AI-Driven Fraud Detection & AML Using Graph-Based Machine Learning

Project category

Project Name :

FinSecure Analytics

Industry :

Finance

Country :

United States

Duration :

4 Months

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Project Overview

A leading US-based financial services firm faced increasing challenges with fraudulent transactions, regulatory compliance, and high false-positive rates in Anti-Money Laundering (AML) monitoring. The existing rules-based system generated numerous alerts that required manual investigation, leading to inefficiencies and operational costs.

Leveraging its data analytics consulting services, X-Byte Analytics implemented an AI-powered Fraud Detection & AML Solution leveraging anomaly detection and graph-based machine learning techniques. The solution improved detection accuracy, prioritized high-risk transactions, and reduced false positives by 35% within four months — enabling faster investigations and stronger compliance adherence.

Key KPIs Tracked

The Fraud Detection & AML solution monitored critical metrics for risk management and operational efficiency:

Fraud Detection Accuracy - Improve precision identifying suspicious transactions.
Alert Resolution Time - Speed up investigation & case handling.
Suspicious Transaction Identification - Detect anomalies in real-time.
AML Compliance Score - Strengthen regulatory adherence.
High-Risk Transaction Prioritization - Rank alerts by risk severity.
Case Investigation Efficiency - Reduce manual review workload.
Detection of Hidden Networks - Discover linked accounts & transaction patterns.

Solution Offered — AI-Powered Fraud Detection & AML:

We delivered a data-driven fraud detection solution through a 5-step structured approach:

Step 01

Data Integration & Preparation

Unified customer records, transactions, and KYC data into a centralized data lake to establish a clean foundation for analysis, enabling better fraud detection accuracy and reduced false positives through improved data quality.

Step 02

Anomaly Detection & Graph-Based Model Development

Developed ML models combining anomaly detection and graph analytics to identify abnormal behaviors and hidden networks of linked accounts. This significantly improved the detection of suspicious transactions, risk prioritization, and fraud detection accuracy.

Step 03

Dashboard Design & Visualization

Built real-time Power BI dashboards displaying alerts, risk scores, workflow progress, and relationship graphs, enabling faster insights and reducing alert resolution time while improving case investigation efficiency.

Step 04

Automation & Real-Time Monitoring

Automated scoring pipelines and alert triggers ensured continuous monitoring, proactively flagging high-risk activities and improving prioritization of investigations while maintaining a high AML compliance score.

Step 05

Insight Sharing & Compliance Collaboration

Enabled collaborative reporting across fraud, risk, and compliance teams with workflow tools and exportable documentation — strengthening regulatory adherence and boosting audit-ready compliance performance.