A Big Data Deep Learning Approach for Credit Card Fraud Detection

Illanko, Kandasamy; Soleymanzadeh, Raha; Fernando, Xavier

doi:10.1007/978-981-19-0898-9_50

Cited by 5 publications

(1 citation statement)

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“…However, as our nation has witnessed rapid advancements in science, technology, and theoretical research in recent years, China has embarked on decades of progress in this domain. A recent investigation harnessed a deep learning model, a convolutional neural network (CNN) specifically (Illanko et al, 2022). These deep learning models excel in credit assessment due to their innate ability to automatically extract crucial credit-related features, thus augmenting the model's overall performance.…”

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confidence: 99%

Application of AdaBound-Optimized XGBoost-LSTM Model for Consumer Credit Assessment in Banking Industries

Fan,

Wang,

2024

Journal of Organizational and End User Computing

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Consumer credit assessment has always been a crucial concern in the financial industry. It involves evaluating an individual's credit history and their ability to repay loans, playing a pivotal role in the risk management and lending decisions made by credit institutions. In the present landscape, traditional credit assessment methods confront various shortcomings. Firstly, they typically only consider static features and are unable to capture the dynamic changes in an individual's credit profile over time. Secondly, traditional methods struggle with processing complex time series data, failing to fully exploit the importance of time-related information. To address these challenges, we propose an innovative solution – the XGBoost-LSTM model optimized with the AdaBound algorithm. This hybrid model combines two powerful machine learning techniques, XGBoost and LSTM, to leverage both static and dynamic features effectively.

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confidence: 99%