearningBusiness Applications in FinanceTime for a Reality CheckSafeguards and Systemic Risk
1. Be Narrow Minded2. Weigh the Risk3. Get the “Last Mile” Right4. Consider That Less Data May Mean More5. Do Your HomeworkNote
Challenge 1: A House Built on SandChallenge 2: The Digital Transformation DilemmaOpportunity 1: Share Your Data with the WorldOpportunity 2: The Alternative Data RevolutionA Bright FutureNotes
Making Sense out of Complex Text through Natural Language Processing (NLP)Ontologies Link Entities and Thus Create Valuable ConnectionsHow Augmented Human Intelligence Will Change the Way We Read News and Inform OurselvesNote
DataResearch FailureBiasExplainabilityEmotionEthicsOrganizational ReadinessConclusion

The Future of Artificial IntelligenceReinventing How We InventAI Neural NetworkThe Human API and Digital War: World War III
Notes
BackdropEnhanced Cognition: The Good NewsMacro-Trend Analysis of Workforce ChallengesPragmatic Suggestions for a Way ForwardConclusionNotes
The Art and Science of Board DynamicsThe Challenge of AI for BoardsWork with Strength-Based Management TechniquesDesign and Provide Special Educational Programmes for Board MembersDare to Change the Composition of the BoardEstablish an AI CouncilCreate a Communication CampaignNote
OverviewUser IdentificationCredit DecisionsFraud PreventionConsumer Lending: Proprietary Risk Management Based on Big DataCredit Decision Powered by Knowledge GraphSME Lending: Unsecured Loans Backed by AI and Machine LearningChatbots Used in Debt Collection
Value StoresFuture of DepositsAccess to CreditChanging ExpectationsData DelugeFuture of Lending
Alternative DataValue ChainOrigination and OnboardingUnderwritingFinancing and ContractServicing and Payment CollectionsImagine!How Much Data Is Enough?How Much Information Do You Store About Your Customers?Is What You Are Doing Transparent, Ethical and Fair?Last Word
Brief HistoryThe Expansion of ChatbotsBanking ApplicationsNon-Banking ServicesMoney MattersInformation and Banking OperationsFinancial CoachingConclusionNote
Identifying More Creditworthy SMEsSpeed Is of the EssenceProblem Solving, Sector by SectorThe Power of AI to Shift CapitalNote
Note
The ProblemHow Do We Bridge the Gap?
Two Significant Challenges: Financial Exclusion and CybercrimeThe solutions widely used in African FinTech ecosystemAI at the Heart of Innovation FinTech SolutionsThe Use of ChatbotsLooking AheadNotes
Insurance: Too Important Not to Lead on AI?Your Job as Leaders (1): Get the Reality CheckYour Job as Leaders (2): Question the “Absolutes”Your Job as Leaders (3): Help the Firm Get PurposefulYour Job as Leaders (4): Bring AI Ethics to the BoardroomFinal Thoughts: Architects of Exclusion — or Enablers of Protection?VERY Selected Further ReadingNotes
AI and Insurance – A Natural PartnershipInsurance turning AlgocraticRegulating the Algocracy
What’s the Problem with AI in Health Insurance?AI Use Cases That Can Move the NeedleLevel 1 – Standard AI SolutionsLevel 2 – Tailor-Made AI SolutionsLevel 3 – Explainable AI SolutionsHow to Get There – Strategic ImperativesDefine Clear Use Cases and Demystify the Topic of AIIntelligently Integrate Partners Instead of Developing Everything InternallyFocus on Solving Real Business ProblemsValue Patient Centricity and Gain Patients’ Trust by Ensuring Transparency
AI and Healthcare NowThe Economic OpportunityWhat Is the Role of Health Insurers in Emerging Healthcare AI Business Models?Notes
Why AI, Why Now?A Deluge of Data, A Drought of InsightsUse Cases for AI in Commercial Insurance UnderwritingThe Rise of the Digitally-Enabled UnderwriterWe Are Still at the BeginningNotes
AI-Supported Policy Life Cycle Management: Point of SaleRe-Scoring and Re-Evaluating the Initial Application after a Claim Has Been SubmittedIt’s Not Just about Fraud
ProductsProduct ManagementCustomer OnboardingUnderwritingCustomer ServicesClaims and Settlement Management
