identify and create value through data analytics across the credit cycle in consumer credit. Presentation at EFMA consumer credit conference by george georgakopoulos

1. Data Analytics across the Credit Cycle
Case study
EFMA – Consumer Credit Conference
George Georgakopoulos
Executive Vice President – Bancpost
President of the BOD – EFG Retail Services
George.georgakopoulos@bancpost.ro
June 6th 2012

2. Introduction and Summary
The financial environment is challenging across Eastern Europe. In Romania, we have seen lower
capital inflows, lower consumer confidence and higher delinquency over the last 3 years
In such an environment, the consumer credit providers can use data analytics, to identify value
creation strategies
EFG Group in Romania has been using data analytics across the entire cycle of consumer
lending, from targeting to underwriting, in customer service till collections & recoveries
Credit providers can develop their your own models/strategy; there is though great opportunity to
use external tools and data, mapped on their strategies
Key issue for success is top management buy-in; the key task of leadership in a consumer credit
provider is to create a culture where data analytics are embedded into the process of the firm
Extensive usage at EFG Group Romania has given our consumer credit operation a commercial
advantage, doubled net spreads since 2008, reduced roll rates and increased recoveries.
2

3. Romania
A Challenging environment in Consumer Credit
3

4. Capital Inflows
A large current account deficit in the run-up to the crisis was financed by FDI and inflows to the
financial sector. Since the crisis, the inflows would have Romani
Capital Inflows to collapsed, had it not been for the IMF
Sept 2008
25 25
22 22
20 20
17 17
15 15
12 12
10 10
7 7
5 5
2 2
-1 -1
-3 -3
-6 -6
-8 -8
-11 -11
-13 -13
-16 -16
-18 -18
-21 -21
-23 -23
Dec-06 Jun-07 Dec-07 Jun-08 Dec-08 Jun-09 Dec-09 Jun-10 Dec-10 Jun-11 Dec-11
IMF loans Potfolio investment Foreign direct investment
Financial derivatives Financial loans and cash Current Account Deficit
Euro Billion
Data Source: NBR
4

5. Factors Driving Borrowing have evolved negatively since 2008
Ever higher inflows until end 2008 boosted the economy, creating higher employment and subsequently
high optimism at households. Dramatic change of sentiment after the crisis, with some stabilization in
the last 1 year
Employment Outlook Sept 2008
90 14%
85
12%
80
10%
75
70 8%
65
6%
In the period from 2003 to 2008,
60 consumers’ income and employment
4%
55 expectations rose rapidly
50 2%
Mar-02 Dec-02 Sep-03 Jun-04 Mar-05 Dec-05 Sep-06 Jun-07 Mar-08 Dec-08 Sep-09 Jun-10 Mar-11 Dec-11
This benign outlook encouraged the
Unemployment Expectations Unemployment Rate (rhs.) expansion of lending
Balance of positive answers, Percentage points
Data Source: European Commission, ANOFM
Both the financial and employment
Financial Outlook outlook deteriorated sharply from 2008
Sept 2008
500 74
450
73
400
350 72
300 71
250
70
200
150 69
Sep-03 Jul-04 May-05 Mar-06 Jan-07 Nov-07 Sep-08 Jul-09 May-10 Mar-11 Jan-12
Statement on financial situation of household (rhs)
Euro Denominated Net Real Wage (lhs)
Euros, Balance of positive answers 5
Data Source: INSSE, NBR, European Commission

