Financial Transaction Classification Using Supervised Learning for Banking Risk Assessment
DOI:
https://doi.org/10.15680/IJRCSM.2017.0103001Keywords:
Financial transaction classification, supervised learning, banking risk assessment, machine learning, fraud detection, random forest, gradient boosting, class imbalance, financial securityAbstract
The increasing volume and complexity of financial transactions have created significant challenges for banks in identifying fraudulent activities, assessing financial risks, and ensuring regulatory compliance. Traditional rule-based transaction monitoring systems often struggle to recognize sophisticated fraud patterns and adapt to evolving financial crime techniques. This study examines the application of supervised machine learning techniques for financial transaction classification to improve banking risk assessment. The proposed approach focuses on classifying financial transactions into predefined categories, such as legitimate and suspicious transactions, using historical transaction records and labeled datasets. Relevant features, including transaction amount, transaction frequency, account activity, transaction type, geographical information, and customer behavioral patterns, are considered for model development. Supervised learning algorithms, including logistic regression, decision trees, random forests, and gradient boosting, are discussed as potential classification methods. Model performance can be evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve. Particular attention is given to class imbalance, data privacy, model interpretability, and false-positive reduction. The study proposes a systematic framework for integrating machine learning into banking risk management systems. By enabling faster identification of suspicious transactions and supporting data-driven decision-making, supervised learning can strengthen fraud detection capabilities, improve operational efficiency, and enhance financial security. The findings are expected to provide a foundation for developing reliable, scalable, and responsible transaction classification systems
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