InsightAI provides AI‑driven fraud and AML detection for regulated financial institutions, using graph intelligence, behavioral analytics, and device fingerprinting to spot illicit activity earlier. Its platform automates case summarization, entity linking, and risk scoring, cutting Level‑2 investigation effort by up to 70% while reducing alert volume by half and speeding review times by 70%. The solution delivers explainable, proactive risk insights that lower operational costs and improve compliance outcomes.
Funding
Funding not disclosed
Founders
Product
Problem
Financial institutions face overwhelming volumes of fraud and AML alerts, many of which are false positives, leading to high investigation costs and delayed response to genuine threats. Traditional rule‑based systems struggle to detect sophisticated, coordinated attacks before loss occurs.
Solution
InsightAI delivers AI‑driven fraud and AML intelligence that combines graph analytics, behavioral profiling, device fingerprinting, and document forensics to identify illicit activity early. Its platform automatically links related entities, summarizes cases, and assigns risk scores, enabling investigators to focus on high‑risk alerts. Explainable risk insights and real‑time scoring reduce false positives by more than 50% and cut Level‑2 investigation effort by up to 70%, accelerating case closure by 70%. The solution is offered as a secure SaaS or isolated on‑premise deployment, supporting high‑volume, regulated environments.
Target Audience
Primary customers are banks, payment platforms, insurers, and fintech firms that operate regulated AML and fraud monitoring programs and need to reduce alert noise while maintaining compliance.
Features
- Graph‑based relationship modeling that maps devices, users, transactions, and documents in real time
- Device intelligence layer creating persistent fingerprints from network, browser, and behavioral signals to detect pre‑transaction fraud
- AI‑powered AML case engine with automated entity linking, case summarization, and contextual risk scoring
- Document forgery detection using multi‑modal AI models to spot metadata anomalies, pixel‑level tampering, and cross‑data inconsistencies
- Low‑latency risk analytics delivering millisecond‑level fraud scores for high‑throughput payment rails
- Explainable AI outputs and audit trails designed for regulator review and compliance reporting
- Flexible deployment options (SaaS or on‑premise) with RBAC, SOC 2 Type II, ISO 27001 compliance, and 99.9% uptime