Success Story
Client Introduction
A U.S.-based regional bank operating a high-volume Third-Party Payment Processor (TPPP) ecosystem required a scalable AML monitoring framework to manage complex transaction flows across merchants, counterparties, and beneficiary accounts.
As transaction volumes increased, the bank sought enhanced visibility into behavioral patterns while ensuring alignment with regulatory expectations around explainability, traceability, and governance.
Problem Statement
The bank’s existing monitoring framework faced multiple structural and analytical limitations:
- Fragmented customer and counterparty identifiers across transaction flows
- Monitoring logic focused on individual transactions rather than aggregated behavioral patterns
- Limited ability to detect anomalies such as dormancy bursts, rapid inflow–outflow cycles, and counterparty churn
- Absence of statistical baselines to distinguish normal vs abnormal activity
- Limited traceability between transaction data, rule logic, and alert outputs
- Increasing expectations for explainability and governance under SR 11-7
The bank required a monitoring framework capable of detecting complex behavioral risk while remaining transparent, interpretable, and regulator-ready.
Solution Offered
Anaptyss implemented an integrated AML monitoring framework combining rule-based detection, behavioral analytics, and governance-aligned architecture on the bank’s TPPP production data.
The solution transformed transactional data into structured behavioral indicators through standardized entity-level monitoring, aggregation layers, and statistical analysis across multiple time horizons.
Detection logic was organized into defined behavioral risk categories, supported by dynamically recalibrated baselines and explainable alert generation. Governance and traceability were embedded into the framework to align with SR 11-7 model risk management expectations.
Key Capabilities Delivered
- Standardized canonical identifiers enabling consistent monitoring across customers and counterparties
- Daily account-level aggregation layers normalizing transaction activity
- 60+ detection signals across 11 behavioral risk categories
- Behavioral analytics across 7-, 30-, 90-, and 180-day monitoring windows
- Z-score anomaly detection, rolling averages, and percentile-based baselines
- Detection of velocity, volatility, lifecycle, and structural transaction patterns
- Dynamically recalibrated thresholds adapting to evolving activity
- Explainable alerts linking transactions, behavioral metrics, and rule triggers
- End-to-end traceability from source transaction data to alert outputs
- Governance-aligned documentation supporting SR 11-7 compliance
- Power BI dashboards providing monitoring visibility and oversight
![](/wp-content/uploads/2025/06/mob-business-outcomes.webp")
Business Outcomes
- Generated 4,527 behavioral alerts across 11 detection categories (Q3 2025)
- Enhanced visibility into complex fund flows across TPPP entities
- Enabled detection of dormancy bursts, counterparty churn, and rapid fund movement patterns
- Shifted monitoring from transaction-level triggers to behavioral pattern detection
- Strengthened traceability and explainability for regulatory review
- Improved investigator prioritization through structured and interpretable signals
- Established a scalable, governance-aligned AML monitoring framework
Want to learn more or need a solution?
Write to us: info@anaptyss.com
Download PDF
