How (not only) risk department uses data in HC
Tomáš Kočka
Head of Fraud Prevention
Home Credit International
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HOME CREDIT
Consumer loan business model
Sales
Partner
Application
Underwriting
CRM
Collection
• Sales Force management
• Administrator, POS and Partner management
 Contracts with partners
• Marketing, application process
• Underwriting – scoring and verification
• CRM – cross sell and up sell
• Collections
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HOME CREDIT
Sales Force management
• Sales Volumes are planned and monitored
• POSes are distributed geographically
• Fraud and Default Rates need to be measured, on all
POSes and newly opened POSes
• Profitability should be monitored
• Commissions need to be tightly monitored
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HOME CREDIT
Administrator, POS and Partner management
• Administrator motivation on high yield products, total
sales and low risk figures
• POS & Partner default rate, sales volumes monitoring
• POS & Partner profitability monitoring
• Contract with Partners to make sure goods delivery to
address in contract, to prevent sharing access info, data
manipulation and other unwanted behavior – to be
monitored
• Welcome calls to verify the clients/contracts quality are
the fastest tool to fight fraud
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HOME CREDIT
Marketing, application process
• Marketing – defines product mix, wants to sell long term
high total yield product
• Sales – different actions for partners, action products
 How do we stand in and out of action, from sales and profit
point of view
• Application form – key driver of time to yes and risk
figures
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HOME CREDIT
Underwriting – scoring, verification
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Scorecard quality has to be monitored, predicted quality of
underwritten portfolio as well, shifts of population need to be
discovered quickly
Scorecards need to be updated frequently
Optimal verification scenarios including cost benefit analyses
Reject rates per distribution channels, products and good types
need to be monitored quickly
Dtto default rates (both vintage and portfolio) but with longer
periodicity
ROA approach towards underwriting
ABC critical for optimal underwriting
Constraints stemming from contracts with partners heavily
influence optimal strategy
Future profits from cross sell impact the underwriting as well
Collection efficiency is important in the estimate of credit risk
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expenses
HOME CREDIT
CRM – cross sell and up sell
• The primary goal is to cross sell from POS loans to
revolving cards and cash loans
 Separate behavioral scoring models are needed; activation
and up-sell procedures
 Important is activation rate, therefore propensity to buy
models are developed
• Some customers are coming back themselves for POS
loan again
• This is the cornerstone of consumer loan business
profitability
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HOME CREDIT
Collection
• Effective collections requires sufficient staffing
• Effective collection needs to be fast, collecting before
30DPD is critical
• Optimal future actions heavily depend on past collection
behavior – any contact or payment means much higher
probability to collect
• There needs to be fast first payment default collection
quickly going from phone to personal and law collection
• There exist prime times, ideal is peak staffing
• Different collectors have drastically different collection
efficiency, important is to measure and report this, relate it
to bonuses, support team work by team bonuses
• Champion challengers are important for optimal collection
process equally as segmented approach
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HOME CREDIT
Data Quality Issues
Entity identification
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Critical is client identification – we have solved this quite well in primary system by
a combination of data entry logical checks, strong and weak matches and manual
resolution of potential weak duplicities
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For scoring it is very useful to identify streets and addresses, which we are
currently able to do in CR only to some extent and not in other countries
Wrong or unstructured data
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We face never ending problems with goods type, being filled in wrongly and only
relevant information being in detail bought goods description from which we guess
if it was a mobile phone or not
Missing data
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We face critical problems with missing data about calls from IP telephony
(collections, card activation), missing relations between call and its result, missing
relation between end of one collection method and start of another one, … .
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