A Hyperparameters Tunned ML Algorithm for Fraud Identification in Banking and Financial Transactions


Authors : Srinivasa Rao Bogireddy; Haritha Murari

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/mrsdfwup

Scribd : https://tinyurl.com/3jfyu44c

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG458

Abstract : Banking, a pioneering industry, is experiencing rapid growth, leading to the adoption of cashless transactions. Digital banking offers better service quality but has faced challenges from fraudulent activities. Since the banking industry is expanding quickly throughout the globe, using cash for payments is becoming less common. Instead, people are using cashless transactions. Digital banking customers receive higher-quality services in money transfers, cashless payments, credit cards, and prepaid cards. Nonetheless, the fraudulent activities of scammers have drawn attention to the security of digital banking, as a lack of adequate protection has discouraged many users from using the service up to this point. Even though fraud is not a new problem, its associated actions always cause billions of dollars' worth of annual losses to the world economy. Fraudulent actions carry a wide range of severe financial hazards that might jeopardize an economy's profitability and reputation. The study aims to introduce an efficient hyperparameter-tuned machine learning approach to detect fraud in banking and financial transaction systems. Proper preprocessing and application of feature engineering, such as outlier rejection, null value handling, standardization, and parameter tuning, have been incorporated with the approach. Later, the Extreme gradient boosting model was trained with tunned parameters and evaluated with test data. The model demonstrated praiseworthy performance, having 99.63% accuracy. Extensive analysis using feature selection, confusion matrix, roc, and tunning evaluation graph was conducted to detect fraud in financial transactions.

Keywords : XGB, Financial Baking, Fraud, Grid Search, Tuning.

References :

  1. Tomasic, R., & Akinbami, F., “The role of trust in maintaining the resilience of financial markets”, Journal of corporate law studies, Vol. 11, pp. 369-394, 2011.
  2. Bolton, R. J., & Hand, D. J.,” Statistical fraud detection: A review”,  Statistical science, Vol. 17, pp. 235-255, 2002.
  3. Stojanović, B., Božić, J., Hofer-Schmitz, K., Nahrgang, K., Weber, A., Badii, A., ... & Runevic, J., “Follow the trail: Machine learning for fraud detection in Fintech applications”, Sensors, Vol. 21, pp.1594, 2021.
  4. Ryman-Tubb, N. F., Krause, P., & Garn, W., “How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark”, Engineering Applications of Artificial Intelligence, Vol. 76, pp. 130-157, 2018.
  5. M. S. H. Talukder, A. H. Nur, S. Zaman, M. R. Noor, M. A. U. Khan and F. Amir, "Optimizing Diabetes Prediction Accuracy: A Comprehensive Approach with Advanced Preprocessing and Diverse Machine Learning Classifiers," 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), pp. 1-6, 2024.
  6. M. S. Hasan Talukder, A. Krishno Sarkar and M. Nuhi-Alamin, "An Improved Model for Nutrient Deficiency Diagnosis of Rice Plant by Ensemble Learning," 2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI), pp. 1-6, 2022.
  7. K. W. Thar and T. T. Wai, "Machine Learning Based Predictive Modelling for Fraud Detection in Digital Banking," 2024 IEEE Conference on Computer Applications (ICCA), pp. 1-5, 2024.
  8. R. K. Somkunwar, A. Pimpalkar, K. M. Katakdound, A. S. Bhide, S. P. Chinchalkar and Y. M. Patil, "A Fraud Detection System in Financial Networks Using AntiBenford Subgraphs and Machine Learning Algorithms," 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), pp. 1-6, 2023.
  9. A. Al-Maari and M. Abdulnabi, "Credit Card Fraud Transaction Detection Using a Hybrid Machine Learning Model," 2023 IEEE 21st Student Conference on Research and Development (SCOReD), pp. 119-123, 2023.
  10. M. Auleria, D. E. Saputra and Y. Yustiawan, "Data Driven Analysis of Fraudulent Transaction Characteristics in Branchless Banking," 2024 3rd International Conference on Digital Transformation and Applications (ICDXA), pp. 68-73,2024.
  11. H. Ali Mohamed and S. Subramanian, "Fraud Classification In Financial Statements Using Machine Learning Techniques," 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD), pp. 1-4, 2023.
  12. R. Achary and C. J. Shelke, "Fraud Detection in Banking Transactions Using Machine Learning," 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), pp. 221-226, 2023.
  13. Mahajan, V. S. Baghel and R. Jayaraman, "Credit Card Fraud Detection using Logistic Regression with Imbalanced Dataset," 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 339-342, 2023.
  14. Backiyalakshmi and B. Umadevi, "A Systematic Short Review on Intelligent Fraud Detection Approaches in the Banking Sector using Deep Learning and Machine Learning with Future Trends," 2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC), pp. 474-481, 2203.
  15. “Synthetic Financial Datasets For Fraud Detection”, Online Available: https://www.kaggle.com/datasets/ealaxi/paysim1/data  . (assesed on 28 July, 2024)
  16. P. Singla and V. Verma, “Towards Personalized Job Recommendations: A Natural Language Processing Perspective,” 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), pp. 768-773, 2023

Banking, a pioneering industry, is experiencing rapid growth, leading to the adoption of cashless transactions. Digital banking offers better service quality but has faced challenges from fraudulent activities. Since the banking industry is expanding quickly throughout the globe, using cash for payments is becoming less common. Instead, people are using cashless transactions. Digital banking customers receive higher-quality services in money transfers, cashless payments, credit cards, and prepaid cards. Nonetheless, the fraudulent activities of scammers have drawn attention to the security of digital banking, as a lack of adequate protection has discouraged many users from using the service up to this point. Even though fraud is not a new problem, its associated actions always cause billions of dollars' worth of annual losses to the world economy. Fraudulent actions carry a wide range of severe financial hazards that might jeopardize an economy's profitability and reputation. The study aims to introduce an efficient hyperparameter-tuned machine learning approach to detect fraud in banking and financial transaction systems. Proper preprocessing and application of feature engineering, such as outlier rejection, null value handling, standardization, and parameter tuning, have been incorporated with the approach. Later, the Extreme gradient boosting model was trained with tunned parameters and evaluated with test data. The model demonstrated praiseworthy performance, having 99.63% accuracy. Extensive analysis using feature selection, confusion matrix, roc, and tunning evaluation graph was conducted to detect fraud in financial transactions.

Keywords : XGB, Financial Baking, Fraud, Grid Search, Tuning.

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe