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Credit Scoring
Dr. Selim Seval
Tehran, November 11th, 2014
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Presentation agenda
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How credit scores are developed - predictive modelling - validation The importance of scores - decision making - pricing
Credit scores and Basel II and III
Credit scores in an emerging market environment
Credit scores in credit insurance
The future in credit scoring
What is a credit score - advantages - types of scores
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Credit scores
3
What is a credit score?
Credit score is a
• A quantitative measure of failure or delinquency risk
• A predictive indicator: evaluates the likelihood of a future event
• Objective and consistent: statistically derived from actual
historical information
Probability of Default (%) Score (0 – 100) Rating (AAA, AA, AA)
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Origination
Approve / Decline
Initial credit limits
Risk-based pricing and collaterals
Cross-sell / Up-sell
Retention
Wallet share
Limit increase / decrease
Authorizations
Review
Prioritization
Resource allocation
Outsourcing
Acquisition Portfolio
Marketing
Risk
Management
Collections
Advantages of credit scoring
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Advantages of credit scoring
Speed • process a larger number of credit applications therefore cutting down the costs
Advanced risk management • know in advance the risks associated with any customer (predictiveness)
Consistency and objectivity • establish credit allocation and follow-up policies based on solid criteria that do not change within a
given time interval and applied consistently throughout the organization • decide objectively throughout the whole organization irrespective of the credit officer, branch and
regional peculiarities
Employee productivity • plan the work time of its credit officers; i.e. for top rated customers an automatic accept decision
may be taken while sparing more time on lower rated and problem cases. Lowest rated customers may also be automatically rejected
Portfolio management & monitoring • monitor customer and portfolio risks throughout time and take the necessary steps in advance to
avoid bad debts
Improved forecasting and strategy formulation • store credit data in a more organized fashion to enable further analysis including future runs and
tests of the scoring models
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How credit scores are developed – predictive modelling
Types of scorecards during the life-cycle of a credit
Debt Collection
Customer Management
Fraud Management
Origination
ANALYTICS
Application Score
Behavioral Score
Credit Bureau Score
Collection Score
Marketing Score
Attrition Score
Fraud Score
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Scorecard development
Credit scoring is a process whereby information available is converted into numbers that are added together to arrive at a score via a scorecard. • Generic Method (least advised method, may be used as a first step in consumer scoring) • Expert Method (judgmental, good for emerging market environments as a first step) • Statistical Method (well-proven methodology, may be strengthened with bootstrapping and reject inference for emerging market scorecards) • Hybrid Method (good to overcome deficiencies in electronic data storage)
How credit scores are developed – predictive modelling
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Scorecard development methodology
How credit scores are developed – predictive modelling
Scorecard development methodology has evolved for more than 50 years since it was first introduced to assist banks in making their credit lending decisions. Starting from the divergence based scorecard method which dominated the industry for the first a couple of decades, it has now diversified into a spectrum of methodologies: • logistic regression • decision trees • mathematical programming • neural network • genetic algorithm • survival analysis modelling • support vector machine • graphical models • double hurdle modelling Among all these, logistic regression is now the most commonly used method for scorecard building
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How credit scores are developed – predictive modelling
Most commonly used scorecards
• Application Scorecard (First-time Customer Scorecard) Derived with non-financial, financial, industry and external behavioral data
• Behavioral Scorecard Derived with internal and external behavioral data
• Existing Customer Scorecard Derived with full credit dataset (non-financial, financial, industry, external and internal behavioral data) • Credit bureau scorecard Provides a behavioral score of an entity pertaining to how its obligations were met to the entire universe of financial institutions
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How credit scores are developed – predictive modelling
Application Scorecard
• Application scorecards are used to analyze credit applications. Most application scorecards are custom, which means that they are based on the information collected from the applicant as well as external behavioral data from other sources.
• Credit bureau scorecards are useful for institutions without historical data or the resources to develop custom application scorecards. They can also be used in conjunction with application scorecards to increase overall predictiveness.
