Platform Agent

Rule Recommendation

Continuous, data-driven rule tuning to catch more fraud with fewer false positives.

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Where it Helps

The Rule Recommendation agent uses AI to analyze historical alert outcomes, confirmed fraud, and false positives, then recommends new rules and threshold changes for transaction monitoring. Each recommendation includes projected catch-rate gain and expected false-positive reduction, so risk teams can prioritize with confidence. Approved recommendations are backtested and can be deployed directly, with every change logged for governance.

Key Capabilities

Pattern Mining

Continuously analyzes historical alerts and outcomes to identify emerging fraud patterns.

Rule Recommendations

Suggests new rules and threshold adjustments targeted at underdetected or noisy patterns.

Impact Projection

Each recommendation includes projected catch-rate gain and expected false-positive reduction.

Backtesting

Recommended rules are tested against historical data before deployment to validate performance.

Priority Ranking

Recommendations are ranked by projected impact so teams tackle highest-value changes first.

One-Click Deployment

Approved recommendations can be pushed directly into the live rule set.

Full Change History

Every recommendation, approval, and deployment is logged for governance and review.

How it Works

Historical Data Analyzed

Agent continuously reviews alert outcomes, confirmed fraud, and false-positive patterns.

Recommendations Generated

Agent proposes new or tuned rules with projected catch-rate and false-positive impact.

Risk Team Reviews

Team reviews backtested results and approves, edits, or rejects each recommendation.

Rules Deployed

Approved rules are pushed to production and monitored for actual performance impact.

Popular Use Cases

Transaction Monitoring

Recommends threshold and velocity rule changes to catch emerging laundering patterns.

Card Fraud Detection

Identifies underperforming rules and suggests tuning to reduce false-positive volume.

New Product Risk

Recommends starting rule sets for newly launched products with limited historical data.

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Rule Recommendation

Continuous, data-driven rule tuning to catch more fraud with fewer false positives.

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