Banking & Financial Services

Financial institutions face dual pressures: maximizing profitability while managing risk and regulatory compliance. Causal machine learning delivers both by identifying true drivers of credit risk, fraud, customer value, and retention while producing interpretable models that survive regulatory audit.

We applyCausal Machine Learningto drive innovation in the Banking & Financial Servicessector.

Credit risk modeling moves beyond black-box predictions to understand what causal mechanisms determine default. [Instrumental variable methods](/research#post-selection-inference) isolate genuine effects of debt-to-income ratios, employment stability, and credit history while controlling for unobserved borrower quality. This prevents discriminatory lending practices while improving predictive accuracy. Fraud detection leverages causal analysis to identify genuine fraud patterns separate from statistical anomalies, reducing false positives that create poor customer experiences. Customer lifetime value prediction anchors on causal drivers of retention and cross-sell propensity, enabling targeted retention campaigns that maximize impact per marketing dollar. Churn modeling identifies which customer segments are at genuine risk and what interventions (rate changes, product bundling, service improvements) actually reduce attrition.

Banks deploying our platform can reduce credit losses through improved risk assessment, improve fraud detection false positive rates, and increase retention efficiency through precision targeting. Regulatory compliance improves because models are interpretable and defensible—you can explain to regulators exactly why a customer was declined and provide evidence the decision criteria don't create disparate impact. Mortgage lenders improve approval rates for qualified borrowers by removing statistical discrimination.

Our solutions integrate with core banking systems, credit bureaus, and regulatory reporting platforms.