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Comparative Analysis of Numerous Approaches in Machine Learning to Predict Financial Fraud in Big Data Framework

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1380))

Abstract

Nowadays, financial frauds that are taking place across the globe generate much more threats and thus have a thoughtful impact on the financial subdivision. Due to this fact, financial institution is forced to improve their fraud detection mechanism so that these various kinds of financial frauds can be detected in early stages. The various studies done in past few years show that the usage of ML and big data analytics has improved the efficiency of these methodologies. This paper basically proposed a state of art on different kinds of financial frauds and specify various financial frauds, detection and prevention techniques used in financial frauds. The key purpose of the work done is to discuss in detail various fraud detection methodologies and technologies, their comparison and performance efficiency. It provides a complete comparison of all techniques based on machine learning that are used in detecting and preventing the different kinds of financial frauds. It also delivers a complete study of all the methods which were used in recent past with their efficiency and capability for the detecting various kinds of frauds in financial sector.

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Authors and Affiliations

  1. Department of CSE, Graphic Era Hill University, Uttarakhand, India

    Amit Gupta & M. C. Lohani

Authors
  1. Amit Gupta
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  2. M. C. Lohani
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Shobhit University Gangoh, Gangoh, Uttar Pradesh, India

    Tarun K. Sharma

  2. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Chang Wook Ahn

  3. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Om Prakash Verma

  4. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Gupta, A., Lohani, M.C. (2022). Comparative Analysis of Numerous Approaches in Machine Learning to Predict Financial Fraud in Big Data Framework. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-1740-9_11

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