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Machine learning-based fraud detection in the insurance sector: enhancing efficiency and preserving trust: credit agricole Portugal case
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Resumo(s)
The use of Machine Learning algorithms to identify insurance fraud, a major financial
burden on the sector, is examined in this thesis. Working together with Grupo Crédito Agrícola
Vida, we made use of a large dataset for forecasting. The study evaluates the effectiveness of
sequential neural networks, random forests, and logistic regression in spotting fraud in insurance claims. It addresses the necessity for ethical considerations in automated decision-making
and highlights the synergy between human expertise and machine learning. The study emphasizes how Machine Learning may improve operational effectiveness and fraud detection accuracy, which can strengthen the insurance industry's credibility and integrity.
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Machine learning Fraud Data Data analysis Data processing Artificial intelligence Business analytics