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Fraud Prediction in Smart Societies Using Logistic Regression and k-fold Machine Learning Techniques

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Abstract

The credit/debit card deceit detection is an enormously difficult task. However, it is a well known problem of our cloud based mobile internet society and it must be solved by technocrats in the welfare of societal mental harassments. The main problem in executing credit/debit card fraud detection technique is the availability of limited amount of fraud related data like transaction amount, transaction date, transaction time, address, and vendor category code related to the frauds. It is the truth of mobile internet world that there are billions of potential places and e-commerce websites where a credit/debit card can be used by fraudulent people for online transactions and payments which make it exceedingly thorny to trace the pattern of frauds. Moreover, the problem of fraud detection in cloud— Internet of Things (IoT) based smart societies has numerous constraints like continuous change in the behavior of normal and fraudulent persons, the fraudulent people try to develop and use new method for executing frauds, and very little availability of frauds related bench mark data sets. In this research article, the authors have presented logistic regression based k-fold machine learning technique (MLT) for fraud detection and prevention in cloud-IoT based smart societal environment. The k-fold method creates multiple folds of bank transactions related data before implementing logistic regression and MLT. The logistic regression performs logic based regression analysis and the intelligent machine learning approach performs registration, classification, clustering, dimensionality reduction, deep learning, training, and reinforcement learning steps on the received bank transactions data. The implementation of proposed methodology and its further analysis using intelligent machine learning tools like ROC (Receiver Operating Characteristic) curve, confusion matrix, mean-recall score value, and precision recall curves for European banks day-to-day transactions related bench mark data set reveal that the proposed methodology is efficient, accurate, and reliable for detecting frauds in cloud-IoT based smart societal environment.

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Funding

Funding was provided by Birla Institute of Scientific Research (Grant No. 0012).

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  1. Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, India

    Kamta Nath Mishra & Subhash Chandra Pandey

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  1. Kamta Nath Mishra
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Correspondence to Kamta Nath Mishra.

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Mishra, K.N., Pandey, S.C. Fraud Prediction in Smart Societies Using Logistic Regression and k-fold Machine Learning Techniques. Wireless Pers Commun 119, 1341–1367 (2021). https://doi.org/10.1007/s11277-021-08283-9

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