Application of Machine Learning Techniques in Fintech Integrations in the fields of Fraud Detection and AML |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-3 |
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| Year of Publication : 2024 | ||
| Authors : Prasenjit Banerjee, Rajarshi Roy | ||
| DOI : 10.14445/22312803/IJCTT-V72I3P107 | ||
How to Cite?
Prasenjit Banerjee, Rajarshi Roy, "Application of Machine Learning Techniques in Fintech Integrations in the fields of Fraud Detection and AML ," International Journal of Computer Trends and Technology, vol. 72, no. 3, pp. 46-52, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I3P107
Abstract
Fintech Systems have evolved rapidly in the last ten years, first with the commercialization of Big data systems and then with the abundance of Machine learning models that have been trained on a large volume of data sets. However, it is important to understand the challenges faced by the organizations related to the adoption of this machine learning algorithm in the financial technology space. Finance is very well regulated, and most of the data in financial transactions involves personally identifiable information that cannot be made available to Machine learning models because of regulatory requirements. In this paper, we will examine some of the real-world challenges and the solutions offered. We are evaluating novel techniques that examine the application Bayesian perspective and allow us to model machine learning algorithms with simulated data that mask sensitive information. Our approach follows an iterative performance of classification in a diagnostic setting. These novel techniques allow for simplification without negatively impacting the efficacy of the model. We will also look at some of the other aspects of data preparation that allow for speed evaluation and rapid prototyping. Last but not least, we will focus on realworld applications of machine learning algorithms in categorization and anomaly detection.
Keywords
Machine Learning, Big Data, Artificial Intelligence, FinTech, Data classification, Data.
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