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How credit scores are developed – predictive modelling
Behavioral Scorecard
• Behavioral scorecards are used for ongoing management of existing accounts. These scorecards are based on the customer’s actual payment and credit usage behavior.
• Credit bureau scores are also very useful for the management of existing accounts because they consider a customer’s credit risk by analyzing their payment behavior across all trade lines reported to the credit bureau at that point in time.
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How credit scores are developed – predictive modelling
Developing a scorecard - data vs information
• Data = information lacking structure (for instance the alphabet is data)
• Information = data with structure (a word, a sentence etc.)
• Information consists of facts and data organized to describe a particular situation or condition. Information can be judgmental. Data is objective, impartial.
• Data is an electronically readable piece of alphabetical, numerical or alphanumerical information stored according to a given layout.
• Data is often meaningless unless one or several of them are used to derive an information with the help of a formula or an algorithm.
i.e. A balance sheet does not tell whether the company will fail unless analytics is applied on it.
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How credit scores are developed – predictive modelling
Types of data elements (predictive variables)
According to sources • Application data: Data that the applicant provides to the financial
institution at the time of the application • Behavioral data: How the credit customer behaves with regard to
meeting its obligations Internal behavioral data: Behavioral data of the customer that
the financial institution captures External behavioral data: Behavioral data of the customer that a
credit bureau captures from all financial institutions
According to characteristics • Financial information • Non-Financial data and information • Industry and economic information
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How credit scores are developed – predictive modelling
Variable selection
Starting from the Long List of available variables, these steps lead to a Short List of candidates to became part of the Final Model.
1. Exclusion of variables with a high percentage of missing values
2. Exclusion of variables with a low predictive power
3. Exclusion of incoherent variables
4. Exclusion of correlated variables
Identification of the Short List, constituted by variables
• Statistically highly predictive
• Well interpretable from a business and operational perspective
• Uncorrelated
That are candidates to be entered the statistical model
Available Variables
75
61
40
29
16
LONG LIST
MEDIUM LIST
SHORT LIST
Data consistency
Data analysis
Correlation Analysis
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How credit scores are developed – predictive modelling
Steps in scorecard development
Project Objectives Definitions
• Define objectives for the project • Organizational objectives and scorecard role • Review credit policy (past, present, and future)
Model Design • Definition of the sample and performance windows • Review credit policy (past, present, and future) • Target measure definition
Sample Definition
• Sizing • Data requirements specifications • Data Extractions
Data Organization
• Data Aggregation, merging and manipulation • Normalization of the sources attributes • Data Integration
Project Objectives Definitions
• Define objectives for the project • Organizational objectives and scorecard role • Review credit policy (past, present and future)
Model Design • Definition of the sample and performance windows • Review credit policy (past, present, and future) • Target measure definition
Sample Definition
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How credit scores are developed – predictive modelling
Steps in scorecard development
Exploratory Analysis
• Data Quality Check, in terms of readability, correctness and completeness
• Variable understanding
Segmentation Analysis
• Segmentation of the known population, with the purpose to augment the predictive power of the model.
• Business driven segmentation
Reject Inference
• It allows to recover the target measure (e.g. Bads
and Goods) for the data records that do not have one (these records are typically the rejected cases).
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How credit scores are developed – predictive modelling
Steps in scorecard development
Data Analysis
• Purpose of this analysis is the selection of predictive data variables, and group the data values into robust categories
• Selection of the best predictive and “business meaning” variables
Preliminary Model Development
• Process a multivariate model based on the target
variable and on the set of predictive variables; • Verify the quality and performance of the model
with Statistical Measures of discrimination
Fine Tuning and delivery of the model
• Refinements with the feedback from users • Adjusting of the model and identification of
anomalies or concentrations in the distribution of the score
• Eventual intervention in the grouping categories of the variables
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How credit scores are developed – validation
Validation tools – Perfect Model, Gini Coefficient and ROC
There are several statistical methods used to measure the scorecard power, among which two are the most commonly used.
