AI a regulator can sign off on
Risk, fraud and underwriting intelligence across banking and insurance — exposure modeled, anomalies caught in real time, and every decision explained line by line to ECB and EBA examiners.
A Hominis app module · your financial services & insurance data, app-ified
Sharper decisions your examiners can audit
Risk, fraud and underwriting across banking and insurance — explainable, fair and real-time.
Credit risk & underwriting
Legacy scorecards replaced with explainable models that read traditional financials alongside alternative data for a holistic view of borrower risk — sharper discrimination, no black box.
30% better risk prediction (Gini)
Real-time fraud & AML detection
Transaction streams scored as they clear, so anomalous patterns and laundering typologies surface before money moves — not in a next-day batch report.
Sub-second transaction scoring
Fairer, wider underwriting
Fairness constraints embedded in training itself — qualified underserved applicants approved without loosening portfolio quality.
+18% underserved approvals
Insurance claims & exposure modeling
Claims triaged and exposure modeled across the book, so reserving, pricing and catastrophe risk rest on one current picture instead of stale actuarial cuts.
Portfolio-wide exposure view
Explainable decisions for examiners
Every credit, fraud and pricing decision carries a SHAP-based reason code — the adverse-action and model-governance evidence regulators ask for, decision by decision.
Per-decision SHAP reason codes
Fair by construction, auditable by design
The two innovations that carried it into production at a regulated bank.
Fairness as a training constraint, not a patch
A constrained-optimization framework jointly maximizes prediction accuracy and minimizes demographic-parity gaps — so bias is engineered out during training rather than audited after the fact. That is what let the model widen approvals 18% while portfolio quality held.
Explainability examiners can interrogate
Gradient-boosted models paired with SHAP explanations answer the ECB and EBA explainability mandate decision by decision, while a live dashboard tracks performance, fairness and drift. That auditability — not raw accuracy — is what carried the system into production.
One method, tuned for financial services & insurance
Assess, Transform, Sustain — the cycle every organization runs, dropped one level deeper for your sector's pains and sticky aspects.
Assess
Model-risk, data-lineage and fairness audit against ECB/EBA expectations
We inventory legacy scorecards, rules engines and the data feeding them — mapping lineage, identifying protected-attribute proxies, and benchmarking each model's discrimination (Gini) and demographic-parity gaps. The output is a ranked roadmap of risk, fraud and underwriting use cases scored by value, regulatory exposure and data readiness, plus the model-governance gaps to close first.
Transform
Explainable, fairness-constrained models in core banking at real-time speed
We co-build the highest-value model — credit, fraud or underwriting — with fairness constraints baked into the objective and SHAP reason codes wired into every output, then integrate it with core banking systems at the sub-second speeds production scoring demands. Each release ships with model cards, adverse-action logic and human-in-the-loop overrides so it clears model validation and second-line review.
Sustain (AIOps)
Live drift, fairness and performance monitoring for models under examination
Every model in production runs under a dashboard tracking discrimination, demographic-parity metrics and population drift in real time, with scheduled retraining and incident response. When a portfolio shifts or an examiner asks, the fairness audit trail, performance history and reason-code evidence are already there — accountability that holds up between exam cycles, not just at launch.
Fairer credit decisions, sharper risk discrimination
Explainable credit-risk AI — prediction accuracy up 30%, bias down, EUR 12M annual savings.