Enhancing Fraud Detection using Predictive Analytics and Machine Learning

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Businesses face significant challenges in detecting fraud during the reconciliation process due to the complexity and volume of transactional data. Traditional methods often rely on manual checks or basic rule-based systems that can miss subtle signs of fraudulent activities. This proposal introduces the use of predictive analytics and machine learning to enhance fraud detection during reconciliation. By analyzing historical data and identifying anomalous transaction patterns, predictive models can flag potentially fraudulent activities in real-time, reducing the risk of financial loss. Machine learning algorithms, trained on past data, will continuously adapt and improve, enabling the system to detect new fraud tactics as they emerge. The expected outcomes include faster detection, higher accuracy in identifying fraud, and improved operational efficiency.