big data in banking - firstrand · pdf fileubs 19th annual financial services conference: the...

UBS 19th Annual Financial Services Conference: The technology frontier

Big Data in banking

UBS 19th Annual Financial Services Conference | 13 October 2016 | slide no. 2

Content

Modern Credit, Marketing, Financial Crime, CVM and Operations

Big data enablers

The evolution of Retail and Commercial Banking

Key challenges and what’s next


Content


Big data enablers




The evolution of Retail/Commercial Banking

Branch Online

Product Customer

Automated Scoring

Judgmental


Evolution from branch to e-channels Number of interactions Millions

0

500

1 000

1 500

2 000

2 500

3 000

3 500

4 000

4 500

2010 2011 2012 2013 2014 2015 2016

Online Mobile App ATM POS Branch


Evolution – from judgemental to automated credit

0

10

20

30

40

50

60

70

80

90

100

1999 2001 2003 2005 2007 2009 2011 2013 2015

Judgemental decisions Accuracy

% Gini for

‘banked’ customers,

non-banked much lower


Evolution from product centric to customer centric view

99.5 95

Customers on CIS* (%)

Individual Juristic

200 220

260 300

375

2012 2013 2014 2015 2016

Number of customer documents on Documentum** (Million)

* Customer Information System ** Central electronic document repository

Customer information management has been

centralised outside of product systems

Customer and product records have been centralised

outside of product systems


Content


Big Data enablers




Big Data enablers

Big Data

Comprehensive single view of

customer

Enterprise data warehouse/lake

Analytical capability

Operational decision making


Comprehensive single view of customer

FirstRand Citizen

UBS 19th Annual Financial Services Conference | 13 October 2016 | slide no. 11 First National Bank – a division of FirstRand Bank Limited. An Authorised Financial Services and Credit Provider (NCRCP20).

Risk Marketing & Sales

Financial Crime CVM Operations

Enterprise Data Warehouse/Lake


More sophisticated analytical capabilities required to extract value

EXPERT SYSTEMS SIMPLE STATISTICAL MODELS

MORE ADVANCED METHODS

Logistic Regressions Decision Trees Neural Networks

Random Forest

Σ


Operationalising decisions Br

anch

Onl

ine

App

Mob

ile

ATM

Integration layer

CIS

IDS

ILP

CAM

S Channels

Customer and Product Systems DATA STREAMS

Actions

Decisions

Estimates

“Analytics without decisions is meaningless”

Ability to deliver Big Data Analytical Decisions

into production systems is key


Content


Big Data enablers




Big Data use cases

Risk Financial crime CVM Operations Marketing

and Sales


Big Data use cases - Risk

•  Enhanced Credit Models

•  Integrated view across products, channels, enablers

•  Sophisticated behavioural analysis

•  Augmented/Derived characteristics

•  Risk appetite – optimised at every level

•  ICAAP models

•  Regulatory Risk/Compliance & Market Conduct

•  Insurance

Risk


Big Data use cases – Marketing and Sales

•  Facilitated by integrated view of customer across products/

accounts/ transactions/ channels and external data

•  Needs-based modelling across borrow, transact, deposit and

insure

•  Behaviour-based propensity modelling at product level

•  Life-event and other specialized models deployed on

experimental basis

•  Significant lift in customer product take-up

Marketing and Sales


Digital ‘Big-3’ platform instrumental in sales growth

0

500 000

1 000 000

1 500 000

2 000 000

2 500 000

FY13 FY14 FY15 FY16

Borrow

Transact

Investment

Insurance


Big Data use cases – Financial crime

•  Money-laundering risk modelling pioneered use of Big Data

analytics platforms and combination models

•  Fraud modelling combining product, channel, geography, sector

and device behavioural information

•  Network analysis used for complex investigative efforts (AML and

Fraud)

Financial crime


Big Data use cases - CVM

•  Customer profitability modelling allows customer-level

decisioning and strategy guidance across levels

•  Retention modelling retains good customers

•  Pricing optimisation across multiple dimensions

•  Rewards designed to influence customer behaviour

CVM


CVM – Deep understanding of profitability

0

5

10

15

20

25

30

35

40

45

Monthly profit per risk grade

NIACC Cost of capital Tax Opex TTC imp

Risk grade Low risk High risk

Million


Big Data use cases - Operations

•  Operational implementation has taken place to automate a wide

range of credit, marketing, sales, fraud, record keeping, validation

and other tasks

•  Call-centres and collections large focus for efficiency and

effectiveness optimisation

•  Major focus for channel optimisation going forward and perhaps

area of greatest future opportunity

Operations


Modern operations – Contactability is one of many key drivers of efficiency

Aim is to contact the right customer using the right method at the right time with the right message

NPV: TBC

Relevant Contact

Contact language/ message

Contact frequency & priority

Best Time To Contact

Contact Method CONTACTABILITY

01

02

03

04

05


Content


Big Data enablers




The modern FI requires new infrastructure and skills

FROM TO

•  Silo product approach and systems •  Integrated customer approach and systems

•  Overnight batch •  Real-time streaming

•  BI people, IT developers, DB administrators •  Data scientists, architects, engineers

•  Separate RDB and analytic platforms •  Combined data-analytic platforms (e.g. Hadoop)

•  Accounting orientation (historical, incurred basis, top-down)

•  Analytic orientation (forward-looking, predictive, bottom-up)

•  Traditional marketing •  One-on-one marketing


What’s next

Intelligent agents - robo-advisors, service and sales agents

GAAF (Google, Apple, Amazon & Facebook) & others entering financial services

Integrated Financial services (Lend, Deposit/Invest, Transact, Insure, Telco, …)

Thank you

big data in banking - firstrand · pdf fileubs 19th annual financial services conference: the...

Documents