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1 How (not only) risk department uses data in HC Tomáš Kočka Head of Fraud Prevention Home Credit International.

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Presentation on theme: "1 How (not only) risk department uses data in HC Tomáš Kočka Head of Fraud Prevention Home Credit International."— Presentation transcript:

1 1 How (not only) risk department uses data in HC Tomáš Kočka Head of Fraud Prevention Home Credit International

2 HOME CREDIT 2 Consumer loan business model 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 SalesPartnerApplicationUnderwritingCollectionCRM

3 HOME CREDIT 3 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

4 HOME CREDIT 4 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

5 HOME CREDIT 5 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

6 HOME CREDIT 6 Underwriting – scoring, verification 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 expenses

7 HOME CREDIT 7 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

8 HOME CREDIT 8 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

9 HOME CREDIT 9 Data Quality Issues Entity identification 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 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 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 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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