26 November 2019

Luca Calconi - CRIF

The new frontiers of data sharing

How alternative data prove to be decisive in formalizing unbanked population (the Water Scoring)

The use of Credit Bureau services in the Telco industry (the Italian case).

• Master Degree in Statistics and Post Graduate Master Degree in Finance Economics

• 25+ years of experience in Credit Risk Management and lending processes

• 20+ years spent in top tier organizations (CRIF, Accenture, Experian)

• Global exposure: Europe, Latin America, Middle East

• Supporting CRIF geographic expansion in Middle East and SEA since 2017

• Sparse digital processes• Credit scoring was the new

wave• Risk management focused on

the corporate sector

25 years in the same field and still enjoying it

• Big data• Quantum computing• Machine Learning / Artificial

Intelligence

Agenda

1 Intro on credit scoring

2Alternative data sources for credit scoring

3Credit Bureau for Telco’s risk management

Ultimate goal of credit scoring is to

predict the future behavior of borrowers and their capability to repay financial obligations?

• Commonly classified as ‘first person’ fraudsters: people who ask for credit with no intention to repay the debt

Never pay

Lazy payers

Financial distressed

• People used to mange their financials very carefully usually managing multiple exposures with different lenders

• People who encounter unexpected high expenses or reduction of cash inflows during the repayment plan period.

Credit Scoring is a

largely adopted risk management tool by lenders globally

Scoring is an objective measurement of the risk (or other phenomena) based on data

Processes based on the outcome of a scoring algorithm handle applications fairly i.e. ensure that decisions are not affected by the subjectivity of human beings.

Consistency

Recommended decisions based on score can be configured to be aligned to the lender risk appetite.

Highly flexible tool to respond to strategic risk management changesand to adapt to changing external environment

Control

Scoring models provide accurate long term forecast of risk parameters underlying credit risk management practices in a bank

Scores are powerful forward looking risk quantification measure to be used at all stages of the credit life cycle

Predictive

Scoring models enable high level of processes automation i.e. facilitate lender to make faster, better decisions at all stages of the credit life cycle

Increased processes efficiency enables improved staff productivity, cost reduction and customer satisfaction

Automation

Key

benefits from

the usage of

scoring in your

decision processes

Why lenders globally leverage scorecards to take credit decision

Increase accept rate

Turn account holders

into creditors

Development of POS financing

Impact on lending practiceThe reduction of information gap between lender and borrower is a key driver to boost credit volumes (in economy expansion phases) and to contain credit crunch (during liquidity crisis times)

01

02

03

What data can help risk assessment

What are the drivers which bring consumers to miss their payments

Stability Elements:• ‘Time at’ variables

Personality treats:• Honestly• Discipline• Responsibility • Social desirability

Traumatic events:• Job loss• Divorce

RiskDrivers Financial planning capabilities:

• Age• Debt burden ratio

Role of the data scientist is to seek, analyze and combine for all potential predictive information to reach the most accurate forecast of the counterparty likelihood to default

Credit Product type:• Secured vs Unsecured

Agenda

1 Intro on credit scoring

2Alternative data sources for credit scoring

3Credit Bureau for Telco’s risk management

Slide su access

to credit

Water Score

The problem statement

Is there any correlation between the consumer water bills data and credit risk?

How can these data be used to assess credit risk and affect lending process?

The business opportunity

Can ‘alternative’ data source help to assess counterparty risk whenever ‘traditional’ data sources are not available?

CRIF R&D unit regularly conducts analysis to enhance risk management tools.

Two different data sources used:

• 8 years of water bills data• Credit file payment performance in Eurisc®, the Italian Credit

Bureau

End of outcome window

Snapshots: Only bills data analysed for clients with NO credit history

8 years of bills data available to calculate predictive characteristics

12 months of performance data (Credit Bureau History) available to assess clients performance

Diagnostic reports can be provided

• To detect model accuracy• To take lending decisions

Slide su access to credit

Firstly we created the data-mart with the water company clients who – as of ‘today’ – do have a credit history in the credit BureauThis is a pre-requisite to track the payment performance and classify counterparties as good or bad

Secondly we filtered the initial data-mart to fulfil the requirements to build the ‘use case’1. We excluded all counterparties WITH some credit

history before bills data are available2. We excluded counterparties with some Bureau

history before the selected outcome window

The rationale behind is that we want to replicate a scenario in which the

sample is representative of No Hit population, at the time of the credit evaluation

Slide su access to credit

Development Sample KPIs

14k individuals

12-14 months of payments data in Eurisc

Installment loans contract only

90+ dpd was the definition adopted to classify a contract as BAD

3.2% is the portfolio bad rate

Demand for Credit: How do SMEs Choose a Creditor?