Cu
mu
lati
ve %
of
defa
ult
s
Cumulative % of total sample
Perfect model
Random model
(No differentiation)
0% 100%
Area A
Area B
Best scores Worst scores
Area A
Potential model
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How credit scores are developed – predictive modelling
Validation tools –Gini Coefficient and ROC
Gini Coefficient = A / (A+B)
ROC = A + (A+B) / 2*(A+B)
Cu
mu
lati
ve %
of
defa
ult
s
Cumulative % of total sample
Random model
(No differentiation)
0% 100%
Area A
Area B
Best scores Worst scores
Area A
Gini Coefficient = A / (A+B)
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How credit scores are developed – validation
Validation tools
• Test the statistic stability, i.e. the model capability to replicate on one or more samples performances coherent to the ones observed during the development phase.
• The validation analysis involves comparing results from the validation samples against results from the original development database.
• In particular, comparisons are made for the bad rate against score,
BOOTSTRAP: sub-sample iterating generation starting from 25% of the population
OUT OF SAMPLE: 10-25% holdout sample from the development database kept separate from all analyses
OUT OF TIME: more recent population than the development
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How credit scores are developed – predictive modelling
Focus on the Overrides Analysis
• Verify the stability of the decision scoring tool compared to a discretional usage
• It allows to know the utilization of the decision scoring tool and to evaluate the impact in terms of cut-off strategy
• The override is a decision that is in contrast with the decision suggested by the score
• The override is a physiologic element in the application scoring process. It is very important for the financial institutions to have consistent policies that define some thresholds in the usage of the overrides and tools dedicated to codify, measure and store the overrides and the reasons.
• The override could be permitted in relation to the commercial strategy of the institution (or if the model doesn’t cover the variables that are considered particularly relevant for the decision related to the credit request.
• The quantitative measure between the override rate and the Reject / Accept rate should be always kept under control
“Disciplined” utilization of the overrides
“Randomized” utilization of the overrides
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External
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The importance of scores - decision making
Application for Credit
Application Process
External Information
Decision Rules
Automatic Approval
Automatic Reject
Gray Area
Review or Adjust Terms
Credit Allocation Process
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The importance of scores - decision making
Scores/ratings form the basis of a creditor’s line allocation, increase / decrease and termination decisions. Scorecards facilitate the implementation of: • risk-based pricing and limit allocation
• auto-reject and auto-accept rules
• cut-off policies
• customers’ credit performance evaluation
• collateral policy
• early warnings during credit follow-up
From scores to decisions
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The importance of scores - decision making
SME
•Reject / Very
Low Limit
•Strong Collateral
•High Price
•Low Limit
•Strong / Medium Collateral
•Medium Price
•Medium Limit
•Weak Collateral
•Low Price
MID-MARKET
•Very Low Limit
•Strong Collateral
•High Price
•Medium Limit
•Medium Collateral
•Medium Price
•High Limit
•No or Weak Collateral
•Low Price
CORPORATE
•Low / Medium Limit
•Strong Collateral
•Relatively High Price
•Medium / High Limit
•Medium Collateral
•Medium Price
•Very High Limit
•No or Weak Collateral
•Low Collateral
LOW SCORE
MEDIUM SCORE
HIGH SCORE
Application Score above cut-off and Credit Decision
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The importance of scores - decision making
POOR
•Terminate Limit
•Keep or Reduce Limit
•Keep or Increase Collateral
•Keep or Reduce Limit
•Keep or Increase Collateral
MEDIUM
•Keep or Reduce Limit
•Increase collateral
•Keep Actual Limit
•Keep Actual Collateral
•Marginal Limit Increase
•Keep Actual Collateral
HIGH
•Keep or Reduce Limit
•Keep or Increase Collateral
•Marginal Limit Increase
•Keep Actual Collateral
•Big Limit Increase
•Reduce Collateral
POOR
MEDIUM
HIGH
Internal Behavioral Score & Credit Bureau Score
I N T E R N A L B E H A V I O R A L S C O R E
B U
R E
A U
S
C O
R E
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The importance of scores - decision making
HIGH
Early legal or
collection agency
Accelerate effort or
