research-article

Ensemble learning for credit card fraud detection

Authors:

Rameshwar Pratap,

Ullas NambiarAuthors Info & Claims

CODS-COMAD '18: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data

Pages 289 - 294

https://doi.org/10.1145/3152494.3156815

Published: 11 January 2018 Publication History

Abstract

Timely detection of fraudulent credit card transactions is a business critical and challenging problem in Financial Industry. Specifically, we must deal with the highly skewed nature of the dataset, that is, the ratio of fraud to normal transactions is very small. In this work, we present an ensemble machine learning approach as a possible solution to this problem. Our observation is that Random Forest is more accurate in detecting normal instances, and Neural Network is for detecting fraud instances. We present an ensemble method - based on a combination of random forest and neural network - which keeps the best of both worlds, and is able to predict with high accuracy and confidence the label of a new sample. We experimentally validate our observations on real world datasets.

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Cited By

Museba TVanhoof K(2024)An Adaptive Heterogeneous Ensemble Learning Model for Credit Card Fraud DetectionAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0903019:3(1-11)Online publication date: May-2024
https://doi.org/10.25046/aj090301
Ebenezer AOlutayo BOlumide O OG AEmmanuel MOluwayemisi Boye F(2024)Money Laundering Detection and Mitigation Using Decision Tree Ensembles2024 IEEE SmartBlock4Africa10.1109/SmartBlock4Africa61928.2024.10779497(1-11)Online publication date: 30-Sep-2024
https://doi.org/10.1109/SmartBlock4Africa61928.2024.10779497
Wang SKo RBai GDong NChoi TZhang Y(2024)Evasion Attack and Defense on Machine Learning Models in Cyber-Physical Systems: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.334480826:2(930-966)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2023.3344808
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cover image ACM Other conferences

CODS-COMAD '18: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data

January 2018

379 pages

ISBN:9781450363419

DOI:10.1145/3152494

Conference Chair:
Sayan Ranu
IIT Delhi
,
General Chairs:
Niloy Ganguly
IIT Kharagpur
,
Raghu Ramakrishnan
Microsoft
,
Program Chairs:
Sunita Sarawagi
IIT Bombay
,
Shourya Roy
American Express Big Data Labs

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 11 January 2018

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CoDS-COMAD '18

CoDS-COMAD '18: The ACM India Joint International Conference on Data Science & Management of Data

January 11 - 13, 2018

Goa, India

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CODS-COMAD '18 Paper Acceptance Rate 50 of 150 submissions, 33%;

Overall Acceptance Rate 197 of 680 submissions, 29%

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Cited By

Museba TVanhoof K(2024)An Adaptive Heterogeneous Ensemble Learning Model for Credit Card Fraud DetectionAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0903019:3(1-11)Online publication date: May-2024
https://doi.org/10.25046/aj090301
Ebenezer AOlutayo BOlumide O OG AEmmanuel MOluwayemisi Boye F(2024)Money Laundering Detection and Mitigation Using Decision Tree Ensembles2024 IEEE SmartBlock4Africa10.1109/SmartBlock4Africa61928.2024.10779497(1-11)Online publication date: 30-Sep-2024
https://doi.org/10.1109/SmartBlock4Africa61928.2024.10779497
Wang SKo RBai GDong NChoi TZhang Y(2024)Evasion Attack and Defense on Machine Learning Models in Cyber-Physical Systems: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.334480826:2(930-966)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2023.3344808
Jemai JZarrad ADaud A(2024)Identifying Fraudulent Credit Card Transactions Using Ensemble LearningIEEE Access10.1109/ACCESS.2024.338082312(54893-54900)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3380823
Gandhar AGupta KPandey ARaj D(2024)Fraud Detection Using Machine Learning and Deep LearningSN Computer Science10.1007/s42979-024-02772-x5:5Online publication date: 18-Apr-2024
https://doi.org/10.1007/s42979-024-02772-x
Harish SLakhanpal CJafari A(2024)Leveraging graph-based learning for credit card fraud detection: a comparative study of classical, deep learning and graph-based approachesNeural Computing and Applications10.1007/s00521-024-10397-736:34(21873-21883)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s00521-024-10397-7
Sujatha EUmarani VRekha KGopirajan PManickavasagan V(2024)Handling Imbalanced Data for Credit Card Fraudulent Detection: A Machine Learning ApproachAdvances in Artificial Intelligence and Machine Learning in Big Data Processing10.1007/978-3-031-73068-9_18(220-233)Online publication date: 1-Oct-2024
https://doi.org/10.1007/978-3-031-73068-9_18
Vishnumolakala SGopu SDash JTripathy SSingh S(2024)Deep Learning Models in Finance: Past, Present, and FutureMachine Learning Approaches in Financial Analytics10.1007/978-3-031-61037-0_21(453-465)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-3-031-61037-0_21
Rajesh PShreyanth SSarveshwaran RNithin Chary V(2024)Bayesian Optimized Random Forest Classifier for Improved Credit Card Fraud Detection: Overcoming Challenges and LimitationsAccelerating Discoveries in Data Science and Artificial Intelligence I10.1007/978-3-031-51167-7_20(205-214)Online publication date: 29-May-2024
https://doi.org/10.1007/978-3-031-51167-7_20
Strelcenia EPrakoonwit S(2023)Improving Classification Performance in Credit Card Fraud Detection by Using New Data AugmentationAI10.3390/ai40100084:1(172-198)Online publication date: 31-Jan-2023
https://doi.org/10.3390/ai4010008
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