Lambros Tsolkas Partner Credit Analytics · customer behaviour • Product pricing & re-pricing •...
Transcript of Lambros Tsolkas Partner Credit Analytics · customer behaviour • Product pricing & re-pricing •...
Lambros Tsolkas
Partner
Credit Analytics
Athens, November 9th, 2012
Copyright © 2012 Accenture All Rights Reserved. 2
European Credit Market
Despite economic headwinds that still impact the economic
and credit sector recovery, some positive signs are emerging
Source: ECB statistics
The volume of credit has
declined in Spain, UK and
obviously in Greece,
where the private sector
deleveraged due to high
debt levels and worries of
future economic
uncertainty fuelled by
sovereign debt crisis
Lending trend in Europe
(2007-2010, 2007=100)
90
95
100
105
110
115
120
125
130
135
140
145
2007 2008 2009 2010 2011 2012 2013
Nordics
CEE
ES
IT
DE
FR
UK
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Credit Challenges
Credit market is currently facing four main challenges
• Large foreclosure &
write downs
• Failure of the
collection strategy
• Increased
delinquency
Credit losses
• Internal & external
frauds
• Changing fraud
patterns
• Increased fraud
losses
• Failure to detect the
fraud incidents
Fraud
• Failure to
understand
customer behaviour
• Product pricing &
re-pricing
• Bad strategies and
wrong
segmentation of
customers
Loss making credit
portfolio
• New addition to the
existing regulations
• Existing regulation
becoming strict in
adherence and
compliance
• Poor data quality
process and
technology to adapt
for the change
Regulatory
compliance
1 2 3 4
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Source: Accenture Risk Analytics Study (2012)
High performing banks are increasing their risk analytics
investments in order to address the challenges efficiently
Change in investment
1% 1% 1%
10%
24%
49%
15%
Decrease greater than 20%
Decrease 10%-19,9%
Decrease 0%-9,9%
No change
Increase greater than 20%
Increase 10%-19,9%
Increase 0%-9,9%
30%
Area of risk analytics investments focus
32%
39%
55%
50%
58% 58% 56%
Data quality and sourcing
Software
Staffing
Reporting
Systems integration
Management use and acceptance
Business rules development
Modeling
Risk analytics investment Risk analytics investment focus
% % 10% - 20% increase on risk analytics investments
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Credit Value Chain
Analytics will have significant role throughout credit value
chain
Credit Risk Mitigation
Credit policies
Credit Risk Management
Approval
and review
Early warning &
pre-collections
Marketing
and sales Servicing
Product
offering
Collections
& recovery
Credit Scoring Analytics
1
Fraud Analytics
2
• Credit Monitoring
Model
• Customer
Segmentation &
Credit Re-structuring
3
Recovery Management
4
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Analytics – Credit Scoring Methodologies
Risk scores predict the likelihood of payment, default or
repayment taking into consideration a variety of variables
Acquisition credit scoring Internal behavioral scoring Collections scoring
Independent Variables
• Credit bureau tradeline history
• Past payment performance
• Depth of credit background
• Amount of existing outstanding credit
• Derogatory information – eg.
Collections, bankruptcy
Dependent Variables
• Pay vs. No-pay
• Response vs. No-response
• Churn/Attrition vs. Retention/Loyalty
• High-profit vs. Low-profit
• High LTV vs. Low LT
Performance Period
1
• Customer targeting
• Acquisition
• Order receipt & fulfillment
• Invoicing & revenue recognition
• Management & cash application
• Payment processing
• Treatment Optimization
• OCA/3rd Party Management
• Write-off & Bad-debt Recovery
Non-Exhaustive
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Analytics – Credit Scoring Levels of Sophistication
Different level of credit scoring capabilities are required for
the different types of industries
High-level of credit scoring sophistication is required
Credit scoring sophistication
factors
• Availability of independent
data sources
• Balance per account
• Transaction volume
• History of use
1
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Analytics – Consumer Credit Bureau Score
Risk scores predict the likelihood of payment, default or
repayment taking into consideration a variety of variables C
on
su
mer
Cre
dit
Bu
reau
Sco
re
Consumer
payment history
Outstanding debt
Credit tenure
Report inquiries
Types of credit
outstanding
35%
30%
15%
10%
10%
The more bills that have been sent out for collection, the lower the
overall score.
The more cards at/near the limit, the lower the score.
The longer the consumer has had established credit, the higher
the overall credit score.
The more recent these inquiries, the lower the credit score.
The number of loans and available credit from credit cards a
consumer has makes a difference.
