Risk, Fraud & Decision Systems
Production decision systems with model-validation and fair-lending discipline: a $150M risk portfolio and $1B annual transaction policy at Drip Capital; 16 million fraud decisions a month at Citibank.
- PRACTICE NO.
- 06
- PRINCIPALS
- SV · NB
- CASES ON FILE
- 02
- FIELD
- BANKING & RISK · FINANCIAL SERVICES · TRADE FINANCE & LENDING · CRYPTO & BLOCKCHAIN
PROBLEM SPACE
Every transaction is a decision, and most of them cannot wait for a human. The hard part is not the model. It is everything a regulator, an auditor, or a fraud ring will eventually test: stability under drift, fairness under scrutiny, a policy that holds at scale, and a paper trail for every call the system made.
We build decisioning the way banks have to run it: scored in production, validated on a schedule, and designed so the consequential edge cases route to a person while the routine majority clears in milliseconds.
WHAT WE DELIVER
- 2.1fraud & risk modelsreal-time and batch scoring
- 2.2underwriting / KYC / AML flowsonboarding through ongoing monitoring
- 2.3model validation (KS, PSI)stability and drift, on a schedule
- 2.4policy designthresholds, limits, exception paths
- 2.5decision audit trailsevery call reconstructable
HOW IT SHIPS
Systems in this practice run as a loop, not a launch. Inputs are scored, routine outcomes execute automatically, and anything consequential holds at a review gate where a named person clears it with context attached. Every decision (human or automatic) lands in the audit trail.
PROOF
WHO BUILDS IT
ADJACENT
Tell us the use case. One call is enough to scope whether there is a fit, and what it takes to ship.
Start a brief