Organisational and business context (3 min):

• New lending product in a “risky” segment
• Approach is to provide access, but price-for- risk

Analytical Problem (2 mins):

• Large amounts of customer data, from traditional and emerging sources
• Very young lending program. Only 1000 disbursals, not “bads” as yet
• How to estimate risk even as the data is thickening?

Data Structure (5 mins):

• Specific data from the lending program
  • Nearly complete set of customer features at the time of booking: application form, credit bureau, “big-data” footprint through mobile phone, employment details, bank statements etc.
  • Partial outcome data: missed payments but no defaults.

• Surrogate data from the credit bureau
  • Nearly complete outcome data: actual defaults on all organized sector loans.
  • Restrictions on data access, must be processed at the bureau.
  • Limited data on customer features: “big-data” footprint, bank statement insights, employment details etc. are not available at the bureau.

• Adding new data every month
  • Missed payment/ default outcomes, and qualitative diagnostics provided by domain experts.

Analytic Approach #1 (6 mins)

• Build model on surrogate outcomes by merging our partner’s data with credit bureau data, to the
  extent data sharing was possible.

Analytic Approach #2 (6 mins)

• Build model on our own partial outcomes, using data sources from app form, credit bureau, “big- data” from mobile phone, summarized bank statements etc.