Fraud Detection
Score every transaction for fraud risk in under 60ms. EkamFlow's private AI model detects payment fraud, account takeover, and suspicious patterns in real time — without a dedicated fraud team.
What is Fraud Detection?
Fraud detection uses machine learning to identify fraudulent transactions, account takeovers, and suspicious behavior in real time. Unlike rule-based systems that rely on static thresholds, ML-powered fraud detection learns evolving patterns from your transaction data and adapts as fraud tactics change.
Building an in-house fraud detection model requires specialized ML expertise, real-time serving infrastructure, and constant retraining as fraud patterns evolve. Most fraud teams rely on expensive third-party tools that don't understand their specific business context.
EkamFlow trains a private fraud detection model on your transaction history and behavioral signals. Every transaction gets a fraud risk score in under 60ms — fast enough for payment authorization. The model learns your specific fraud patterns, not generic industry data, and improves continuously without manual intervention.
How EkamFlow does it
Sub-60ms fraud scoring
Fast enough for payment authorization flows. Score every transaction at checkout, login, or account change without adding latency your customers notice.
Adaptive to your fraud patterns
Your model learns from your specific transaction data — card-not-present fraud, account takeover signals, and AML patterns unique to your business.
No fraud team required
Replace rule-based fraud systems and expensive third-party tools. One private model covers payment fraud, ATO, and AML — no ML engineers needed.
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