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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
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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
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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)
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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
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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
!
!
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But how do we create value in our industry? Big Data: Volume, Variety, Velocity (HBR, Oct 2012)
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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
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Modelling in the digital edge
Common Sense Market Practice and industry’s peers informal feedback
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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
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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
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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
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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…