Exploring the Effectiveness of Machine Learning Models in Detecting Anomalous Transactions

Anand Radhan; N. Sumith; Srividya

doi:10.1109/AIDE64228.2025.10987475

Exploring the Effectiveness of Machine Learning Models in Detecting Anomalous Transactions

Anand Radhan
, N. Sumith
, Srividya

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Money laundering and illicit financial flows facilitate criminal operations and undermine economic stability. Cryptocurrencies present regulatory challenges due to their anonymity and decentralized nature. Anomalous transactions refer to financial transactions that deviate from established patterns, indicating potential fraud, errors, or unusual behavior. This paper reviews machine learning techniques for detecting anomalous cryptocurrency transactions from an anti-money laundering/counter-terrorist financing (AML/CFT) perspective. A real-world Bitcoin transaction dataset is analyzed for our study. The paper assesses how well various machine learning models perform in detecting anomalous transactions. Detecting these anomalies is important in preventing fraud in areas like banking, e-commerce, and financial services.

Original language	English
Title of host publication	2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	85-89
Number of pages	5
ISBN (Electronic)	9798331527518
DOIs	https://doi.org/10.1109/AIDE64228.2025.10987475
Publication status	Published - 2025
Event	2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Nitte, India Duration: 06-02-2025 → 07-02-2025

Publication series

Name	2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings

Conference

Conference	2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025
Country/Territory	India
City	Nitte
Period	06-02-25 → 07-02-25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems
Statistics, Probability and Uncertainty
Instrumentation

Access to Document

10.1109/AIDE64228.2025.10987475

Cite this

Radhan, A., Sumith, N., & Srividya (2025). Exploring the Effectiveness of Machine Learning Models in Detecting Anomalous Transactions. In 2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings (pp. 85-89). (2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIDE64228.2025.10987475

Radhan, Anand ; Sumith, N. ; Srividya. / Exploring the Effectiveness of Machine Learning Models in Detecting Anomalous Transactions. 2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 85-89 (2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings).

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title = "Exploring the Effectiveness of Machine Learning Models in Detecting Anomalous Transactions",

abstract = "Money laundering and illicit financial flows facilitate criminal operations and undermine economic stability. Cryptocurrencies present regulatory challenges due to their anonymity and decentralized nature. Anomalous transactions refer to financial transactions that deviate from established patterns, indicating potential fraud, errors, or unusual behavior. This paper reviews machine learning techniques for detecting anomalous cryptocurrency transactions from an anti-money laundering/counter-terrorist financing (AML/CFT) perspective. A real-world Bitcoin transaction dataset is analyzed for our study. The paper assesses how well various machine learning models perform in detecting anomalous transactions. Detecting these anomalies is important in preventing fraud in areas like banking, e-commerce, and financial services.",

author = "Anand Radhan and N. Sumith and Srividya",

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series = "2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings",

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Radhan, A, Sumith, N & Srividya 2025, Exploring the Effectiveness of Machine Learning Models in Detecting Anomalous Transactions. in 2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings. 2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 85-89, 2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025, Nitte, India, 06-02-25. https://doi.org/10.1109/AIDE64228.2025.10987475

Exploring the Effectiveness of Machine Learning Models in Detecting Anomalous Transactions. / Radhan, Anand; Sumith, N.; Srividya.
2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2025. p. 85-89 (2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Exploring the Effectiveness of Machine Learning Models in Detecting Anomalous Transactions

AU - Radhan, Anand

AU - Sumith, N.

AU - Srividya,

PY - 2025

Y1 - 2025

N2 - Money laundering and illicit financial flows facilitate criminal operations and undermine economic stability. Cryptocurrencies present regulatory challenges due to their anonymity and decentralized nature. Anomalous transactions refer to financial transactions that deviate from established patterns, indicating potential fraud, errors, or unusual behavior. This paper reviews machine learning techniques for detecting anomalous cryptocurrency transactions from an anti-money laundering/counter-terrorist financing (AML/CFT) perspective. A real-world Bitcoin transaction dataset is analyzed for our study. The paper assesses how well various machine learning models perform in detecting anomalous transactions. Detecting these anomalies is important in preventing fraud in areas like banking, e-commerce, and financial services.

AB - Money laundering and illicit financial flows facilitate criminal operations and undermine economic stability. Cryptocurrencies present regulatory challenges due to their anonymity and decentralized nature. Anomalous transactions refer to financial transactions that deviate from established patterns, indicating potential fraud, errors, or unusual behavior. This paper reviews machine learning techniques for detecting anomalous cryptocurrency transactions from an anti-money laundering/counter-terrorist financing (AML/CFT) perspective. A real-world Bitcoin transaction dataset is analyzed for our study. The paper assesses how well various machine learning models perform in detecting anomalous transactions. Detecting these anomalies is important in preventing fraud in areas like banking, e-commerce, and financial services.

UR - https://www.scopus.com/pages/publications/105006511927

UR - https://www.scopus.com/pages/publications/105006511927#tab=citedBy

U2 - 10.1109/AIDE64228.2025.10987475

DO - 10.1109/AIDE64228.2025.10987475

M3 - Conference contribution

AN - SCOPUS:105006511927

T3 - 2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings

SP - 85

EP - 89

BT - 2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025

Y2 - 6 February 2025 through 7 February 2025

ER -

Radhan A, Sumith N, Srividya. Exploring the Effectiveness of Machine Learning Models in Detecting Anomalous Transactions. In 2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2025. p. 85-89. (2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings). doi: 10.1109/AIDE64228.2025.10987475

Exploring the Effectiveness of Machine Learning Models in Detecting Anomalous Transactions

Abstract

Publication series

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UN SDGs

All Science Journal Classification (ASJC) codes

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