early legal
Standard collection
practice
MEDIUM
Accelerate effort
or early legal
Standard collection
practice
Delay work queue
or low key follow-up
LOW
Standard collection
practice
Delay work queue
or standard practice
Very low-key
follow-up
LOW SCORE
MEDIUM SCORE
HIGH SCORE
Early Past Due Accounts and Collection Score
P A S T D U E A M O U N T
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The importance of scores - decision making
From behavioral scorecard to an action model
Action models are advised primarily for micro and SME credits
An action model uses the outcomes of the behavior
scorecard and produces decisions like: • Continue as is • Increase collateral margin • Change collateral type • Change price • Decrease limit • Terminate credit contract • Start legal action
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Decision models - pricing
Scores and pricing
Price is a function of: 1. PD for each rating group 2. Collateral recovery ratio 3. Targeted base interest rate / premium rate
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Decision models - pricing
Expected Collection
(within related rating
group)
Defaults Live Credits Defaults Live Credits
0 (1-PD) × radj × P PD × RR × P (1-PD) × P (1 + rbase) × P
Interest Collection
(within related rating
group)
Principal Collection
(within related rating group)
PD = PD related to a rating group
RR = Collateral recovery ratio (as a percentage)
P = Principal of credit at a rating group
rbase = Base interest rate
radj = Risk-adjusted interest rate for a rating group
Scores and pricing
For any rating category:
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Decision models - pricing
PD
RRPDrr baseadj
1
)1(
Risk-adjusted interest rate for a rating group
A 24,95% 0,67% 20,64%
B 23,90% 1,20% 21,17%
C 16,17% 2,18% 22,32%
D 14,57% 3,50% 23,82%
E 14,17% 7,33% 28,38%
Risk-based interest rate
considering
loss given default (radj)
Default Probability
(PD)
Rating
Category
Collateral
recovery ratio
from defaults
(RR)
When rbase = 20%
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Developing scorecards in an emerging market
Developed markets: • Full disclosure • no secrets, SarOx • well-maintained public records • efficient databases Emerging markets: • Confidentiality • reliance on company-sourced data • several sets of accounting books • limited accessibility to public information • Banks may not have written or well-established credit assessment
and decision policies i.e. No clear formulation of bad customers, good customers, auto-reject rules etc.
Differences in market environments
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Developing scorecards in an emerging market
• In an economic environment where volatility is an everyday phenomenon, finding an observation window may be difficult
• The model, whether expert or statistical, should be based on a time period free from extraordinary events
• We have to consider a 12 or 18-month maturity period (i.e. sufficient time period for a credit to mature and allow us to assess accurately its performance)
12 / 18 months
Outcome Period Observation Window
Observation Point Today
Differences in market environments
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Developing scorecards in an emerging market
• Financial institutions in emerging countries usually do not store the contents of their credit files (information) in their computer systems/databases. Information stored in Word or Excel files are not usable for statistical methods
• External data is not readily available in electronic format. Exclusion of such data elements may significantly alter the predictiveness of models.
• It is difficult to change old ways of doing business. Credit officers (underwriters) who do not have prior credit scoring experience are usually prejudiced against scorecards. They fear the scorecard will replace their jobs and duties.
• The scorecard is like a living creature. It must have an owner within an organization. This is highly important for the success of scorecard management.
From a financial institution’s perspective
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Place - 01/01/2014 Name Surname 34
Credits to Individuals
Consumer credits
Credit cards
Mortgage
Vehicle credits
Small Business Credits
Micro Companies
SME Credits Mid-market Credits
Large Corporate Credits
• A person or a family
• Structured on fixed income
• A company or group of
companies
• Commercial activity
• Structured on a future
variable income
Developing scorecards in an emerging market
Typical segmentation of credits
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Place - 01/01/2014 Name Surname 35
Why company size matters?
