Why Should You Combine Machine Learning and Graph Tech to Build Your Fraud Detection System?

Insurance companies face challenges in detecting fraudulent claims due to their reliance on assumptions of rational client behavior, leading to losses when fraudulent activities go unnoticed. To address this, companies are increasingly integrating machine learning with graph technology to enhance fraud detection systems. Machine learning offers adaptable solutions by analyzing large datasets to identify patterns and anomalies, but its effectiveness is limited by the imbalance and complexity of data. Graph technology complements this by revealing hidden connections and enriching data features, making it possible to detect fraudsters' networks and interactions that traditional data analysis might miss. Graph algorithms like community detection and PageRank can uncover relationships and generate insights that improve machine learning models' predictive accuracy. By combining machine learning's data-driven approach with graph technology's ability to map intricate relationships, insurance companies can create robust fraud detection systems that are both powerful and explainable, offering insights that are crucial for legal and practical decision-making.