The future of FinTech product using pervasive Machine Learning automation - André Balleyguier (DataRobot)

Shift Money
Nov 26th 2018
The future of FinTech using
pervasive Machine Learning automation
André Balleyguier - Chief Data Scientist EMEA at DataRobot

Confidential. ©2018 DataRobot, Inc. – All rights reserved
About me...
+
Chief Data Scientist EMEA
London
Previously...

Agenda
1. A story about Transformation
2. Machine Learning in Financial services
Applications of Machine Learning today in the FinTech space
3. The curse of ML: Scalability
Main challenges to scale the use of Machine Learning
4. Automated Machine Learning in FinTech
Leveraging highly automated and pervasive machine learning systems
to optimise all business lines

Machine Learning: A story
about Transformation1)

A story about my grandmother Germaine...

Letters from friends to the
family?

Does this look familiar?

So many ways to capture this information...
● Public data sources
● No response to other
communication channels
● No activity for years…

So many ways to capture this information...
● Public data sources
● No response to other
communication channels
● No activity for years…
Fuzzy matching of names to
public data sources
Predict customer propensity
to respond
Predict customer dormancy
Machine Learning

Machine Learning: A story about transformation
Machine Learning and automated decisions based on data are
reshaping the way businesses operate and interact with their
customers.
● Automation of processes that usually require humain judgement: Fraud,
Compliance, Cash management, Screening of candidates, …
● Optimisation of customer interactions based on profile
● Efficiently measure risk taken by various activities like lending, etc.

Confidential | Copyright © DataRobot, Inc. | All Rights Reserved
“
Those who rule data will
rule the entire world.
”
M A S A Y O S H I S O N
CEO | Softbank

The impending AI divide
AI and ML will generate $2.9 TRILLION in
business value and recover 6.2 BILLION hours of
worker productivity by 2021.
- Gartner Predictions (Forbes) -

The Digital champions
● Heavy focus on digitization of consumer
● Investing a lot in AI
● Data-driven and innovative culture

Machine Learning in
Financial Services2)

Demystifying the buzzwords
Artificial
Intelligence
Data
Science
Statistical
Modeling
Machine
Learning
Deep
Learning
Machine Learning: Learning from the past to predict the future
AI: Systems able to perform tasks that ordinarily require human intelligence

The value of AI today in the enterprise
Machine
Learning Deep
Learning
$ $ $ $ $ $
$ $ $ $ $ $
$ $ $ $ $ $
$ Automate $ Optimize $ Produce Actionable Insights
Key: [Boring Stuff] [Deep Learning] [Machine Learning]

All aspects of financial services are impacted
Upsell/Cross-sell Customer loyalty, retention
CUSTOMER JOURNEY / 360
Pricing analytics Churn reduction
OPERATIONS
Loan review
COMPLIANCE HUB
Loss forecasting/
Stress Testing
PRODUCT & SERVICE EFFICIENCIES
Investment research and client
targeting
Portfolio analytics and
robo-advising
REVENUE GENERATION
Prospecting Marketing
RISK / FRAUD
Delinquent asset resolution Credit and prepayment risk
Model Governance
and Validation
KYC/AML
Underwriting
Succession
planning
Fraud reduction (ATM, card, merchant services)

What are key use cases for...
DIGITAL LENDING PLATFORMS
Lending Risk scoring
Application fraud detection
Product recommendation
Customer targeting
PAYMENT PLATFORMS
Transaction fraud
Anti-Money Laundering / KYC
Customer complaint resolution
Crypto recommendation
INSURTECH
Underwriting automation
Dynamic Pricing
Fast-track Claims handling
Claims Fraud or Litigation detection
Renewal optimisation
CHALLENGER BANKS
New client targeting
Anti-Money Laundering / KYC
Customer Service Automation
Product Cross-sell
Credit Risk
Transaction Fraud
Investment / Robo-advisors

Bottom Line: The 3 pillars of ML value
1. Customize Financial Products to clients
and improve Customer experience, interest and retention
2. Provide access to services
for Customers who usually don’t have access to them
3. Improve Operational Efficiency:
Faster/Automated underwriting, Easier Compliance, More efficient Fraud detection,
Optimised Customer Support

Recent stories...