• Corporate scorecards focus more on a company’s commercial and financial performance while a small business scorecard tries to predict a company’s future payment record
• In small business scorecards, the main shareholder’s payment behavior, his/her socio-economic status and demographic traits are also major determinants
• In small business credits, the scorecard should be able to process large volume of applications in a short period of time and help credit officers to assess majority of applications automatically
Developing scorecards in an emerging market
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Place - 01/01/2014 Name Surname 36
• Lack of sufficient information and data accuracy are major concerns with small businesses
• Small business variety is vast to enable them consolidate under meaningful groups
• There are no benchmark segmental ratios for small businesses
• Statistical demographic risk-related information is not available
• Generic models implemented in developed countries will not work effectively in emerging countries
Difficulties in small business scoring
Developing scorecards in an emerging market
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• Establish an international benchmark for banks’ credit evaluation
• Create awareness for data quality, transparency, credit scoring and analytical risk management systems
• Separation of default risk and loss risk
Expected Loss (EL)=Probability of Default (PD) x Loss Given Default x Exposure At Default (EAD) Borrower Rating (What is the likelihood that the customer will default on an obligation?) Facility Rating (In the event of a default, how much does the creditor expect to lose?)
How the Basel Accord helps?
Credit Scores and Basel II and III
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Credit insurance = Non-cash credit
Credit Scores in Credit Insurance
• The task of an underwriter in a credit insurance company is not any different than a credit allocation officer in a bank or a lending institution
• The same credit allocation and risk assessment processes should apply
• Credit insurance is akin issuing a bank letter of guarantee (a non-cash credit product)
• A major difference is that an insurer obtains the credit information report from external sources. A credit insurer should also have access to bank credit bureau databases and scores
• The insurer should insist that the credit reports contain credit scores developed and maintained with appropriate methodolies explained in this presentation. The insurer should have periodical access to the informaton provider’s score development and validation processes.
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Importance of scores in credit insurance – future trends
Credit Scores in Credit Insurance
• As economic and market conditions change rapidly, credit insurers will need «smart scores» that quickly adopt to current market conditions in an effort to avoid increases in claims.
• Credit insurers will continue to obtain scores from external competent score providers. Therefore, score providers should rely more and more on analytics to calculate more sophisticated scores which will have the capacity to warn the insurers more accurately and timely.
• Platforms developed and operated by information or score providers will gain importance as these platforms will enable the users to integrate any type of credit data from any source to integrate and come up with «smart decisions».
• As volumes increase when insurers expand their portfolios, there will be
more reliance on scores for small ticket policyholders. Scores will be «the most important factor» in making a decision to issue a policy to cover a small scale debtor.
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The Future in Credit Scoring - Yesterday and Today
Yesterday • limits only to well-known names • highly collateralized transactions • more large scale customers than SMEs • personal judgments of the credit officer as key to credit decisions • no mathematical modeling (scoring and rating) • No well-defined risk management processes Today • micro and SME customers replacing big ticket customers (the world
is becoming one marketplace) • IT developments facilitate accumulation of credit data • credit risk management analytics (objective methodology – scoring
and rating for default prediction) • universal banking regulations (Basel Accord) • reputation collateral
The credit environment – Yesterday and today
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The Future in Credit Scoring - Yesterday and Today
Tomorrow
• Automated self-service credits from the internet
• Credits will go mobile – accessible from mobile devices
• Centralized world-wide databases will gain importance (KYC and anti-money laundering)
• Scoring and decisioning models will replace personal decisions, databases and analytics will dominate the financial businesses
• No limits will be given without a credit score
• Universal financial practices will be more enforceable
The credit environment – Tomorrow
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Thank you
Dr. Selim Seval