Abstract
Neural networks are increasingly being used in predictive modeling within the finance and insurance sectors. These models have the ability to process large and complex datasets, identifying patterns that are difficult for traditional models to capture. This article explores the application of neural networks in financial risk assessment, fraud detection, customer segmentation, and pricing strategies in insurance. Additionally, the article discusses the challenges associated with the integration of neural networks in these industries, including model interpretability, data privacy concerns, and regulatory considerations.
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