6. Depreciation of the Currency and Lower Expectations on Growth Led to Sharp
Increase of NPLs
Volume of overdue loans increased very quickly from 2008, but the growth rate is receding.
Both the credit risk ratio and the NPL ratio deteriorated rapidly once overdue loans started to accumulate.
Asset quality deterioration in the banking system:
Volume of Overdue
Sept 2008 Sept 2008
3.5 500% 24
B illions
R ON
450% 21
3.0
400%
18
2.5 350%
15
300%
2.0
250% 12
1.5
200% 9
1.0 150% 6
100%
0.5 3
50%
0
0.0 0%
Jan-07 Oct-07 Jul-08 Apr-09 Jan-10 Oct-10 Jul-11
Dec-05 Sep-06 Jun-07 Mar-08 Dec-08 Sep-09 Jun-10 Mar-11 Dec-11
EUR Overdue Loans RON Overdue Loans Credit Risk Ratio NPL Ratio*
Ron Overdue Loans (y-o-y growth rate) Euro Overdue Loans (y-o-y growth rate)
Percentage points
percentage points
Data Source: NBR, Bancpost Estimates
Data Source: NBR
* Backwards from November 2009, the NPL ratio is re-constructed as an interpolation of the Credit Risk Ratio.
Credit Risk Ratio is defined as gross exposure to non-banking loans and interest classified as “doubtful” and “loss” to total non-banking loans and interest, excluding off-balance
sheet elements
6

7. Romania - A case study in consumer credit
How to identify value opportunities by using data
analytics
7

8. Data Analytics across the Credit Cycle have Defined a New
Business Model for EFG Romania
The benefits of using data analytics shifts the “blind mass approach” to “segmented approach” across the
credit cycle, from customer acquisition to collections.
Targeting Customer Customer Service & Collections &
Underwriting Recoveries
of Customers Development Anti-attrition
ACTIVITIES
• Card acquisition • Top-ups • Anti-attrition
• Pricing of new • Collections & recoveries
• X-sell to existing production • Add-ons offers
strategies
lending base • Complaint
• Usage
management
BEFORE
TOOLS
• Judgmental • Same pricing • Judgmental • Delinquency and outstanding
• N/A
policies for all approved policies balances
AFTER
• Focusing on net
TOOLS
• Credit cards • Behavioral score
targeting model margin results, • Yield matrix
• Behavioral score, • Credit Bureau black & white
thus tailored
• Behavioral targeting good • Behavioral score • Employment info from the Pension House
approach per
score (FICO) customers • Property info from Fiscal Authorities
segment
8

9. The Romanian Credit Bureau Provides Valuable Info & Scores
Romania has a single Central Credit Bureau that contains data of ~98% of the banking system, both
negative and positive data. Since 2009, a behavioral scorecard has been developed by Fair Isaac
Corporation (FICO), adding a ranking tool in the existing available data (exposure of the customers,
payment behavior, demographic data)
In 2009, the Credit Bureau introduced an integrated behavioral scoring developed by Fair Isaac
Corporation, called FICO Score. Bancpost was one of the early adopters and implemented it as an
analytical tool to be used across the credit cycle.
The components on which the FICO
score is calculated:
5. Credit mix 10%
1. Payment
4. Pursuit of new
History 35%
credit 10%
3. Credit history
length 15%
2. Outstanding
debt 30%
9

10. Targeting
Bancpost has replaced common sense (judgmental) targeting with an approach based on developed
analytical tools. We studied the existing populations with the respective product based on the mix of
other products and their behavior, based on which the drivers that make an individual to be less risky and
more profitable have been identified.
First phase: development of the model for targeted approach
Observe Create and Apply the logic on
predicting validate the logic: the existing Suppress
Suppress
variables for segmentation or population non low
high risk
revenue and data modeling holder of a Credit revenue
customers
risk Card bringers
Second phase: Review current line assignment process and criteria as the size of the line is the trigger for both revenue
and risk. In case of Amex and Visa portfolio the lines were not differentiated by risk of default (similar lines no matter the
risk) and current equation were reviewed
Results
More targeted approach towards both risk and revenue to provide rank-order of customers by profitability.
Logic was transferred and implemented into our systems, the prospects list is generated automatically and can be refreshed
on a continuous basis
Optimized line assignment, in order to maximize revenues and reduce risk
10

