Predictive risk modeling with real-time anomaly detection and intelligent scoring. AI agents continuously assess credit risk, fraud patterns, operational threats, and compliance violations. Deploy in 4 weeks and reduce losses by 70%.
Identify threats before they become losses
Detect fraud, credit defaults, and operational risks earlier. AI models predict threats months in advance with 95% accuracy.
Continuous assessment across transactions, counterparties, and portfolios. Risk scores update in milliseconds as new data arrives.
From data integration to production risk models. Train on historical data, validate against outcomes, and deploy continuously learning systems.
Predictive analytics for every risk dimension
AI models trained on historical losses predict future defaults, fraud, and operational failures. Ensemble methods combine credit scores, behavioral patterns, and macroeconomic indicators for superior accuracy.
Identify unusual patterns in real-time—spikes in transaction velocity, geographic inconsistencies, behavioral deviations. Unsupervised learning detects novel threats without pre-defined rules.
Instant risk assessment for every transaction, customer, and counterparty. Scores update continuously as behavior, credit, and market conditions change. API-accessible for integration into workflows.
Model risk exposure under stress scenarios—recession, rate hikes, supply chain disruptions. AI generates thousands of scenarios and quantifies impact on portfolios, liquidity, and capital requirements.
Roll up risk from individual exposures to portfolio, business unit, and enterprise levels. Identify concentration risk, correlated exposures, and hidden vulnerabilities across complex portfolios.
Graph neural networks detect fraud rings, synthetic identities, and coordinated attacks. Link analysis reveals hidden connections between seemingly unrelated transactions and entities.
AI-powered risk management for every function
Predict default probability for consumer and commercial loans. Models incorporate traditional credit bureau data plus alternative signals—transaction history, social media, employment patterns. Reduce charge-offs by 50%.
Real-time fraud scoring for payments, account openings, and insurance claims. Detect account takeovers, synthetic identities, merchant fraud, and organized crime. Block fraud while minimizing false positives.
Predict system failures, process breakdowns, and human errors. Models trained on incident history identify high-risk processes, vulnerable systems, and error-prone workflows before failures occur.
Monitor vendor, supplier, and partner financial health. Early warning signals for bankruptcy, liquidity crises, and business disruptions. Assess concentration risk and identify alternative suppliers.
Traditional risk model development takes 6-12 months. Strongly.AI delivers production-ready predictive models in weeks with continuous retraining as new data arrives.
Typical financial services deployment
See how AI-powered risk assessment transforms reactive monitoring into predictive intelligence