The curse of ML:
Scalability
3)

Recent stories...
“When it comes to data science at Monzo, we have a lot more ideas than we can
practically implement. In particular, with machine learning there are many promising
business applications but it’s prohibitively time consuming to test them all,
especially as a small two-person team.”
Dimitri Masin, Head of Data and Analytics

The curse of ML
“80% of Data Science projects never
go to production!”
Some (most) of my prospects
“We mainly build prototypes”
Scalability

Why? Let’s go back to basics
Discovery &
Problem
definition
Prepare Data
Develop
model
Socialise the
model with the
business
Operationalise
In theory, a Machine Learning project is a simple iterative flow:

The Data Scientist: A rare unicorn
1. Knowledge of the business and business problem
2. Knowledge of the data
3. Ability to write code to gather data
4. Ability to write code to explore/inspect data
5. Ability to write code to manipulate data
6. Ability to write code to extract actionable items
7. Ability to write code to build models
8. Ability to write code to implement models
9. Foundational statistics
10. Internals of algorithms
11. Practical knowledge and experience
12. Knowing how to interpret and explain models

Machine Learning: So many things could go wrong!
Discovery &
Problem
definition
Prepare Data
Develop
model
Socialise the
model with the
business
Operationalise
(1) This requires heavy business
input, but a lot of data science
teams work in silos
(2) Data prep can be very time
consuming: i.e put the data in the
right shape for the algorithms (3) This requires a data scientist /
engineer expert and can be hard

Main bottlenecks in Machine Learning
Demand for machine learning & AI
Data scientists in the world
2010 2012 2014 2016 2018 2020 2022 20242008
● Exponential demand in all sectors, including FinTech
● Experts are hard to find and retain
● Machine is a long and complicated process
Our answer: Automated Machine Learning

Automated Machine
Learning for the Future of
FinTech
4)

Automated Machine Learning
Open source technologies
Years of experience of
top-ranked data scientists
Accelerate the process of researching, testing,
and deploying Machine Learning models through
automation and enabling a wider set of users.
“Democratize” Data Science!

Automated Machine Learning: more focused on
the business expertise
1. Knowledge of the business and
business problem
2. Knowledge of the data
3. Small amount of pragmatic education
and mentoring.

How does automation improve the process?
Discovery
& Problem
definition
Prepare
Data
Develop
model
Socialise
the model
with the
business
Operationalise
(1) The process requires heavy business input, but a lot of data science teams work in silos
=> Democratise Data Science to business users through automation and education
(2) Data prep can be very time consuming: i.e put the data in the right shape for the algorithms
=> Automation can automate some parts of the data preparation, also ensuring the
process is more iterative, hence the data prep more focused on actual business value
(3) Model development and deployment require a data scientist expert and can be hard
=> Automation allows to simplify the Machine Learning lifecycle and make it more accessible

Automation addresses the skills challenge
Demand for machine learning & AI
Data scientists in the world
2010 2012 2014 2016 2018 2020 2022 20242008
● Make Data Scientists more productive and closer to the business
● Enable Business Analysts to leverage ML
● Simplify the entire Machine Learning process
Data scientists + Business analysts leveraging
Automated ML

Pervasive Machine Learning for greater
customer experience and products
Targeting
Right product,
Right time
Tailored messaging
Fair price
Fast registration
process
Fair risk assessment
Automated compliance / KYC
Efficient support service
Automated routing
Chatbots
Cross-sell relevant
products
Personalised offers
recommendations
Personalised Robo-advisor
Trading strategies
Action recommendations
Security
Compliance
Forecasting
Fraud
Attrition and
CV screening
=> 100’s of models to build and maintain
=> Requires automation!

The world’s most advanced Automated Machine Learning platform
INSURANCE HEALTHCARE BANKING & FINTECH CYBER-SECURITY AND MORE
200+
800,000,000+
Models built on
DataRobot cloud
#1 ranked
Data scientists
4 50+Top 3 finishes
Data scientists &
Engineers (of 500+)
$225M+
In funding
2012Founded
HQ in Boston, MA

The future of FinTech product using pervasive Machine Learning automation - André Balleyguier (DataRobot)

The future of FinTech product using pervasive Machine Learning automation - André Balleyguier (DataRobot)

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