ARTIFICIAL INTELLIGENCE IN THE FINANCIAL SECTOR

Azeem Azhar, VP Venture & Foresight Schibsted Media Group

@azeem http://azeem.io/s

Only three ways for humans to get smarter

Evolution Genetic engineering Artificial intelligence

Inspired by Doug Lenat

3

Across the uncanny valley

AI is an umbrella term

AI

Machine learning

Deep learning

NLP

Predictive analytics

Rule-based systems

The BLUF

Better, cheaper predictions Risk management, trading, advising, KYC, credit assessments

Automation of human tasks Customer service, advice, interfaces, back office process

Product innovation New products and markets will open up

WHAT NEW SOURCE OF ALPHA

Judgment

Experience, intimacy

Agility or monopoly

Algorithmic improvementDQN DDQN PRIORITISED DUELLING

IMPROVEMENT OVER 12 MONTHS

Miles Brundage

Better at video games 7

Good as human: captioning 8

Complex Tasks: Image Caption Generation

http://arxiv.org/abs/1411.4555 “Show and Tell: A Neural Image Caption Generator”

9

Neural machine translation

Major global internet companies release internal machine learning & AI frameworks

G, M, A & F: High level MLaas & AIaas

Majors move into open source

11

Can I compete?

AI favours the large

$$$$training cost

Improves with scale(data, layers, parameters)

Hiring the right talent

Artificial Intelligence Explodes: New Deal Activity Record For AI Startups

The AI lock-in loop

Inspired by Barney Pell

FinTech is booming with UK in the lead

This isn’t new

41 startups bringing artificial intelligence to Fintech

In Q3 2016 VC-backed finch companies raised

$2.4B across 178 deals

Overall fintech investment reached $2.9B

BENEFITSFaster actions and decisions (e.g. for fraud detection)

Better outcomes (e.g. for portfolio optimisation)

Greater efficiency (i.e. better use of highly skilled people or expensive equipment)

Lower costs (e.g. reducing labour costs with automated telephone customer service)

Greater scale (i.e. performing large-scale tasks impractical to perform manually)

Product and service innovation (from adding new features to creating entirely new products)

CHALLENGES

AUTOMATION COMPETITION CASCADING FAILURE

“I suspect that it’s going to transform all aspects of the financial industry”

Andrew Lo, Director of the Laboratory for Financial Engineering at the MIT Sloan School of Management

Euromoney Institutional Investor Thought Leadership survey of 424 senior executives from financial institutions around the world

Within 15 years, 68% expect to see complete or substantial change to their own

jobs.4/10 fear it will have a negative effect on the structure of the workforce sooner – within the next three years.

Euromoney Institutional Investor Thought Leadership survey of 424 senior executives from financial institutions around the world

LOAN ASSESSMENTRISK MANAGEMENT

UP SELL/CROSS SELLFRAUD DETECTION

TRADINGKYC/AML

USE CASES

Algorithmic trading systems now handle

75 % of the volume of global trades worldwide

TRADING

“In five to ten years, half of all financial advisors will be gone”Ric Edelman, Edelman Financial Services, top financial advisor in US

Identifies and executes trades entirely on its own

AI that runs across on thousands of machines

Uses evolutionary computation and deep learning

Automatically recognizes changes in the market and adapts

HEDGE FUNDS: AIDYIA

Numerai: collective artificial intelligence to make stock market predictions.

Data scientists submitted over 12 billion equity price predictions in less than a year

STOCK MARKET PREDICTIONS

BIOMETRIC BANKING

SANTANDER: VOICE PRINTMASTERCARD: SELFIE IDHSBC: FINGERPRINT

Robots as customer service in retail banks

Human AI chat bots e.g. Cleo

Challenges - opportunity identification & ethical execution

How to identify opportunities

How to build capabilities & culture

How to manage business model challenges

How to deal with humans

How to manage risk in the face of systemic cascades

@azeem

Thank you.

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