11. Underwriting – Risk Based Pricing (I)
As opposed to a standard approach used previously on all qualifying customers, a segmented approach
has been developed, aiming to reward the good behavior, and as well as to keep the net margin at the
same or higher levels.
Spreads, albeit discounts
RBP Implementation
(using Credit Bureau’s
FICO as key discriminator)
No. of Low risk customers
in the portfolio
Consumers’ market perception of interest for consumer loans. Bancpost’s strategy is to reward existing
good behavior, attract more low risk customers and maintain or increase its net revenues.
DAE was estimated for a 5Y loan, 30 days between the simulation and the 1st due date, 12,000 RON as loan amount
Avg. Market DAE
~ Non Secured RON ~
BT Alpha Var. BP var. BRD CEC B Rom RZB var. BP var. BP var. BCR Garanti UCR Sp Alpha fix BP var. Bravo fix
Seg A Seg B Seg. C
Data as of December 2011 Before RBP 11

12. Underwriting – Risk Based Pricing (II)
The risk-based pricing was implemented as an extensive marketing campaign (A LOAN IN YOUR
MEASURES), with very good results and good press coverage.
Introduction of
RBP Product
12

13. Customer Service – Anti attrition
Bancpost developed an anti-attrition model for Amex Cards to replace the “common sense” approach of
proactively (through retention campaigns) or reactively addressing customers.
Categories of Variables for Propensity to Attrition Modeling
Based on:
• Customer Life Time
Transaction Data Value
• Probability of attrition
Customer Service Payments Data w/bank
• Spending pattern
• Utilization
Account Performance Retention Strategy Marketing Data
Clients are addressed
differently with and not only:
Application Data
Other relationships w/bank • annual fee waiver
• cash back
Credit Bureau Data
• lower interest
The model provides the client’s likelihood (%) to attrite and also the customer lifetime value (CLTV).
13

14. Collections - Early
The strategy for early collection shifted from time-based approach to a risk-based approach of the
delinquent customers; risk-rating per customer was derived from the Credit Bureau’s FICO score and
own Basel models.
tions
llection ac
Low Risk Intensity of early co
delinquent days
High Risk Intensity of
early collection a
ctions
Risk based collection strategy led to
decrease in vertical 1-5 roll rates
Per each risk segment and bucket, different collection tools & actions are applied:
for each bucket, different letter layouts & text were implemented;
intensity of calls varies according to risk & bucket: lower buckets, higher intensity is applied for medium & high risk
accounts, while higher buckets low risk is treated with higher intensity;
different timeline is used in sending letters and text messages. 14

15. Late Collections & Recoveries
The Legal process uses an information based strategy for recoveries. Considering answers received from
interrogation performed to state authorities, the case is assigned to either legal or amicable process.
180+ dpd recoveries
Information based
recovery strategy and
We interrogate the Fiscal intensification of
actions
Authorities and the Pension
House
Per account strategy is
Starting point for
defined by the relevant defining recovery
information strategy using
customer risk
if no information is identified,
sources are re-interrogated at
regular intervals
15
Bancpost internal data

16. Financial Results & Data Analytics
With the help of data analytics across the credit cycle the effects of the financial crisis are not “visible” in
the net spread of the consumer lending business.
Consumer lending net spreads (after impairment)
250
Risk-based targeting
200
Risk-based pricing & limit allocation
for cards 150
Old programmes 100
Risk-based collection strategies 50
Information-based recoveries
0
FY 08 FY 09 FY 10 FY 11 FY 12
Act Act Act Act Prop
16

17. Conclusions
The financial environment is unfavorable to consumer finance across Eastern Europe
driven by lower capital inflows, lower consumer confidence and higher delinquency
since the crisis started in 2008
EFG Group in Romania has been using data analytics, and extensively data and scores
from the credit bureau, across the entire cycle of consumer lending, to identify value
creation opportunities
Extensive usage at EFG Group Romania has given our consumer credit operation a
commercial advantage, doubled net spreads since 2008, reduced roll rates and
increased recoveries.
17