How Real-Time Streaming Helping Fraud Detection for Trade Clearing Firms |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-6 |
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| Year of Publication : 2024 | ||
| Authors : Prabhu Patel | ||
| DOI : 10.14445/22312803/IJCTT-V72I6P110 | ||
How to Cite?
Prabhu Patel, "How Real-Time Streaming Helping Fraud Detection for Trade Clearing Firms ," International Journal of Computer Trends and Technology, vol. 72, no. 6, pp. 72-79, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I6P110
Abstract
The application of Time-Series Analysis and Fraud Score Calculation as essential elements of clearing organisations' fraud detection systems is investigated in this paper. Using sequential data point analysis and numerical scoring of transactions, these analytical methods provide proactive ways to identify and reduce fraudulent activity. Through Fraud Score Calculation, clearing companies may quickly detect high-risk transactions and prioritise resources by utilising statistical methodologies and machine learning algorithms. In order to preserve market integrity, Time-Series Analysis simultaneously makes it possible to identify minute patterns and trends in transaction data, which paves the way for predictive modelling and proactive intervention. The results highlight how crucial these analytical skills are for negotiating the intricacies of the financial markets and avoiding possible dangers. On the other hand, clearing companies are better able to detect fraud, reduce losses, and maintain customer confidence thanks to the combination of Time-Series Analysis and Fraud Score Calculation.
Keywords
Real-Time Data Streaming, Fraud detection, Trade clearing firm.
Reference
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