Detection & Suspicious financial flow by using Autoencoder and RBA

YERRABOGULU DILIP KUMAR; G.V.S ANANTHNATH

doi:10.36948/ijfmr.2025.v07i04.50174

Detection & Suspicious financial flow by using Autoencoder and RBA

Author(s)	Mr. YERRABOGULU DILIP KUMAR, Mr. G.V.S ANANTHNATH
Country	India
Abstract	Detection and prevention of fraudulent transactions in e-commerce platforms have always been the focus of transaction security systems. However, due to the concealment of e-commerce, it is not easy to capture attackers solely based on the historic order information. Many researches try to develop technologies to prevent the frauds, which have not considered the dynamic behaviors of users from multiple perspectives. This leads to an inefficient detection of fraudulent behaviors. To this end, this project implements the RBA and DNN algorithms by combining internal control risk factors with the existing AML algorithms. Model selection is performed on the base of POC Data, and AE is found to be the most suitable model for unsupervised learning. The predictive model aims to provide accurate predictions for new data, that is, data not used during model training. The objective is to enhance the generalization performance of the predictive model. The predictive model includes hyperparameters that are closely aligned with the training data. Selecting hyperparameters that closely match the training data often leads to overfitting, which causes performance loss. To address this problem, dropout was used during the learning process
Keywords	Risk-based approach (RBA), anti money laundering (AML), autoencoder, money laundering symptoms, suspicious transaction report (STR)
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-07-07
DOI	https://doi.org/10.36948/ijfmr.2025.v07i04.50174
Short DOI	https://doi.org/g9s9dq

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Detection & Suspicious financial flow by using Autoencoder and RBA

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