Critically Evaluating SWIFT’s Strategy as a Monopoly in the Fintech Business

Dasgupta, Sabyasachi; Grover, Priya

doi:10.35940/ijitee.l3355.1081219

Cited by 4 publications

(1 citation statement)

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“…This provides an interbank network offering different services, such as money transfers between bank accounts. More than 11,000 banking organizations across nearly 200 countries use SWIFT to transfer money [2]. SWIFT transactions are international and may have multiple intermediaries between the transaction's originator and beneficiary.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Method to Detect Fraudulent Transactions and Identify Fraud Types while Minimizing Mounting Costs

Hamza¹,

Abrouk²,

Cullot³

et al. 2023

IJACSA

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Financial fraud is a complex problem faced by financial institutions, and existing fraud detection systems are often insufficient, resulting in significant financial losses. Researchers have proposed various machine learning-based techniques to enhance the performance of these systems. In this work, we present a semi-supervised approach to detect fraudulent transactions. First, we extract and select features, followed by the training of a binary classification model. Secondly, we apply a clustering algorithm to the fraudulent transactions and use the binary classification model with the SHAP framework to analyze the clusters and associate them with a particular fraud type. Finally, we present an algorithm to detect and assign a fraud type by leveraging a multi-fraud classification model. To minimize the mounting cost of the model, we propose an algorithm to choose an optimal threshold that can detect fraudulent transactions. We work with experts to adapt a risk cost matrix to estimate the mounting cost of the model. This risk cost matrix takes into account the cost of missing fraudulent transactions and the cost of incorrectly flagging a legitimate transaction as fraudulent.In our experiments on a real dataset, our approach achieved high accuracy in detecting fraudulent transactions, with the added benefit of identifying the fraud type, which can help financial institutions better understand and combat fraudulent activities. Overall, our approach offers a comprehensive and efficient solution to financial fraud detection, and our results demonstrate its effectiveness in reducing financial losses for financial institutions.

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Section: Introductionmentioning

confidence: 99%