Using data mining and neural networks techniques to propose a new hybrid customer behaviour analysis and credit scoring model in banking services based on a developed RFM analysis method

Alborzi, Mahmood; Khanbabaei, Mohammad

doi:10.1504/ijbis.2016.078020

Cited by 19 publications

(9 citation statements)

References 32 publications

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“…The sub-theme, AI and credit (15 papers), covers the use of AI technology, such as machine learning and data mining, to improve credit scoring, analysis, and granting processes. For instance, Alborzi and Khanbabaei ( 2016 ) examined the use of data mining neural network techniques to develop a customer credit scoring model. Post-2013, there has been a noticeable increase in investigating how AI improves processes that go beyond credit analysis.…”

Section: Resultsmentioning

confidence: 99%

Utilization of artificial intelligence in the banking sector: a systematic literature review

2022

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This study provides a holistic and systematic review of the literature on the utilization of artificial intelligence (AI) in the banking sector since 2005. In this study, the authors examined 44 articles through a systematic literature review approach and conducted a thematic and content analysis on them. This review identifies research themes demonstrating the utilization of AI in banking, develops and classifies sub-themes of past research, and uses thematic findings coupled with prior research to propose an AI banking service framework that bridges the gap between academic research and industry knowledge. The findings demonstrate how the literature on AI and banking extends to three key areas of research: Strategy, Process, and Customer. These findings may benefit marketers and decision-makers in the banking sector to formulate strategic decisions regarding the utilization and optimization of value from AI technologies in the banking sector. This study also provides opportunities for future research.

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Section: Resultsmentioning

confidence: 99%

Utilization of artificial intelligence in the banking sector: a systematic literature review

2022

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“…Kaneko and Yada, on the other hand, using the data on shopping transactions and browsing in the store belonging to a group of supermarket customers, they found that the customers buy more products as they browse through the store. This collected data is the data revealed by matching the customers' POS data with an RFID tag attached to the shopping carts that customers use while shopping (Alborzi and Khanbabaei, 2016;Culotta and Cutler, 2016;Ghazavi and Lotfi, 2016;Kaneko and Yada, 2016).…”

Section: Findings From the Literature Reviewmentioning

confidence: 99%

Industry 4.0 Perception Regarding to New Developments and New Trends of Industries

Erdil¹

2021

European Journal of Science and Technology

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The purpose of this research is to assess the core principles of Industry 4.0, its advantages, literature review, and effective implementation techniques. Searching for published publications may demonstrate recent developments of industries with big data of production system and technology. Industrialization is a catch-all term for a wide range of modern automated systems, data exchanges, and manufacturing technologies. This revolution is a set of ideals comprised of the internet of things, online services, and cyber-physical systems. While the Industry 4.0 model represents a revolution in every aspect, it also delivers numerous breakthroughs, transformations, and adjustments. This research examines the evolution, technical advances, and benefits of Industrial 4.0, gives a brief background to this configuration and its use in industry implementations, and emphasizes the key components influencing successfully businesses. Furthermore, it is underlined those organizational innovations are required for the industry 4.0 revolution in terms of product, process, and business advancements. As a result, a significant deal of innovation is necessary in many sectors before the industry 4.0 model can be implemented. In product, process, business, and operational terms, innovation refers to variety that creates additional value. To that end, the ideas of Industry 4.0 and innovation are described briefly, followed by an examination of innovation through the lens of Industrial 4.0, with illustrations and concepts provided. It is abundantly clear through publications that are being searched those sectors and industries are making use of big data in the production environment and information. This research reflects on the developments of framework-structure in the enviroment of industry for the businesscompanies. The research also contains the aspect-application of the industry 4.0 technology depending on manufacturing system and improvement phases of manufacturing-production of the company. The application via Industry 4.0 technology of a white goods manufacturing company was evaluated on behalf of manufacturing performance and effectiveness of this manufacturing enterprise according to inputs and outputs components of manufacturing system for the analysis in terms of operation management and the business expectation.

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“…Most of the statistical and data mining techniques at the state of the art can be used in order to build credit scoring models [14,15], e.g., linear discriminant models [16], logistic regression models [3], neural network models [17,18], genetic programming models [19,20], k-nearest neighbor models [21], and decision tree models [22,23].…”

Section: Credit Scoring Modelsmentioning

confidence: 99%

An Entropy Based Algorithm for Credit Scoring

Saia¹,

Carta²

2016

Lecture Notes in Business Information Processing

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Part 6: Decision Support in EISInternational audienceThe request of effective credit scoring models is rising in these last decades, due to the increase of consumer lending. Their objective is to divide the loan applicants into two classes, reliable or unreliable, on the basis of the available information. The linear discriminant analysis is one of the most common techniques used to define these models, although this simple parametric statistical method does not overcome some problems, the most important of which is the imbalanced distribution of data by classes. It happens since the number of default cases is much smaller than that of non-default ones, a scenario that reduces the effectiveness of the machine learning approaches, e.g., neural networks and random forests. The in Maximum Entropy (DME) approach proposed in this paper leads toward two interesting results: on the one hand, it evaluates the new loan applications in terms of maximum entropy difference between their features and those of the non-default past cases, using for the model training only these last cases, overcoming the imbalanced learning issue; on the other hand, it operates proactively, overcoming the cold-start problem. Our model has been evaluated by using two real-world datasets with an imbalanced distribution of data, comparing its performance to that of the most performing state-of-the-art approach: random forests

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Using data mining and neural networks techniques to propose a new hybrid customer behaviour analysis and credit scoring model in banking services based on a developed RFM analysis method

Cited by 19 publications

References 32 publications

Utilization of artificial intelligence in the banking sector: a systematic literature review

Utilization of artificial intelligence in the banking sector: a systematic literature review

Industry 4.0 Perception Regarding to New Developments and New Trends of Industries

An Entropy Based Algorithm for Credit Scoring

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