How Plurikit Credit cut false positive fraud alerts by 72% in 30 days.

The challenge

Luminos Credit, a consumer lending platform, was generating over 1,800 fraud alerts per week from their rule-based detection system. Of those, approximately 72% were false positives — legitimate customer transactions incorrectly flagged as suspicious.

The result was a fraud operations team spending the majority of their time reviewing and clearing cases that weren't fraud, while genuine fraud slipped through in the noise. Customer complaints about blocked transactions were rising, and the team was burning out.

The solution

AgenticOS replaced Luminos Credit's static rule engine with an adaptive intelligence layer:

• Behavioural profiling — agents built dynamic transaction profiles for each customer, learning spending patterns, typical amounts, merchant categories, and timing

• Contextual scoring — every alert was scored in context, weighing the transaction against the customer's individual history rather than population-level thresholds

• Network analysis — agents mapped relationships between accounts to identify genuine fraud clusters versus isolated anomalies

• Continuous model updates — fraud detection logic updated automatically as new patterns emerged, without manual rule maintenance