Blockchain & AI in Fintech & Insurtech

216 Pages·2017·8.44 MB·English

Blockchain & AI in Fintech & Insurtech:
A Practical Guide to Navigating the Landscape
Paul Schulte, MA, MALD
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[email protected]
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a www.schulte-research.com
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s ([phone]
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e Gavin Liu, CFA – Senior Analyst
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Table of Contents
1. How Us Frail Humans Can Make Peace w/AI
2. A New Fin Tech Creature from the Black Lagoon
3. New business model for bank & AI: case study – Ping An
4. Intro to blockchain, why banks lost and how to fix it
5. BLOCKCHAIN: RIP for SWIFT & HTTP?
6. Can Big Banks, Insurance and Telecom Make a Comeback in Fintech?
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Part 1. How Us Frail Humans Can Make Peace w/AI:
Ditch the Lizard Brain & Embrace Algorithms.
Lizard:4 F’s. Feed, Fight, Fornicate, Freeze. Driven by
emotional memory & fear of humiliation & exclusion.
“The disposition of everyone on all sides was not to prepare for the
harder alternative, not to act upon what they suspected to be true.”
-- Barbara W. Tuchman
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Cognitive Bias: I MUST BELONG. I can’t fail. Polite is good. Boss knows better.
Yesterday predicts tomorrow. Data confirms my view.
Group think
Fear of Authority
Ambiguity
Group Attribution Survivorship Courtesy
Availability Cascade Authority Fallacy Not Invented Here
Status Quo
Functional Fixedness Fundamental Attribution Self-serving
Social Comparison
Lack of Imagination Hyper-Rational
Backfire CongruenceNormalcy Anchoring Illusory Correlation
Confirmation Fallacy
Mere Exposure Conservatism Law of the Instrument
Overconfidence
Optimism
Empathy Gap
Shared Information
Halo
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AI: tireless, rude, in your face, needs no friends, constantly
evolving, fearless, full field of vision, independent, logical.
A. Self-Learning
Reinforcement Learning
Deep Learning Transfer Learning
Decision-tree learning
B. AI Tools
Linear Classifiers
Dimensionality Reduction
Clustering
Regression Analysis
Automated data correlation
Instance-based learning
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Foundation for human frailty: Immediate Desire creates perception
– desire for $, pleasing, pleasure, pals, perspective, predictability.
Group think Fear of Authority
Ambiguity Survivorship Courtesy
Group Attribution
Authority Fallacy Not Invented Here
Availability Cascade
Functional Fixedness Status Quo Fundamental Attribution Self-serving
Social Comparison
Lack of Imagination
Hyper-Rational
Backfire Congruence Normalcy Anchoring Illusory Correlation
Confirmation Fallacy
Conservatism Law of the Instrument
Mere Exposure
Overconfidence
Optimism
Empathy Gap Halo
Shared Information
Objective: needs Mass Computing: AI does not Never tires: Always Evolving:
no pleasure full field of vision want to belong good judgment rude & fearless
A. Self-Learning
Deep Learning Transfer Learning
Reinforcement Learning
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s Clustering B. AI Tools
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Dimensionality Reduction Decision-tree learning
Linear Classifiers
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u Regression Analysis
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Types of bias/defects
Types of bias Negative Effect Outcome
1. Group think is dominant Fear of Judgement We quit
2. Authority is dominant Fear of Failure We hide
3. Imagination is dominant Fear of Unknown We defer
4. Rationality is dominant Fear of the Irrational We freeze
Why? We:
1) depend on the group;
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2) tire easily;
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3) want to be liked;
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5) love pleasure
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I. Cognitive Bias (human blind spots)
Humans need AI as a recall for our defects
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Summary
I. Group think is dominant -AI CrowdSourced Data II. Fear of Authority is dominant -MassData to Check Assumptions
1 Ambiguity Fallacy 6 Authority Fallacy
2 Availability Cascade 7 Courtesy Fallacy
3 Functional Fixedness 8 Survivorship
4 Group Attribution 9 Fundamental Attribution
5 Status Quo 10 Not Invented Here
11 Self-serving
12 Social Comparison
III. Lack of Imagination is dominant -AI for Alternative Scenarios IV. Excess Rationality is dominant -Sorting of MassDat
13 Backfire Fallacy 20 Anchoring Fallacy
14 Confirmation Fallacy 21 Conservatism Fallacy
15 Congruence Fallacy 22 Empathy Gap
16 Overconfidence 23 Halo Fallacy
17 Mere Exposure 24 Illusory Correlation
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18 Normalcy Problem 25 Law of the Instrument
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e 19 Shared Information 26 Optimism
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27 Risk/Compensation Bias
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