Overview

SecureMint Financial is a FinTech company with a digital-first approach for online payment processing and digital banking. The company was facing increased challenges with the accurate identification of fraudulent transactions. Their rules-based fraud monitoring system had too often identified ‘fraudulent’ legitimate transactions, resulting in costs to operations, customer friction, and lost revenues. With growing volumes of transactions and increasingly sophisticated fraudulent events, SecureMint Financial partnered with Bacancy to develop AI agents for fraud detection. These AI agents were aimed at minimizing false positives, increasing the accuracy of fraud case detection, enabling adaptive learning, and more effectively preventing real-time fraud. These AI agents were designed to analyze transactional patterns, behavioral signals, and risk scores, enabling intelligent fraud alerts with greater precision.

Technical Stack

  • Python
  • TensorFlow
  • Scikit-learn
  • PostgreSQL
  • AWS

    • Industry

    FinTech

  • region

    • Region

    North America

  • project-size

    • Project Size

    Non-Disclosable

Highlights

Improved fraud detection accuracy with AI-driven anomaly detection

Reduced the false positive rate through behavioral and transactional analysis

Scalable and adaptive fraud detection models using continuous monitoring

Reduced review time and manual investigation costs

Challenges & Solutions

High false positive rates because of static rule-based systems

  • Solution: Bacancy developed adaptive machine learning models using TensorFlow and Scikit-learn to analyze transaction behavior, spending patterns, and risk anomalies. These AI agents dynamically changed risk thresholds, which reduced false positives while keeping fraud detection accuracy high.

Inability to detect evolving fraud patterns

  • Solution: Bacancy shows its AI agent development expertise by enabling continuous learning pipelines for the AI agents, with which they could retrain on the newer fraud signals and transaction feedback. It helped identify emerging fraud tactics and improved fraud detection precision on unseen patterns.

Manual review overload and delayed investigations

  • Solution: Automated risk scores for every transaction were generated based on anomaly detection, geolocation, device fingerprinting, and behavioral profiling. It helped internal teams concentrate resources only on high-risk cases by reducing manual review time and operational cost.

Limited visibility into fraud detection and reporting

  • Solution: Real-time dashboards and in-depth analytics reports were developed to monitor fraud trends, risk scores, and alert performance. This helped business teams make data-driven decisions with greater transparency and governance.

Core Features

  • AI-powered fraud detection models
  • Anomaly detection with behavior and transaction insights
  • Automated risk assessment and adaptive learning
  • Dashboard for fraud trends and investigative insights

Result

  • no.-of-resources

    • Number of Resources

    5

  • time-frame

    • Time Frame

    February 2025 to September 2025

Experience With Bacancy

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