The Challenge

Financial Institutions' AML and fraud monitoring systems generate thousands of alerts every year, yet the vast majority turn out to be false positives. Analysts spend valuable time investigating low-risk alerts while genuinely suspicious cases can remain buried in large, unprioritized queues.

The root cause is structural. Traditional rule-based monitoring relies on static thresholds that cannot effectively distinguish routine customer behavior from genuinely suspicious activity. To avoid missing potential threats, institutions often over-alert, creating investigation backlogs, analyst fatigue, and slower response times.

MAQ Software's AI-Powered Fraud Alert Triage validates an AI-powered prioritization layer on top of your existing monitoring systems. By learning from historical alert outcomes, the solution ranks alerts based on risk, helping analysts focus on the cases most likely to require investigation.

Key Questions

From the alerts your team investigated last month, what percentage were ultimately determined to be false positives?
Do analysts know which alerts are most likely to represent suspicious activity before beginning their investigations?
How much investigator time is spent reviewing alerts that never become cases?
Could high-risk alerts be delayed because they are buried in large investigation queues?
Would explainable risk scores improve confidence in alert prioritization decisions?

Our Approach

Weeks 1–2: Data Foundation in Microsoft Fabric — Connect and unify historical alert outcomes, transaction data, and customer profile data into Microsoft Fabric, covering one defined alert type and a fixed historical window.
Weeks 3–4: Build & Test the Risk Score — Use Azure Machine Learning AutoML to analyze historical outcomes and identify which factors most strongly predict suspicious activity versus false positives, producing an explainable risk score.
Weeks 5–6: Validate & Deploy the Queue — Validate the risk score against historical outcomes and surface a prioritized alert queue in Power BI, enabling analysts to focus on the highest-risk cases first.

Deliverables

Unified Historical Data Layer — Alert, transaction, and customer profile data connected in Microsoft Fabric for the scoped alert type.
Validated Risk Scoring Model — An explainable AutoML-generated score identifying the factors most associated with higher-risk alerts.
Prioritized Alert Queue (Power BI) — A re-ranked view of alerts by risk score, allowing investigators to work the highest-priority cases first.
Validation Results — Performance metrics showing how effectively the model separates higher-risk alerts from likely false positives.
Scale-Out Plan — A roadmap for extending the approach to additional alert types and future real-time scoring scenarios.

Business Impact

Give analysts a queue ordered by actual risk instead of a flat list where high-risk alerts may remain hidden.
Reduce investigation effort spent on low-value alerts and false positives.
Improve analyst productivity and investigation efficiency.
Support explainable, data-driven prioritization decisions.
Validate the approach using historical outcomes before production deployment.
Establish a scalable foundation for future AML and financial crime monitoring initiatives.

Who Benefits

Users: AML Analysts, Compliance Teams, Fraud Investigation Teams, Financial Crime Operations Leads

Decision Makers: Chief Compliance Officer, Chief Risk Officer, Head of Financial Crime, VP of Fraud & AML Operations

Prerequisites

Read access to historical alert data with known investigation outcomes.
Access to the transaction and customer profile data associated with those alerts.
Microsoft Fabric and Azure Machine Learning provisioned within the organization's environment.
Availability of AML and compliance stakeholders to validate results during the engagement.

Why MAQ Software

Built to Be Explainable — Risk scores are derived from your institution's historical outcomes rather than black-box algorithms, making results easier to understand and justify.
Microsoft-Native Solution — Built using Microsoft Fabric, Azure Machine Learning, and Power BI to accelerate implementation and adoption.
Rapid Time to Value — A focused six-week engagement designed to deliver measurable outcomes quickly.
Scalable Foundation — Provides a roadmap for extending prioritization capabilities across additional alert categories and future real-time scoring scenarios.

Contact us: CustomerSuccess@MAQSoftware.com to explore how AI-powered alert prioritization can help your organization focus investigation efforts where they matter most.

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