Credit Risk Assessment and Fraud Detection in Financial Transactions Using Machine Learning

Credit risk assessment and fraud detection are crucial tasks in the financial industry, vital to preserving financial organizations' legitimacy and sustainability. Traditional methods often fall short in accurately assessing risk and detecting fraudulent activities in a timely manner. In recent years, machine learning has emerged as a powerful tool for enhancing these processes, leveraging great dimensions of transactional statistics and superior algos for making more informed decisions. This research paper explores the usage of ML techniques in credit risk assessment and fraud detection within financial transactions. The paper begins with an overview of the importance of accurate risk assessment and fraud detection in financial transactions and introduces the role of machine learning in addressing these challenges. A comprehensive literature review is conducted to analyze existing methodologies, algorithms, and research trends in the field. Data acquisition and preprocessing techniques are discussed, emphasizing the importance of clean and relevant data for model training. Feature engineering strategies are explored to extract meaningful information from financial transaction data and enhance the predictive capabilities of machine learning models. Various machine learning algorithms suitable for credit risk assessment and fraud detection are examined, including LR, SVMs, RF, DTs and DNNs. The efficacy of these techniques is evaluated by discussing model metrics for assessment and ensemble approaches for boosting efficiency, with a focus on metrics such as accuracy, precision, recall, and ROC-AUC. The paper presents case studies and experimental results illustrating the application of machine learning models in real -world scenarios, highlighting their effectiveness in improving risk assessment and fraud detection processes. Additionally, difficulties such as imbalanced datasets, comprehensibility of the model and adherence to regulations are discussed, along with potential research directions and future trends in the field. In conclusion, this research emphasizes the transformative potential of machine learning in credit risk assessment and fraud detection within financial transactions. By leveraging advanced algorithms and data -driven approaches, financial institutions can enhance their decision -making processes, mitigate risks, and safeguard against fraudulent activities, ultimately contributing to a more secure and resilient financial ecosystem.