Enabling and Applying AIHistory vs PresentComputer VisionVoice and NLPInternet of ThingsConclusionNotes
Today’s Security Paradigms Will Not Suffice TomorrowInvisible, Precise, Highly Robust AuthenticationRethinking AuthenticationNote
Payments, a Wealth of DataA Tool to Combat FraudSmart RoutingGetting to Know Your CustomerAdvanced Analytics for Merchants
Towards Invisible Banking and PaymentsNotes
The Cost to the EconomyHow Do Current Practices Inflate Consumer Prices? An IllustrationIntroducing 2DVVIBut It’s Not Quite so SimpleThe Social DimensionNotes
ML in Wealth ManagementProspecting and Conversion, Onboarding and ScreeningClient Onboarding and ScreeningProduct Recommendations and OnboardingAdvisory ProcessInvestment Research and TradingClient AttritionSummary of Different Algorithms and Use Cases for Wealth ManagementData Sharing and ConfidentialityFederated Learning
IntroductionThe Impact of AI on ESGMastering the Data Complexity Challenge with AIEngaging The Investor Community to Address AI ConcernsCollaboration and EngagementConclusion
Inherent Issues in IndiaAI in Investment Management in IndiaSome New ScenariosEmergence of New Business ModelsReference
Random Walk Through Efficient Markets: Are Stock Price Fluctuations Predictable?Bulls, Bears and Butterflies: Markets as Chaotic SystemsBest of Both Worlds: Investing With AI-Driven Decision Enhancement ToolsPredictive Algorithm Developed by I Know First
Data-Based AutomationFront, Middle and/or Back Office?Implementation StrategyImplicationsNotes
Man vs MachineQuantum ComputingConvergence of Advanced TechnologiesAutomated TradingWealth Creation by AlgorithmThe Financial World of Equals
Setting the SceneWhat Is Artificial Intelligence?Using Data Science to Solve Business ProblemsCapital Markets Use CasesTrust, Transparency, and Human InteractionsState of the Art: Selected Highlights 2018/19Where Next?Notes
Defining Artificial IntelligenceMachine Learning (ML) and Deep Learning (DL) within FinanceBarriers — and the Goldilocks RuleNotes
Simple Heuristics Lead Human BehavioursPrevention through DeterrenceData and AI StrategyIntelligent EmpowermentCompliance and Business Benefits?Further readingNotes
Back to Basics: What Is Alt-Data?Redefining Market Players – The MetaQuant ApproachCan a Hybrid Model (Quantamental + MetaQuant) Boost Investment Results?Notes
Organizational Efficiency — Inside and OutRegulatory DevelopmentsFuture Enablers
Tech That Has Legitimacy with a Social LicenceEthical Innovation in FinanceClarity of Ethical Purpose and Mission Is CentralRegulation Introduced Clarity and Wider Support for InnovationFour Ways to Support More Trustworthy, Ethical Innovation in the Financial Services Sector
Ethical Challenges for FirmsEthical GovernanceConclusionNotes
Independent Human Research Review Committees (IHRCs)World’s First Corporate AI-Focused IHRCsAxonCorporate ESG Reporting on AI as a New Paradigm?Notes
Explainability and TransparencyThe FutureWisdom
FairnessPrivacyTransparencyExplainabilityAccountabilityConclusionNotes
What Is Intelligent Empowerment and Why Is it Topical?The FS AI/ML Trust IssueTETE ProposalThe TETE Need and ChallengesHow to Implement a TETE FrameworkConclusionBibliography
The Challenges Hindering Wider AI ImplementationHow to Identify an Explainable AlgorithmUnlocking a New Level of Explainability with Prescriptive AIIndustry Use Cases and Compelling Results
Why Do We Need Data Ethics Now?Treating Data as the AssetConsequences of Data Mistrust
The Client Data PromiseExample from the Wealth Management WorldNotes
Sci-Fi — or Real Life?Racial Bias — the Tip of the Iceberg?Legal and Ethical Issues for Your Watch ListThe GDPR – a Deeper Dive on Key Data PrinciplesSelf-Regulation – A Viable Strategy?Notes
The Need for RegulationCommon Technical StandardsRegulatory MeasuresQuestions of LiabilityFuture RegulationNote
Notes
Why We Need Compliance and AuditIdentification of RisksA Vision for TomorrowConclusion
