A deep learning model for behavioural credit scoring in banks

Ala’raj, Maher; Abbod, Maysam F.; Majdalawieh, Munir; Jum’a, Luay

doi:10.1007/s00521-021-06695-z

Cited by 18 publications

(5 citation statements)

References 60 publications

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“…LSTM is an advanced recurrent neural network designed to solve the difficulties of standard recurrent neural networks in processing long-term dependent information (Ala'raj et al, 2022). The core of LSTM lies in its internal gating mechanism, including forget gate, input gate, and output gate, which control the inflow and outflow of information (Adisa et al, 2022).…”

Section: Lstm (Long Short-term Memory) Network Modelmentioning

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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Section: Lstm (Long Short-term Memory) Network Modelmentioning

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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“…Moreover, recent developments in machine learning have led to many models being learned directly from data, to score the credit worthy-ness of people for example (Ala’raj et al, 2022 ). The process of generating these models, where deep learning is employed, can itself be considered an optimization process (Bennett & Parrado-Hernández, 2006 ).…”

Section: Object Limitmentioning

confidence: 99%

Limits of Optimization

Carissimo

Korecki

2023

Minds & Machines

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Optimization is about finding the best available object with respect to an objective function. Mathematics and quantitative sciences have been highly successful in formulating problems as optimization problems, and constructing clever processes that find optimal objects from sets of objects. As computers have become readily available to most people, optimization and optimized processes play a very broad role in societies. It is not obvious, however, that the optimization processes that work for mathematics and abstract objects should be readily applied to complex and open social systems. In this paper we set forth a framework to understand when optimization is limited, particularly for complex and open social systems.

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“…Recent developments in AI are essential to progress in fields such as recommendation systems [98,99,100], autonomous driving [101,102,103] or robotics [104,105,106]. Moreover, AI's success story has not excluded high-stakes decision-making tasks like medical diagnosis [107,108,109], credit scoring [110,111,112], jurisprudence [113,114] or recruiting and hiring decisions [115,116], However, the behavior and decision-making processes of modern AI systems are often not understandable, so they are frequently considered black boxes. Deploying such black-box models presents a serious dilemma in certain safety-critical domains, for instance, public health or finance [117].…”

Section: Introductionmentioning

confidence: 99%

Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations

Rong,

Leemann,

Nguyen

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

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Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In this paper, we explore how human-computer interaction (HCI) and AI researchers conduct user studies in XAI applications based on a systematic literature review. After identifying and thoroughly analyzing 97 core papers with human-based XAI evaluations over the past five years, we categorize them along the measured characteristics of explanatory methods, namely trust, understanding, usability, and human-AI collaboration performance. Our research shows that XAI is spreading more rapidly in certain application domains, such as recommender systems than in others, but that user evaluations are still rather sparse and incorporate hardly any insights from cognitive or social sciences. Based on a comprehensive discussion of best practices, i.e., common models, design choices, and measures in user studies, we propose practical guidelines on designing and conducting user studies for XAI researchers and practitioners. Lastly, this survey also highlights several open research directions, particularly linking psychological science and human-centered XAI.

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A deep learning model for behavioural credit scoring in banks

Cited by 18 publications

References 60 publications

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

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

Limits of Optimization

Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations

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