• Socio-Demographic • Time on book• Disputed bills• Etc.

• Total invoices• Settled/Non Settled bills• Payments• Delay

Bills and Payment dynamics over time

Categorized collectionaction types and recoverybehaviour

1 2 3 4

Several counterparty /

contract data was

analysed

Client Bills Trends Collection

Step 4:

Reject Inference

Model creation

1. All variables were analysed to measure the degree of

correlation with risk (target variable) – Bivariate Analysis

2. A subset of candidate variables was identified based on risk correlation, business logic, proved data quality and

correlation among themselves – From Long to Short List

3. Statistically significant variables were selected and partial scores are derived for each possible value of the

model variable – Logistic Regression

4. We measured the accuracy of the model with the best

practice indicators (globally adopted) – Post Development Accuracy Measurement

Final ModelResults

• Average of days past due (last 4 quarters)

• Amount of bills settled

• Ratio between billed amounts not settled over the amounts settled

• Difference between days past due (last 6 months against previous 6 months)

• Area of residence

Bills data (amounts and payment behavior) in the last 12 months resulted to most significant

Key variables

Step 4:

Reject Inference

51% is the value of The Gini Index

Post Development Analysis

12.3% is bad rate for the lowest score decile

The lowest score decile is 25 times more risky than

the highest decile

The score can identify 60% of the total population

with a bad rate of half of the average

Almost 80% of the total population could take

benefit form water bills data sharing

Agenda

1 Intro on credit scoring

2Alternative data sources for credit scoring

3Credit Bureau for Telco’s risk management

Slide su access to credit

Telco’s and Financial Institutions: a win-win partnership

• Telco’s data can significantly help lenders to predict risk

• FIs data can significantly help Telco’s to optimize post-paid business processes (use cases in the next slides)

• Assets financing is the common ground• Competitive advantage for Telco’s• New line of business for many landers• Entry point for financial inclusion Institutions

Slide su access to credit

Since 2011 Telco’s can have full access to CRIF Credit Bureau. All the dominant players benefit form using data and scores in the on-boarding and customer management processes

Use case #1

• Accept and decline decisions: a better balance between increase in accept rates and loss reduction

Use case #2

• Debt Collection processes: lowest collection costs by better segmenting customer base using Credit Bureau Score

19

Use case 1: Origination

Redesign of origination strategies, introducing CRIF Credit Bureau Score to improve the customer verification process (about 4M of applications per year)

Policy rulesBlack list & Public Info

Score

1 2 6Full Cost Model

4

P

…

O

A

B

1 2 3 … N ND

INTERNAL RISK CLASS

3

PER

FOR

M

1

2

10

…

Override KO

5

Saving KO

If KO

New KO

10% Bad rate reduction

10.5M$ loss reduction (yearly)

14% Increase of automatic decisions

2 Months of payback period*

* Considering Bureau report cost but excluding operational cost reduction

20

Use case 2: Dunning

The Company outsources about 350M$ per year in external collection management, through 10 collectionagencies. The current recovery rate is about 11%

The introduction of collection score allowed to obtain a more effective processes, maximizing recovery andcash curve speed

Co

llect

ion

Sco

re

Telcodata source

CRIF data source

Internal Score

External score CRIF (Eurisc e CribisD&B)

Internal – external score matrix

1

2

10

…

1 2 10…

GINI0,58

GINI0,56

GINI0,36*

GINI0,43*

Pro

cess Op

timiza

tion

I Action

Early Collection Score

Mediumrecovery

Low recovery

I Action

DCA SOFTI

ActionHigh

recovery

AS IS

TO BE

800k$ operational costs saving 12,5% increase of collected amount

21

Main results obtained leveraging the use of Credit Bureau

MAIN REFERENCES

Approval increase

Loss reduction

Operational costs

Automatic decision rate (process efficiency)

+3÷5 pp

- 7÷12 %

- 5÷15 %

+10÷30 %

22

Key Takeaways

• Alternative data sources add value to predict risk – standalone of in conjunction with traditional data

• In the absence of Credit Bureau data Water Bills data can help financial inclusion

• Credit Bureau data add value to the Telco’s processes

• Telco’s and Financial Institutions can cooperate to grow their business

Merci de votre

attention !

(garamond 48)