This study explores the application of deep learning techniques for credit card fraud detection, aiming to improve the performance and reliability of anomaly detection methods in financial transactions. The research first used the Isolation Forest algorithm, achieving a detection accuracy of 26% for the top 1000 transactions. Subsequently, the Autoencoder algorithm, an unsupervised deep neural network model, was tested, improving detection accuracy to 33.6% in the best case. These results demonstrate the strong feature extraction capability and adaptability of deep learning models, highlighting their potential to surpass traditional methods. However, the high imbalance in the dataset, with only 0.17% of transactions being fraudulent, poses a significant challenge. The study emphasizes the need for further experimentation and optimization of network structures and hyperparameters to achieve more stable and efficient fraud detection. The paper discusses the challenges of traditional financial transaction monitoring, including large and complex data, high false alarm rates, slow response speeds, and cross-institutional coordination issues. These challenges increase operational costs and regulatory burdens, potentially leading to overlooked suspicious transactions. To address these issues, the application of artificial intelligence (AI) and behavior prediction technology is proposed as a viable solution. AI-based fraud detection uses complex algorithms to analyze activity, identify anomalies, and spot fraud in large datasets. Techniques such as predictive modeling, anomaly detection, natural language processing, machine vision, and continuous learning are discussed. Specific machine learning algorithms like logistic regression, decision trees, random forests, and neural networks are highlighted for their effectiveness in fraud detection. The study also presents experimental results showing that deep learning methods, particularly the Autoencoder algorithm, offer superior performance in fraud detection compared to traditional methods. Despite some fluctuations in performance, the Autoencoder achieved a top detection accuracy of 33.6%, indicating its potential for further optimization. The research underscores the importance of continuous experimentation and improvement in deep learning models to enhance the stability and efficiency of fraud detection systems, ultimately aiding financial institutions in mitigating risks and safeguarding their operations. The application of AI in financial transaction monitoring and behavior prediction is expected to significantly enhance the safety, stability, and efficiency of the financial system in the future.This study explores the application of deep learning techniques for credit card fraud detection, aiming to improve the performance and reliability of anomaly detection methods in financial transactions. The research first used the Isolation Forest algorithm, achieving a detection accuracy of 26% for the top 1000 transactions. Subsequently, the Autoencoder algorithm, an unsupervised deep neural network model, was tested, improving detection accuracy to 33.6% in the best case. These results demonstrate the strong feature extraction capability and adaptability of deep learning models, highlighting their potential to surpass traditional methods. However, the high imbalance in the dataset, with only 0.17% of transactions being fraudulent, poses a significant challenge. The study emphasizes the need for further experimentation and optimization of network structures and hyperparameters to achieve more stable and efficient fraud detection. The paper discusses the challenges of traditional financial transaction monitoring, including large and complex data, high false alarm rates, slow response speeds, and cross-institutional coordination issues. These challenges increase operational costs and regulatory burdens, potentially leading to overlooked suspicious transactions. To address these issues, the application of artificial intelligence (AI) and behavior prediction technology is proposed as a viable solution. AI-based fraud detection uses complex algorithms to analyze activity, identify anomalies, and spot fraud in large datasets. Techniques such as predictive modeling, anomaly detection, natural language processing, machine vision, and continuous learning are discussed. Specific machine learning algorithms like logistic regression, decision trees, random forests, and neural networks are highlighted for their effectiveness in fraud detection. The study also presents experimental results showing that deep learning methods, particularly the Autoencoder algorithm, offer superior performance in fraud detection compared to traditional methods. Despite some fluctuations in performance, the Autoencoder achieved a top detection accuracy of 33.6%, indicating its potential for further optimization. The research underscores the importance of continuous experimentation and improvement in deep learning models to enhance the stability and efficiency of fraud detection systems, ultimately aiding financial institutions in mitigating risks and safeguarding their operations. The application of AI in financial transaction monitoring and behavior prediction is expected to significantly enhance the safety, stability, and efficiency of the financial system in the future.