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Lenders & fintech

A microlender or fintech has to lend in minutes without losing money on bad borrowers and fraudsters.

typical problems
  • Scoring relies on rules nobody has tested
  • Online applications are checked by hand
  • Portfolio quality and arrears are only visible in reports after the fact
how we help
how we usually start
  • Testing the current scoring rules against historical data
  • Online identification and screening inside the application flow
  • A scorecard and approval cut-offs, then monitoring of model quality
what changes
  • An application is checked and decided without an operator
  • Approval cut-offs are chosen from data, not by feel
  • Model quality is tracked on a regular schedule
solutions

Problems we solve in this industry

Scoring and automated decisions

A decision on each application in minutes, based on a tested risk model

solutions and platforms

What we use

Trisigma Analytica

One of our two core offerings. Built in-house for banks, lenders and insurers: IFRS 9, ALM, lending and reporting.

primary

Sumsub

Customer verification, AML screening and fraud prevention on one platform.

primary

Snowflake

The data cloud for analytics, AI, data engineering and secure data sharing.

complements
read

Related insights

A credit scoring model for microlenders: where to start

What data you need, when a simple scorecard is enough, how to validate the model, set approval cut-offs and watch for drift.

write to us

The first 30 minutes are a free review of your problem

info@trisigmaerm.com
Discuss a project Request an IFS Cloud demo
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