Bank analyst reviewing fraud performance and risk metrics

Build fraud analytics banks can explain, tune, and operate

Cicrim combines rules, statistical models, machine learning, graph relationships, behavioral signals, policy actions, validation, and monitoring into a governed fraud-decision portfolio.

Engineer the complete decision portfolio, not a single model

Fraud outcomes usually depend on a sequence of analytics: Data-quality checks, identity resolution, features, vendor scores, rules, models, graph relationships, thresholds, overrides, and response policies. Cicrim helps banks define how those components work together and how each decision can be reproduced.

The design begins with the fraud scenario and available action, then works backward through evidence, timing, customer context, operational capacity, validation, and monitoring.

Design analytics for regulated production

Scenario and decision design

Define the typology, population, timing, action, loss exposure, customer impact, available evidence, ownership, and outcome before selecting an analytic technique.

Data and feature engineering

Establish lineage, quality, latency, entity resolution, historical reconstruction, feature definitions, permitted use, retention, and reproducible development datasets.

Rules, models, and graph

Combine interpretable techniques where each adds value, test segments and thresholds, document limitations, and preserve the full decision path.

Validation and monitoring

Challenge conceptual soundness and implementation, then monitor inputs, outputs, fraud capture, losses, alert yield, friction, overrides, drift, and change.

Operationalize fraud intelligence across the bank

Start with a material scenario, a usable action, and measurable outcomes

Cicrim can help baseline data, current rules and models, decisions, alert operations, losses, customer impact, validation, and monitoring, then sequence a controlled first release.