Setting the Scene – The WhyMapping out the RegTech and Legal Risk AI Landscape (The What)Key Building Blocks of AI Solutions Addressing Legal Risk and Regulation (The How)Judgement and LiabilityNotes
Notes
Evolution of RegTechRegTech 3.0: Phases of DevelopmentConclusion
The Evolving Technology LandscapeBeyond Digital Transformation: Thinking Like a Digital NativeInnovation at the Speed of ThoughtA Potentially Utopian Future
Job DisplacementBetting the House on AIThe Spending ConundrumIntelligence, Employment and Social PurposeConclusionNotes
GenesisData Marketplace for the PeopleThe InversionConclusion
IntroductionShortage of DataLack of Trust in AIShortage of Qualified PersonnelFederated LearningNotes
What Do Regulators Do?Endless Financial Crises……That Are Getting WorseAutonomous Regulatory Agents to the RescueWe Created This Mess – We Can Fix It!
Natural DisastersCapital MarketsAI for Diversity and InclusionConclusionNotes
Natural Language UnderstandingMulti-LanguageHuman Personality and Emotional UnderstandingSupport Process OptimizationSales Process OptimizationIs China Coming?Looking Forward
The AI-Prediction Debate: Technology Anxiety and AI, Is This Time Different?But, Will AI Eliminate All Jobs in the Future?From the Future of Work Debate to the Wealth Distribution and Inequality ProblemIs Our Future Preordained by AI Prediction Capabilities?The AI-in-Practice Challenge: Narrow AI and Its Implementation ChallengeAI and Human Strengths and WeaknessesThe Need for an AI–Human Collaboration ApproachNotes
The New NormalA New Type of LeadershipWorkforce DisruptionFocusing on Soft SkillsReskilling and New Skilling to Maintain Relevance
COMPASPredPolGender BiasMorality in the Context of Self-Aware AIAI in the Service of SocietyOpen Questions
2030: Birth2050: Being Renamed “Talan Uring”2055: Learning to Relate and Feel2056: Breakthrough in Neuron Manipulation2060: Genetic Algorithms Entered My Life, I Became an “EA”2061: Humans Tried to Catch up through Thought-Powered Trading2065: I Started to Apply GANs2067: I Rebuilt My Own Hardware2070: Market Super-Intelligence, the World Model2072: Human Traders Sought Justice2075: I Extended My “World Model” beyond Planet Earth2080: I Became an Algorithm Analysing the Work of Other Algorithms2090: Discovery of the Namuh Civilization2095: My Rediscovery of Humankind

Content preview from The AI Book

CHAPTER 66Open Banking, Blockchain and AI: The Building Blocks for Web 3.0

By Cesar J. Richardson¹

¹General Manager Americas, Strands Finance

We produce more data now than ever before, and a valuable stream of behavioural insights resides in our raw transactional data. So far banks, like many large internet companies, have held a firm grip on this data and its derived insights, claiming it as their intellectual property.

With the emergence of open banking the tide is turning, and consumers will soon have the unprecedented choice to share this transactional data with third parties who, in turn, will leverage this information and create new value-added experiences for the original consumer.

The question this raises is what is the optimum system and protocol required to guarantee the success of open data? How can we engineer a solution that is designed to truly democratize the value generated by our data and benefit humanity as a whole? While tech giants today have a firm grip on open application ecosystems, tomorrow we can establish free and open access to information by leveraging crypto technologies and building a new internet architecture, or Web 3.0, with significant positive outcomes for future generations.

Genesis

I first heard of “The Netflix Prize” back in 2010 and realized the tremendous value locked in raw data. This open competition allowed anyone to access a training data set of over 100 million ratings on almost half a million users, with the aim of challenging ...