Credit score
variables
% of total
score
Credit scoring approach
1
EXAMPLE
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Analytics – Fraud Management Methodologies
There are several key analytic methodologies that enable
banks to prevent and detect fraud
2
ILLUSTRATIVE
Fraud Scoring Models Out of Pattern
Analysis Linkage Analysis
Rules / Decision Tree
Development
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Fraud scoring is the most commonly used methodology for
fraud prevention and detection
Logistic Regression Methodology
Good client Bad client (FPD1) Good client Bad client (FPD)
Neural Networks Methodology
KS: Kolmogorov Smirnov Test statistic methodology
FDP: First Payment Default
Analytics – Fraud Scoring Model
2
EXAMPLE
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Credit monitoring model is providing early warning guidelines
based on client risk profile
Analytics – Credit Monitoring Model Guidelines
3A
• Development of
clients’ risk profile
• Design of warning
“signals” report
Credit
Monitoring
Model
• Set of specific
actions according to
the client risk profile
• Set of leaned
processes per risk
profile
• Ratios based on
credit managers
experience
• Calculation rules to
“weight” even “early”
warning
• Actions tailored per
risk profile
• Timeframe tailored
per actions
Dete
ct
Man
ag
e
Guidelines
Continuous improvement
of credit quality and
reduction of bad loans
Innovation
Active credit portfolio
management with overall
reduction of losses and
default rates
Optimization of credit
collection with higher
recovery rates and lower
costs
Operational excellence
that delivers lower
operative costs
Value for Banks
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The Model assigns client with a risk profile (color), linking
every color to specific and time based set of actions
Analytics – Credit Monitoring Model
Detecting Managing
Pro
ce
ss
Mo
del
an
d t
oo
l
Periodical
internal Rating
(e.g. monthly,
quarterly,..)
Other specific
credit ratio
+
Engine Output
yellow
amber
red
green
blue
“Regular”(1)
Monitor
Actions/ rating re-calculation
Actions/ disengage
Disengage
Input
Return
Goals Actions
1 2
Misalignments and
completion of data
missing in rating;
daily ratio to
manage issue
when it occurs
Client report Client report
Soft
Structural
EXAMPLE
3A
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There are three main components that are determining “best-
fit” restructuring offer for customers
Analytics – Customer Segmentation & Credit Re-structuring
Decision to
restructure
Restructuring
offers
Customer profile
Decision tree
Restructuring
offer for
delinquent
customer
3B
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The efficient customer segmentation and profiling is heavily
dependent on the analytics capabilities of the bank
565
0
1
2
3
4
5
6
7
8
9
10
800 700 600 500 400 300 200 100 0
3/EB
1.234
2/EB
1/EB 456
5
4
3
2
1
Key:
: Industrialized restructuring
: Selective restructuring
: Alternative handling
: Size of bubble proportional
to number of customers
Low =
High =
Willingness
to pay
(index)1
Total delinquent portfolio exposure(€m)
“Low hanging fruit”
6.605
4.635 1.476
5.305
Analytics – Customer Segmentation Example EXAMPLE
3B
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From credit recovery to a proactive NPL management
Analytics – Recovery Management Accelerator
4
Enhancing predictive
ability
Dedicated MBO
(Management By
Objective) system
Technological
innovation to support
entire process
• Optimize the combination of strategies to different
distribution channels to improve rates of return and
recovery
• Support and reward best performance
• Provide retroactive guidance to improve entire
credit lifecycle
• Increase efficiency through automation of industrial
activities and the integration of different actors
• Making the updated data available in real time
Enablers
Reco
very
Man
ag
em
en
t A
ccele
rato
r
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The model provides a predictive historical analysis of
customer behavior to improve overall recovery performance
Analytics – Recovery Management Accelerator
4
Historical
Data
Segmentation Data Mart Predictive models
0
50
100
150
200
250
300
350
400
0 25 50 75 100
Collection of
historical data of
customer behavior
Customer
segmentation
based on historical
data
Predictive model
construction to
estimate % of
recovered
customers
• Target specific
customers to
increase recovery
rates
• Assessing early the
effectiveness of
action on individual
customer segments,
thus reducing cost
of credit
EXAMPLE
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Credit Analytics Benefits
Best in Class Credit Analytics can improve ROE BY 2-4%
Source: Bloomberg data and Accenture analysis
NPL
provision
increase
Post-crisis
ROE base
case*
Operational
excellence
& strategic
cost
reduction
Efficient risk
management
Higher cost
of funding
Pre-crisis
high
performer
ROE
Reduced
fee income
Simplify
business
model
De-
leveraging
Customer
excellence
and new
profit pools
Higher
capital ratio
Target ROE
26%
-4-7%
-5-8%
-6-9%
-3-5%
-4-7%
4%
3-4%
3-5%
2-4%
3-5% 15%
• Effective NPL management and recovery
• Better risk pricing for new and existing loans
• More efficient capital allocation
* Aggregate impact of each estimated ROE levers is not cumulative; sensitivity
analysis of a simplified bank financial model is based on a peer set of global banks