Build regulatory-compliant AI for fraud detection, risk management, loan processing, and customer intelligence: with explainability, audit trails, and fairness testing as standard requirements.
Traditional rule-based fraud detection flags 90% false positives, wasting investigation resources and frustrating legitimate customers with declined transactions.
→ ML fraud detection reduces false positives 70% while catching 95%+ of actual fraud
Regulators require transparent decision-making. "Black box" AI creates compliance risk, legal liability, and audit failures.
→ Explainable AI provides auditable reasoning for every decision with regulatory documentation
Historical lending data contains demographic biases. Traditional models perpetuate discrimination, creating legal and reputational risk.
→ Fair lending AI with bias testing ensures equitable decisions while maintaining predictive accuracy
Document review, income verification, and underwriting take days. Manual processes are expensive, inconsistent, and error-prone.
→ AI-powered automation processes loans 80% faster with human oversight for complex cases
We implement AI systems that reduce risk and improve efficiency while maintaining regulatory compliance
Machine learning analyzes transaction patterns, device fingerprints, location data, and behavioral signals to identify fraud in milliseconds. Adaptive models learn new fraud patterns without manual rule updates. Explainable scoring for disputed transactions.
Advanced models assess creditworthiness using traditional and alternative data sources. Fair lending algorithms tested for demographic bias. Explainable credit decisions with adverse action reasons. Continuous monitoring for model drift and fairness.
AI extracts data from documents, verifies income and employment, assesses property values, and flags exceptions for human review. Generative AI creates loan summaries and decision memos. Maintains human oversight for final approval.
Machine learning predicts customer needs, attrition risk, and product fit. Generative AI creates personalized financial advice and product recommendations. Privacy-preserving analysis maintains customer trust and regulatory compliance.
Community bank | $8B in assets | 450K customer accounts
Rule-based fraud detection flagged 92% false positives, costing $2.8M annually in manual review while missing 15% of actual fraud. Legitimate customers were frustrated by declined transactions, and fraud losses were increasing 18% year-over-year as criminals evolved tactics.
We deployed real-time machine learning fraud detection analyzing over 200 transaction features including behavioral patterns, device characteristics, and network effects. The explainable AI system provides fraud investigators with clear reasoning and evidence for every alert, meeting regulatory requirements for model transparency.
"Posvo's fraud detection AI transformed our risk management. We're catching fraud we would have missed while dramatically reducing false alarms that frustrated customers. The explainable AI gives our investigators confidence and meets our auditors' transparency requirements. This is the right way to deploy AI in banking."
— Chief Risk Officer
Every AI system includes audit trails, model documentation, explainability reports, and fairness testing. We understand FCRA, ECOA, Dodd-Frank, and emerging AI regulations.
No black boxes. Our AI provides clear reasoning for every decision, adverse action codes for denials, and confidence levels. Models are auditable and defensible to regulators.
Systematic fairness testing across demographics. Disparate impact analysis. Bias mitigation without sacrificing predictive performance. We help you serve all customers equitably.
Start with a strategic conversation about where AI fits in your financial services organization, how to maintain regulatory compliance, and what outcomes matter most for risk and customer experience.
Initial consultations are confidential and require no commitment.
A strategic conversation about where organizational intelligence fits in your business, what it should accomplish, and how to build it as lasting capability.