Introduction to Our AI-Driven Solution

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A leading financial institution was facing escalating challenges in detecting fraud amidst increasingly sophisticated tactics. Traditional rule-based systems were proving ineffective, often failing to capture subtle, emerging fraud patterns. Recognizing the need for a proactive and AI-driven solution, the institution collaborated with Phenx Machine Learning Technologies to implement an advanced fraud detection framework that leverages both unsupervised and supervised machine learning.​

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Success Story: Uncovering Hidden Threats with Unsupervised AI for Financial Fraud

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Phenx’s AI-based fraud detection solution integrates diverse methodologies to address known and unknown fraud patterns across vast datasets. This multi-layered approach includes:

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• Unsupervised Models for Anomaly Detection

  • Clarity Model: This model uses the Clustering-Based Local Outlier Factor (CBLOF) algorithm to identify high-risk accounts by analyzing key indicators, including inquiry frequency and email fraud scores. It’s especially effective in spotting accounts with unusual activity patterns. Clarity is a score provided by Experian.

  • Neuro-ID Model: Also leveraging the CBLOF algorithm, this model examines user interaction data—such as typing fluency and interaction time—to detect abnormal behaviors that may signal fraud. By assessing user actions, the Neuro-ID model identifies subtle signs of fraud, including automated entries or pauses in entering financial details. Neuro-ID is a behavioral-based fraud detection score focused on user activity within the browser.

  • Blended Variables Model: Employing Histogram-Based Outlier Scoring (HBOS), this model combines diverse features, such as income type and device type, to create a holistic view of risk. The model excludes external credit scores, focusing instead on behavioral and transactional data, making it highly adaptable to various types of fraud.

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• Supervised Model for Enhanced Accuracy

  • XGBoost Supervised Model: Trained on labeled fraud indicators, this model prioritizes high-risk accounts, reducing false positives and reinforcing insights from unsupervised models. It utilizes fraud indicators like account stability and installment loan inquiries.

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• Unified Score Model for Streamlined Risk Assessment

  • Weighted Score Aggregation: Combines outputs from all unsupervised models to deliver a single, unified fraud risk score. This consolidated score enhances detection accuracy, with a strong concentration of fraud cases in the top deciles.

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Results

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Through rigorous validation, Phenx’s fraud detection models demonstrated exceptional accuracy and reliability, capturing complex fraud patterns that eluded traditional systems. Key outcomes included:

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• High Fraud Capture Rate: Decile lift charts demonstrate the model’s ability to rank-order accounts by risk. The charts display the model’s success in capturing a higher percentage of fraudulent accounts within the top deciles, helping to prioritize cases with the highest risk.

• Improved Operational Efficiency: By automatically out-sorting high-risk cases, the model allowed the institution to concentrate resources on accounts with the highest fraud probability, reducing operational strain and false positives.

• Scalability and Adaptability: The solution successfully adapted to the institution’s large datasets and was able to detect new fraud patterns without retraining, thanks to the unsupervised models.

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Why This Matters for Your Business

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This case study demonstrates the powerful impact of incorporating unsupervised AI models into your fraud detection strategy. With Phenx’s solution, businesses can expect to achieve:

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• Proactive Fraud Detection: Uncover hidden patterns and detect fraud before it impacts your bottom line, reducing risk across your operations.

• Enhanced Operational Efficiency: Prioritize investigations on high-risk accounts and reduce false positives, allowing your team to focus on genuine threats.

• Scalability and Flexibility: Adapt seamlessly to growing data volumes and evolving fraud tactics, ensuring your fraud prevention framework remains robust and responsive.

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With Phenx’s advanced AI, financial institutions can stay ahead of fraud trends, safeguard their assets, and optimize resources for maximum impact.