Towards proactive financial crime and fraud detection through artificial intelligence and RegTech technologies

Zeinab Rouhollahi; Amin Beheshti; Salman Mousaeirad; Srinivasa Reddy Goluguri

doi:10.1145/3487664.3487740

Towards proactive financial crime and fraud detection through artificial intelligence and RegTech technologies

Zeinab Rouhollahi, Amin Beheshti, Salman Mousaeirad, Srinivasa Reddy Goluguri

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

6 Citations (Scopus)

Abstract

Recently, financial institutes have been dealing with an increase in financial crimes. In this context, financial services firms started to improve their vigilance and use new technologies and approaches to identify and predict financial fraud and crime possibilities. This task is challenging as institutions need to upgrade their data and analytics capabilities to enable new technologies such as Artificial Intelligence (AI) to predict and detect financial crimes. In this paper, we put a step towards AI-enabled financial crime detection in general and money laundering detection in particular to address this challenge. We study and analyse the recent works done in financial crime detection and present a novel model to detect money laundering cases with minimum human intervention needs.

Original language	English
Title of host publication	23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021 - Proceedings
Editors	Eric Pardede, Maria-Indrawan Santiago, Pari Delir Haghighi, Matthias Steinbauer, Ismail Khalil, Gabriele Kotsis
Place of Publication	New York, NY
Publisher	Association for Computing Machinery
Pages	538-546
Number of pages	9
ISBN (Electronic)	9781450395564
DOIs	https://doi.org/10.1145/3487664.3487740
Publication status	Published - 2021
Event	23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021 - Virtual, Online, Linz, Austria Duration: 29 Nov 2021 → 1 Dec 2021

Conference

Conference	23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021
Country/Territory	Austria
City	Linz
Period	29/11/21 → 1/12/21

Keywords

Anomaly Detection
Anti Money Laundering
Classification
Data Analysis
Financial Fraud Detection

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10.1145/3487664.3487740

Cite this

Rouhollahi, Z., Beheshti, A., Mousaeirad, S., & Goluguri, S. R. (2021). Towards proactive financial crime and fraud detection through artificial intelligence and RegTech technologies. In E. Pardede, M.-I. Santiago, P. D. Haghighi, M. Steinbauer, I. Khalil, & G. Kotsis (Eds.), 23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021 - Proceedings (pp. 538-546). Association for Computing Machinery. https://doi.org/10.1145/3487664.3487740

Rouhollahi, Zeinab ; Beheshti, Amin ; Mousaeirad, Salman et al. / Towards proactive financial crime and fraud detection through artificial intelligence and RegTech technologies. 23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021 - Proceedings. editor / Eric Pardede ; Maria-Indrawan Santiago ; Pari Delir Haghighi ; Matthias Steinbauer ; Ismail Khalil ; Gabriele Kotsis. New York, NY : Association for Computing Machinery, 2021. pp. 538-546

@inproceedings{4fa5f14153da4c438c859342507dced3,

title = "Towards proactive financial crime and fraud detection through artificial intelligence and RegTech technologies",

abstract = "Recently, financial institutes have been dealing with an increase in financial crimes. In this context, financial services firms started to improve their vigilance and use new technologies and approaches to identify and predict financial fraud and crime possibilities. This task is challenging as institutions need to upgrade their data and analytics capabilities to enable new technologies such as Artificial Intelligence (AI) to predict and detect financial crimes. In this paper, we put a step towards AI-enabled financial crime detection in general and money laundering detection in particular to address this challenge. We study and analyse the recent works done in financial crime detection and present a novel model to detect money laundering cases with minimum human intervention needs. ",

keywords = "Anomaly Detection, Anti Money Laundering, Classification, Data Analysis, Financial Fraud Detection",

author = "Zeinab Rouhollahi and Amin Beheshti and Salman Mousaeirad and Goluguri, {Srinivasa Reddy}",

year = "2021",

doi = "10.1145/3487664.3487740",

language = "English",

pages = "538--546",

editor = "Eric Pardede and Maria-Indrawan Santiago and Haghighi, {Pari Delir} and Matthias Steinbauer and Ismail Khalil and Gabriele Kotsis",

booktitle = "23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021 - Proceedings",

publisher = "Association for Computing Machinery",

note = "23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021 ; Conference date: 29-11-2021 Through 01-12-2021",

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Rouhollahi, Z, Beheshti, A , Mousaeirad, S & Goluguri, SR 2021, Towards proactive financial crime and fraud detection through artificial intelligence and RegTech technologies. in E Pardede, M-I Santiago, PD Haghighi, M Steinbauer, I Khalil & G Kotsis (eds), 23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021 - Proceedings. Association for Computing Machinery, New York, NY, pp. 538-546, 23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021, Linz, Austria, 29/11/21. https://doi.org/10.1145/3487664.3487740

Towards proactive financial crime and fraud detection through artificial intelligence and RegTech technologies. / Rouhollahi, Zeinab; Beheshti, Amin ; Mousaeirad, Salman et al.
23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021 - Proceedings. ed. / Eric Pardede; Maria-Indrawan Santiago; Pari Delir Haghighi; Matthias Steinbauer; Ismail Khalil; Gabriele Kotsis. New York, NY: Association for Computing Machinery, 2021. p. 538-546.

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

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AU - Rouhollahi, Zeinab

AU - Beheshti, Amin

AU - Mousaeirad, Salman

AU - Goluguri, Srinivasa Reddy

PY - 2021

Y1 - 2021

N2 - Recently, financial institutes have been dealing with an increase in financial crimes. In this context, financial services firms started to improve their vigilance and use new technologies and approaches to identify and predict financial fraud and crime possibilities. This task is challenging as institutions need to upgrade their data and analytics capabilities to enable new technologies such as Artificial Intelligence (AI) to predict and detect financial crimes. In this paper, we put a step towards AI-enabled financial crime detection in general and money laundering detection in particular to address this challenge. We study and analyse the recent works done in financial crime detection and present a novel model to detect money laundering cases with minimum human intervention needs.

AB - Recently, financial institutes have been dealing with an increase in financial crimes. In this context, financial services firms started to improve their vigilance and use new technologies and approaches to identify and predict financial fraud and crime possibilities. This task is challenging as institutions need to upgrade their data and analytics capabilities to enable new technologies such as Artificial Intelligence (AI) to predict and detect financial crimes. In this paper, we put a step towards AI-enabled financial crime detection in general and money laundering detection in particular to address this challenge. We study and analyse the recent works done in financial crime detection and present a novel model to detect money laundering cases with minimum human intervention needs.

KW - Anomaly Detection

KW - Anti Money Laundering

KW - Classification

KW - Data Analysis

KW - Financial Fraud Detection

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M3 - Conference proceeding contribution

AN - SCOPUS:85122622178

SP - 538

EP - 546

BT - 23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021 - Proceedings

A2 - Pardede, Eric

A2 - Santiago, Maria-Indrawan

A2 - Haghighi, Pari Delir

A2 - Steinbauer, Matthias

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A2 - Kotsis, Gabriele

PB - Association for Computing Machinery

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T2 - 23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021

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ER -

Rouhollahi Z, Beheshti A , Mousaeirad S, Goluguri SR. Towards proactive financial crime and fraud detection through artificial intelligence and RegTech technologies. In Pardede E, Santiago MI, Haghighi PD, Steinbauer M, Khalil I, Kotsis G, editors, 23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021 - Proceedings. New York, NY: Association for Computing Machinery. 2021. p. 538-546 doi: 10.1145/3487664.3487740

Towards proactive financial crime and fraud detection through artificial intelligence and RegTech technologies

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