Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms

Lei, Xiaochun; Mohamad, Ummul Hanan; Sarlan, Aliza; Shutaywi, Mishal; Daradkeh, Yousef Ibrahim; Mohammed, Hazhar Omer

doi:10.1016/j.ipm.2022.103036

Cited by 14 publications

(3 citation statements)

References 32 publications

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“…This attention mechanism effectively captured spatiotemporal patterns in credit card transactions, augmenting the precision and reliability of fraud detection in financial transactions. Lei et al (2022) [14] ventured into the development of an intelligent information system for financial analysis, leveraging supervised machine learning algorithms. This system facilitated comprehensive financial analysis, aiding decision-making by integrating supervised machine learning technology.…”

Section: Review Of Intelligent Recognition Research In Financial Anti...mentioning

confidence: 99%

Financial Anti-Fraud Based on Dual-Channel Graph Attention Network

Wei,

Lee

2024

JTAER

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This article addresses the pervasive issue of fraud in financial transactions by introducing the Graph Attention Network (GAN) into graph neural networks. The article integrates Node Attention Networks and Semantic Attention Networks to construct a Dual-Head Attention Network module, enabling a comprehensive analysis of complex relationships in user transaction data. This approach adeptly handles non-linear features and intricate data interaction relationships. The article incorporates a Gradient-Boosting Decision Tree (GBDT) to enhance fraud identification to create the GBDT–Dual-channel Graph Attention Network (GBDT-DGAN). In a bid to ensure user privacy, this article introduces blockchain technology, culminating in the development of a financial anti-fraud model that fuses blockchain with the GBDT-DGAN algorithm. Experimental verification demonstrates the model’s accuracy, reaching 93.82%, a notable improvement of at least 5.76% compared to baseline algorithms such as Convolutional Neural Networks. The recall and F1 values stand at 89.5% and 81.66%, respectively. Additionally, the model exhibits superior network data transmission security, maintaining a packet loss rate below 7%. Consequently, the proposed model significantly outperforms traditional approaches in financial fraud detection accuracy and ensures excellent network data transmission security, offering an efficient and secure solution for fraud detection in the financial domain.

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Section: Review Of Intelligent Recognition Research In Financial Anti...mentioning

confidence: 99%

Financial Anti-Fraud Based on Dual-Channel Graph Attention Network

Wei,

Lee

2024

JTAER

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“…Thus, machine learning procedures offer better way to adjust the progress and dynamics of the process by assisting the generation and choice of improved decision (Ariffin & Tiun, 2022;Lei et al, 2022). These aim to enhance game performance and to generate better engagement and satisfaction in game playing experiences.…”

Section: Digital Game Application Using Machine Learningmentioning

confidence: 99%

Try Dam: Digital Checkers Game Application Based on Machine Learning

Abdullah¹,

Judi²

2023

IJARBSS

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A traditional game like checker or dam is often played by people in the past during their free time. Despite their leisurely and compelling activities, the games stimulate the cognitive, social and emotional well-being of players, to plan the right strategy to win the game. However, moving along with the difference in time, traditional games that used to be often played by all sections of society are increasingly being marginalized by the younger generation. Due to the development of technology, the younger generation appears to be more inclined to play mobile games than traditional ones. The proposed solution to this problem is to create a traditional game in digital form; and this paper aims to develop a digital checkers game application using machine learning and to test the functionality and effectiveness of the game. The development of this digital checkers game applies agile model, combined with game design strategy. The Alpha-Beta Pruning algorithm will be used to produce a computer capable of playing checkers. Based on the game competition result, the player's winning rate decreases along with the chosen difficulty level. Once the difficulty level increase, players need to demonstrate their mastery to play against computer. The computer has gained increased information to defeat the opponent using various strategies using "alpha-beta pruning" technique. This digital checkers game is expected to increase awareness and passion among generation young towards the local traditional games. Expected game play experience from the application include entertainment and easy access especially using smart telephone device.

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“…By utilizing historical transactional data, these techniques aim to identify fraudulent patterns, detect anomalies, and predict potential fraudulent activities. This study will discuss several ML approaches commonly employed in financial fraud detection, including supervised learning, unsupervised learning, anomaly detection, neural networks, ensemble methods, and feature engineering [5]. Each technique will be examined in terms of its applicability, advantages, and potential limitations.…”

Section: Introductionmentioning

confidence: 99%

Credit Card Fraud Identification using Logistic Regression and Random Forest

Yundong,

Alexander Zhulev,

Omar G. Ahmed

2023

WJCMS

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Fraud is an ancient yet ever-changing profession. Because of the digitization of money, financial transactions, banks, fraudsters now have a limitless number of possibilities to perpetrate crime from behind a screen, anywhere around the world. Fraud has a broad influence, with direct ramifications for business and the economy. It is of great worry to cybercrime organizations as recent studies have proven that ML algorithms may successfully be utilized to identify fraudulent transactions in massive amounts of payment data. Such techniques may identify fraudulent transactions in real time, which human auditors may miss. In this research, we apply supervised ML algorithms to the issue of fraud identification by analyzing simulated financial transaction data that is available to the public. Our aim is to show how supervised ML methods may be utilized to successfully identify data with extreme class disproportion. By way of example, we show how exploratory analysis may be utilized to identify fraudulent from real purchases. We also show that Random Forest outperform Logistic Regression when applied to a clearly distinguished dataset.

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Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms

Cited by 14 publications

References 32 publications

Financial Anti-Fraud Based on Dual-Channel Graph Attention Network

Financial Anti-Fraud Based on Dual-Channel Graph Attention Network

Try Dam: Digital Checkers Game Application Based on Machine Learning

Credit Card Fraud Identification using Logistic Regression and Random Forest

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