Reducing false positives in bank anti-fraud systems based on rule induction in distributed tree-based models

Vorobyev, Ivan A.; Anna, Krivitskaya

doi:10.1016/j.cose.2022.102786

Cited by 13 publications

(7 citation statements)

References 17 publications

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“…Furthermore, to tackle the issue of identifying malicious financial transactions with a significant proportion of false positives (FPR). The authors [3,10] have provided a rules generation system based on distributed trees-based supervised techniques involving Decision Tree, RF, and Gradient Boosting (GB), employing expert rule elements as model variables. The autogenerated rules were designed to boost FPR company measurements.…”

Section: Related Workmentioning

confidence: 99%

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

Abbassi,

E L Mendili,

Gahi

2024

Eng. Res. Express

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Since the use of the Internet has increased exponentially, numerous organizations, including the financial industry, offer services online. As a result, financial scams are expanding in quantity and complexity worldwide, resulting in massive revenue losses and making digital fraudulent transactions a severe issue. Abnormal attempts and illegal access are instances of these dangers that fraudulent activity detection systems must identify. Machine learning and data mining approaches have been extensively used to address this issue in recent years.However, these approaches must be enhanced regarding real-time detection speed, tackling enormous amounts of data, and finding undiscovered attack patterns. Consequently, the present study provides a real-time architecture for averting and identifying digital transaction fraud, which relies on the Isolation Forest (IForest) approach and big data analytic tools, including Spark Streaming, sparkling water, Kafka, and PostgreSQL. This architecture seeks to improve present detection strategies by increasing accuracy for detection when considering enormous amounts of data. Two real datasets of online transactional fraud are used to assess the proposed architecture, and the findings are compared to relevant studies. The investigation results showed that IForest performed flawlessly, achieving an accuracy of 0.99 in two datasets.

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Section: Related Workmentioning

confidence: 99%

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

Abbassi,

E L Mendili,

Gahi

2024

Eng. Res. Express

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“…Applications based on AI and ML have seen a notable rise in the BFSI sector, attributed to their ability to streamline processes (Moutinho and Smith 2000), refine decision-making (Polychroniou and Giannikos 2009), augment customer experiences (Zeinalizadeh et al 2015), and identify fraudulent activities (Vorobyev and Krivitskaya 2022). Hanafizadeh and Zare Ravasan (2018) found that by outsourcing e-banking services, it is possible to categorize all 23 factors into three distinct segments: technological, environmental, and organizational.…”

Section: Financial Inclusion Fintech and Artificial Intelligencementioning

confidence: 99%

Financial Inclusion and Its Ripple Effects on Socio-Economic Development: A Comprehensive Review

Mishra,

Kandpal,

Agarwal

et al. 2024

JRFM

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This study provides an overview of the different dimensions of financial inclusion, its socioeconomic impacts on society’s sustainable development, and future research agendas. Initially, 620 studies were identified using Scopus and other databases, employing keywords such as financial literacy, financial inclusion, financial capability, women’s empowerment, fintech, artificial intelligence, financial accessibility, sustainable development goals, and economic growth. After refinement based on focus and relevance, 325 papers were analyzed in detail for review, primarily focused on India and emerging economies. This review highlights that access to finance by untouched segments of society is essential for sustainable and socio-economic development in developing economies. The official banking system, an effort by the government to assist the financially disadvantaged, can incorporate the impoverished into a formal financial system through campaigns and credit system reforms. Socioeconomic programs reinforce one another and foster the development of children, women, families, and society. This research paper undertakes a systematic literature review primarily focused on relevant articles in broad areas of financial inclusion and its impact analysis and offers a valuable agenda for future research.

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“…K-CGAN has the most excellent F1 Score and Accuracy among them. On the other side [21][22][23], we have developed an automatic rule-generating system to identify fraud systems that employ dispersed tree-based models involving DT, RF, and Gradient Boosting, with the parts of the expert rules serving as model attributes. They tested the proposed method using a bank's card transactions.…”

Section: Online Banking Fraud Detection Tacticsmentioning

confidence: 99%

Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion

Abbassi,

El Mendili,

Gahi

2024

HighTech. Innov. J.

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Digital trades and payments are becoming increasingly popular, as they typically entail monetary transactions. This not only makes electronic transactions more convenient for the end customer, but it also raises the likelihood of fraud. An adequate fraud detection system with a cutting-edge model is critical to minimizing fraud costs. Identifying fraud at the ideal time entails establishing and setting up ubiquitous systems to consume and analyze massive amounts of streaming data. Recent advances in data analytics methods and introducing open-source technology for big data storage and processing opened new options for detecting fraud. This study aims to tackle this critical issue by providing a newly real-time e-transaction fraud detection schema that consolidates the advantages of both unsupervised learners, including autoencoder and extended isolation forests, with cutting-edge big data gadgets such as Spark streaming and sparkling water. It addresses the shortage of non-fraudulent instances and handles the excessive dimension of the set of features. On two real-world transactional datasets, we assess our suggested technique. Compared with other current fraud identification systems, our methodology delivers an elevated accuracy yield of 99%. Furthermore, it outperforms state-of-the-art approaches in reliably identifying fraudulent samples. Doi: 10.28991/HIJ-2024-05-01-014 Full Text: PDF

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Reducing false positives in bank anti-fraud systems based on rule induction in distributed tree-based models

Cited by 13 publications

References 17 publications

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

Financial Inclusion and Its Ripple Effects on Socio-Economic Development: A Comprehensive Review

Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion

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