Big Data in the consumer finance industry vision, myths & opportunities Iuri Cardoso Paixao / Khalid Saâd Zaghloul

Big Data : Modeling, Estimation & Selection Ecole Centrale Lille

June 9th, 2016

Big Data - the 4 V : nothing new… but with more

Volume Today : Petabyte

Tomorrow : Exabyte

Va

riety

F

rom

stru

ctu

red

da

ta to

un

stru

ctu

red

da

ta

Velocity Real-time data collection

Real time data modeling

Va

lue

Da

ta

va

loriz

atio

n

Fo

r the

bu

sin

es

s

Cloud

Big Data

HPA

Data Scientist

Dataviz

It seems today that the volume, the variety and the velocity of data are already assumed to be a reality

McKinsey Global Institute, 2011

Big Data: Volume, Variety, Velocity (HBR, Oct 2012)

A huge potential mainly in the financial sector such as consumer finance as well

!

How to take advantage of analytical levers and how to overcome barriers? That's the question!

• A/B Testing • Association Rule Learning • Classification • Clustering Analysis • Crowdsourcing • Data Fusion / Integration • Data Mining • Ensemble Learning • Genetic Algorithms • Machine Learning • NPL • Neural Networks • Network Analysis • Regression • Predictive Model • Others

A vast set of "analytical" tools available (more than 26 different have already been

mapped)…

…but how to use them and show benefits seems still to be the question

Big Data: Volume, Variety, Velocity (HBR, Oct 2012) A recognized analytical potential available

!

Source: INSEAD eLAB, 2014 McKinsey Global Institute, 2011

Source: INSEAD eLAB, 2014 Source: INSEAD eLAB, 2014

But how do we create value in our industry? Big Data: Volume, Variety, Velocity (HBR, Oct 2012)

Satisfaction with ROI Main types of data analyze

!

!

But how do we create value in our industry? Big Data: Volume, Variety, Velocity (HBR, Oct 2012)

The new age of information

Analytics

"era"Example

Type

of data

Type

of analysisTools

1.0client profitability

demand forecastdiscrete, structured, slow

descriptive, diagnostic-

statistical

BI

online analytical processing

(OLAP)

DW

2.0client behavior analysis

intelligent pricing

Big data:

structured and unstructured,

high velocity, high complexity,

high volume

predictive , prognostic and

advanced data science

Above +

Hadoop®

etc…

3.0Machine-to-machine system

optimization

ubiquitous sources of big data:

anything with IP address is a

source, and sensors add volume

and variety

prescriptive,

embedded/invisible, heavy use

of machine learning

Above +

columnar databases (DBs)

graph DBs

etc…

source : Tom Davenport, Big Data at Work, 2013

Regulation Open Data Robot-advisors DSP2 IoT

Our challenge : how to automatize more and to simplify more… in a more sophisticated industry and a more complex environment ?

Data Privacy

Modelling in the digital edge

Common Sense Market Practice and industry’s peers informal feedback

Industry General Scheme Credit Decision

The credit process is based mainly on decision engine & data Efficiency KPIs : automation / acceptance / time to cash / productivity / risk / etc… Trends : more automation (immediate decisionning) / more data / more data science

Collection

Credit Decision Process at the cutting edge

Project Customer Information

Account checkout

Real time decision

Simplification Customization Direct customer data collected

lightening Automation Dynamic process Segmented process Embedded process

source : EFMA – Mc Kinsey : Digital transformation in 10 building blocks - 2012

11

Data typology & use

Strong impact of data privacy regulation

Socio-

economics

Risk

behaviorTransactions Web on-line Negative CB Positive CB Partners

Public

databasesWeb off-line Localization Social nets Others

Customer discovery

Customer needs

KYC

Reimbursement capacity

Exposure

Credit decisioning

Collection

Litigation

Internal data External dataOperational use

data currenly used data on-going survey

Common Sense Market Practice and industry’s peers informal feedback

Big Data in the consumer finance industry : POC

What is a Proof of Concepts (POC) ?

► POC = prototype Why ?

► Behind sales or buzz communications, need to estimate the real impact & benefits.

How ?

► Problem definition : client. ► Budget : client. ► Data : client. ► Solution : provider (FinTech, Lab, external company, internal team). ► Performance criteria : client.

Some examples :

► Customer localization using Big Data techniques. ► External customer behavior for better risk management. ► Machine learning for granting process & collection process. ► Dynamic modeling for digital business. ► etc…