AI & Machine Learning × Financial Services
Production predictive analytics for banks, insurers, and asset managers: credit risk, fraud, churn, demand forecasting, and anomaly detection with calibrated probabilities, drift monitoring, audit-defensible model governance, and the integration into front-line systems that gets predictions used, not just reported.
The problem
Banks and insurers are awash in data-science notebooks: a fraud model that scored well in cross-validation, a churn model that the executive deck loved, a stress-testing model that ran once. The pattern that fails them is the same: models that are trained but never deployed, deployed but never integrated into front-line workflow, integrated but never monitored for drift, and (when regulators look) unable to produce the model-risk evidence the OCC's SR 11-7 framework requires.
Production financial-services ML needs the parts the notebooks skip: calibrated probabilities, drift monitoring, model cards aligned to model-risk-management standards, fair-lending testing, integration into the loan / underwriting / claims / fraud system, and the operational cadence that keeps models healthy. We design programs around the production half, not the modeling half.
Where it ships
Concrete applications where ai & machine learning unlocks measurable value inside financial services delivery constraints.
+37%
catch rate
Streaming ML for transaction-level fraud scoring. Calibrated probabilities, explainability for case work, drift monitoring against synthetic-identity / ATO / mule-network attack patterns. Model-card evidence for regulators.
Probability-of-default, loss-given-default, and exposure-at-default models for retail and commercial portfolios. SR 11-7 model-risk discipline, ECOA fair-lending testing, and integration into loan origination.
Churn-probability models for deposit and credit-card portfolios with intervention-effectiveness modeling. Tied to the retention workflow. Predictions only matter when they trigger action.
Anomaly-detection layer on top of rules-based AML systems. Reduces false positives, surfaces the patterns rules miss. Investigative-analytics integration for case work and regulator reporting.
Risk-pricing models for P&C and specialty lines. GLM + ML hybrid where regulators demand explainability; full-ML where the line allows it. Rate-filing evidence package, fair-pricing testing.
Deposit run-off forecasting, prepayment models for mortgage portfolios, and ALM scenario analysis. Stress-testing scenarios automated against the modeling cadence regulators want.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to financial services delivery.
Three-week discovery: candidate use cases, model-risk tier per use case, data readiness, regulatory frame, and existing model inventory. Output is a prioritized roadmap with model-risk-management discipline designed in from the start.
Data readiness assessment with explicit gap remediation. Feature store where the use-case portfolio justifies it; clean point-in-time lineage to prevent target leakage. Quality-gated by data-quality SLAs.
Models trained against held-out holdout sets, calibrated for production decisioning thresholds, and stress-tested across known failure modes. Model cards aligned to SR 11-7 evidence requirements; fair-lending and disparate-impact testing where applicable.
Integration into front-line systems (loan origination, fraud platform, claims, retention workflow). Drift monitoring against feature distributions and prediction calibration. Quarterly model review; annual revalidation.
Capabilities
Stack
Compliance overlay
Every financial services engagement carries the evidence collection that procurement and audit teams expect on day one.
Model-risk-management framework applied: model inventory, model cards, validation evidence, change management, and the documentation auditors look for. Independent validation processes supported.
Disparate-impact testing on protected classes; explainability for adverse-action notices; and the rate-comparison evidence regulators expect for credit-decisioning models.
Customer data handling per GLBA Safeguards Rule. Opt-out mechanisms for non-required data uses; documented retention per data class; access controls audited against model-runtime infrastructure.
Selected work
Where this fits
Common questions
We design every production model against the SR 11-7 framework from kickoff. Model cards, validation evidence, change-management records, performance monitoring against KPIs, and the documentation independent validators expect. We've supported model validations for OCC, FDIC, and Fed reviews; the evidence package is configured into the platform, not improvised at audit time.
Yes, with discipline. Adverse-action notices on credit decisions need explainability: SHAP values + reason-code mapping is the standard approach. We design the explainability layer alongside the model, not bolted on. For some highly regulated decisions, GLM-with-feature-engineering wins over full ML on the explainability margin; we recommend per use case.
Yes: streaming ML pipelines with sub-100ms scoring latency, calibrated probabilities, and analyst-explainable feature contributions. Integration into the existing fraud-platform analyst workflow (case management, alert triage, customer-comms) is primary scope, not phase 2. We've replaced rules engines for two regional banks; the framework transfers.
Disparate-impact testing on protected classes (race, gender, age, national origin) at every model release. Documented rate-comparison evidence; explicit thresholds for triggering further review. Where disparate-impact is found, model alterations are designed to preserve predictive power while meeting fair-lending tests, not just removed-and-replaced.
Drift monitoring on feature distributions, prediction calibration, and concept drift signals. Quarterly model review with explicit retraining triggers; annual revalidation. The most common production-ML failure is silent drift. We instrument against it from launch, not after the first incident.
Discovery + use-case prioritization: 3 weeks, $80K–$140K. Single-model production deployment (e.g., fraud, churn): 6–9 months, $500K–$1.4M. Portfolio program (multiple models with shared platform): 9–14 months, $1.5M–$4M. Managed services post-launch: $30K–$120K monthly retainer covering model monitoring, retraining, and validation support.
Talk to us
A senior engineer plus the CORTEX department lead joins the first call, both with prior financial services delivery experience.