Solving the False Positives Problem in Fraud Prediction Using Automated Feature Engineering

Wedge, Roy; Kanter, James Max; Veeramachaneni, Kalyan; Rubio, Santiago Moral; Pérez, Sergio Iglesias

doi:10.1007/978-3-030-10997-4_23

Cited by 15 publications

(18 citation statements)

References 10 publications

(11 reference statements)

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“…Wedge et al [ 67 ] proposed an approach for automated feature engineering designed to reduce the number of false positives. The authors stated that accessible information about cards and customers can increase the size of potential features set drastically.…”

Section: Fraud Detection In the Fintech Domainmentioning

confidence: 99%

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Stojanović

Božić

Hofer-Schmitz

et al. 2021

Sensors

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Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future.

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Section: Fraud Detection In the Fintech Domainmentioning

confidence: 99%

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Stojanović

Božić

Hofer-Schmitz

et al. 2021

Sensors

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“…With the growing focus on illicit activities, the academic literature has emphasised coming up with a wide variety of automated detection systems to detect such illicit activities (Baader and Krcmar, 2018;Battaglia et al, 2018;Chang et al, 2008;Gepp, 2016;Gepp et al, 2018;Gepp, 2015;Khaled et al, 2018;Ngai et al, 2011;Perols, 2011;Phua et al, 2010;Ravenda et al, 2015;Sahin et al, 2013;Singh and Best, 2019;Song et al, 2014;Van Vlasselaer et al, 2017;Wedge et al, 2017). As per Ngai et al (2011), although the application of datamining techniques has been extended towards the detection of insurance fraud, there exists a distinct lack of research on mortgage fraud, money laundering and securities and commodities fraud.…”

Section: Detection Of Money Launderingmentioning

confidence: 99%

A review of money laundering literature: the state of research in key areas

Tiwari

Gepp

Kumar

2020

PAR

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Purpose:The purpose of this study is to review the literature on money laundering and its related areas. The main objective is to identify any gaps in the literature and direct attention towards addressing them.Design/Methodology/Approach: A systematic review of the money laundering literature was conducted with an emphasis on the Pro-Quest, Scopus and Science-Direct databases. Broad research themes were identified after investigating the literature. The theme about the detection of money laundering was then further investigated. The major approaches of such detection are identified as well as research gaps that could be addressed in future studies. Findings:The literature on money laundering can be classified into the following six broad areas: (i) anti-money laundering framework and its effectiveness, (ii) the effect of money laundering on other fields and the economy, (iii) the role of actors and their relative importance, (iv) the magnitude of money laundering, (v) new opportunities available for money laundering and (vi) detection of money laundering. Most studies about the detection of money laundering have focused on the use of innovative technologies, banking transactions, or real estate and trade-based money laundering. However, the literature on the detection of shell companies being explicitly used to launder funds is relatively scarce.Originality/Value: This paper provides insights into an area related to money laundering where research is relatively scant. Shell companies incorporated in the UK alone were identified to be associated with laundering 80 billion pounds of stolen money between 2010 and 2014. The use of these entities to launder billions of dollars as witnessed through the laundromat schemes and several data leaks clearly indicate the need to focus on illicit financial flows through such entities.

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“…In order to reduce the number of false positives, [19] proposed a model based on automated feature engineering to automatically derive behavioral features based on the historical data of a credit card associated with a transaction. A total of 237 features for each transaction was generated, and a random forest was then employed to learn a classifier.…”

Section: Detecting Electronic Banking Fraud On Highly Imbalanced Data …mentioning

confidence: 99%

“…Due to the highly imbalanced nature of the dataset, precision(p), recall(R) and F1scores (F) as presented in equations (33) to (35) respectively are used as evaluation metrics (Wedge et al [19]). y=, Ÿ=, Ÿ8 represent True Positives, False Positives and False Negatives respectively.…”

Section: Fraud Detectionmentioning

confidence: 99%

Detecting Electronic Banking Fraud on Highly Imbalanced Data using Hidden Markov Models

Danaa

Daabo²,

Abdul-Barik

2021

EJMS

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Recent researches have revealed the capability of Machine Learning (ML) techniques to effectively detect fraud in electronic banking transactions since they have the potential to detect new and unknown intrusions. A major challenge in the application of ML to fraud detection is the presence of highly imbalanced data sets. In many available datasets, majority of transactions are genuine with an extremely small percentage of fraudulent ones. Designing an accurate and efficient fraud detection system that is low on false positives but detects fraudulent activity effectively is a significant challenge for researchers. In this paper, a framework based on Hidden Markov Models (HMM), modified Density Based Spatial Clustering of Applications with Noise (DBSCAN) and Synthetic Minority Oversampling Technique Techniques (SMOTE) is proposed to effectively detect fraud in a highly imbalanced electronic banking dataset. The various transaction types, transaction amounts and the frequency of transactions are taken into consideration by the proposed model to enable effective detection. With different number of hidden states for the proposed HMMs, simulations are performed for four (4) different approaches and their performances compared using precision, recall rate and F1-Score as the evaluation metrics. The study revealed that, our proposed approach is able to detect fraudulent transactions more effectively with reasonably low number of false positives.

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Solving the False Positives Problem in Fraud Prediction Using Automated Feature Engineering

Cited by 15 publications

References 10 publications

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

A review of money laundering literature: the state of research in key areas

Detecting Electronic Banking Fraud on Highly Imbalanced Data using Hidden Markov Models

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