Optimization of Financial Fraud Risk Identification System Based on Machine Learning

Abstract

With the ongoing digital transformation of the financial sector, the landscape of financial fraud has become increasingly complex and sophisticated, presenting significant challenges to traditional fraud detection systems, which often suffer from low accuracy and delayed response times. Financial fraud risk assessment systems powered by machine learning technology can automatically detect anomalous patterns by analyzing large volumes of historical transaction data, significantly enhancing the precision and timeliness of detection. Currently, machine learning algorithms such as deep learning, decision trees, and support vector machines serve as the core tools for improving detection efficiency and predictive capability. Despite these advancements, challenges such as inconsistent or poor-quality data, model overfitting, and the selection of relevant features continue to constrain system performance and generalizability. Addressing these issues requires comprehensive optimization strategies, including advanced data preprocessing, robust feature engineering, algorithmic fine-tuning, and model validation techniques, to strengthen the system's ability to identify and anticipate fraudulent behaviors. This article proposes a series of targeted improvement measures to tackle the current limitations in financial fraud detection systems and explores the future potential of machine learning technologies to enable proactive, intelligent, and adaptive approaches to financial fraud prevention.