Machine learning predictivity applied to consumer creditworthiness

Aniceto, Maisa Cardoso; Barboza, Flávio; Kimura, Herbert

doi:10.1186/s43093-020-00041-w

Cited by 21 publications

(7 citation statements)

References 33 publications

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“…Other authors also develop and compare different models for credit risk assessment: SVM, DTs, AdaBoost, RF, logistic regression, ANN, and XGBoost. Of these, the different models with greater predictive accuracy are obtained by each author: AdaBoost and RF [35], DT [36]- [40]. On the other hand, Putri et al [41] propose to analyze credit risk using SMV.…”

Section: Ai and ML Applications And Algorithms Proposed In The Bankin...mentioning

confidence: 99%

Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector

Jáuregui-Velarde,

Andrade-Arenas,

Molina-Velarde

et al. 2024

IJECE

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This paper reviews the advances, challenges, and approaches of artificial intelligence (AI) and machine learning (ML) in the banking sector. The use of these technologies is accelerating in various industries, including banking. However, the literature on banking is scattered, making a global understanding difficult. This study reviewed the main approaches in terms of applications and algorithmic models, as well as the benefits and challenges associated with their implementation in banking, in addition to a bibliometric analysis of variables related to the distribution of publications and the most productive countries, as well as an analysis of the co-occurrence and dynamics of keywords. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, forty articles were selected for review. The results indicate that these technologies are used in the banking sector for customer segmentation, credit risk analysis, recommendation, and fraud detection. It should be noted that credit analysis and fraud detection are the most implemented areas, using algorithms such as random forests (RF), decision trees (DT), support vector machines (SVM), and logistic regression (LR), among others. In addition, their use brings significant benefits for decision-making and optimizing banking operations. However, the handling of substantial amounts of data with these technologies poses ethical challenges.

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Section: Ai and ML Applications And Algorithms Proposed In The Bankin...mentioning

confidence: 99%

Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector

Jáuregui-Velarde,

Andrade-Arenas,

Molina-Velarde

et al. 2024

IJECE

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“…The goals we delegate to AI are no longer inconsequential. They can be used to identify potential threats [46], [47], identify trends in seasonal influenza [48], [49], whether someone is a good job candidate [50], or determine a person's creditworthiness [51].…”

Section: B Fear and Loathing Of Autonomymentioning

confidence: 99%

Designing AI Using a Human-Centered Approach: Explainability and Accuracy Toward Trustworthiness

Schoenherr

Michael

Rivas

et al. 2023

IEEE Trans. Technol. Soc.

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One of the major criticisms of Artificial Intelligence is its lack of explainability. A claim is made by many critics that without knowing how an AI may derive a result or come to a given conclusion, it is impossible to trust in its outcomes. This problem is especially concerning when AI-based systems and applications fail to perform their tasks successfully. In this Special Issue Editorial, we focus on two main areas, explainable AI (XAI) and accuracy, and how both dimensions are critical to building trustworthy systems. We review prominent XAI design themes, leading to a reframing of the design and development effort that highlights the significance of the human, thereby demonstrating the importance of human-centered AI (HCAI). The HCAI approach advocates for a range of deliberate design-related decisions, such as those pertaining to multi-stakeholder engagement and the dissolving of disciplinary boundaries. This enables the consideration and integration of deep interdisciplinary knowledge, as evidenced in our example of social cognitive approaches to AI design. This Editorial then presents a discussion on ways forward, underscoring the value of a balanced approach to assessing the opportunities, risks and responsibilities associated with AI design. We conclude by presenting papers in the Special Issue and their contribution, pointing to future research endeavors.

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“…Overall, these findings suggest that there is potential for businesses to develop default prediction models by experimenting with machine learning approaches. 8 This study focuses on using machine and deep learning models with real-world data to estimate loan default probability. The most significant features from multiple models are selected and used to compare the performance of Random Forest and decision tree classifiers.…”

Section: Literature Surveymentioning

confidence: 99%

“…The results indicated that AdaBoost and Random Forest performed better than other models, while SVM models performed poorly when using both linear and nonlinear kernels. Overall, these findings suggest that there is potential for businesses to develop default prediction models by experimenting with machine learning approaches 8 . This study focuses on using machine and deep learning models with real‐world data to estimate loan default probability.…”

Section: Literature Surveymentioning

confidence: 99%

Analyzing the impact of loan features on bank loan prediction using Random Forest algorithm

Dansana

Patro

Mishra

et al. 2023

Engineering Reports

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Loans are a crucial source of income for the financial sector, but they also come with significant financial risks. The interest on loans constitutes a significant portion of a bank's assets. The demand for loans is growing worldwide, and organizations are devising efficient business strategies to attract more clients. Every day, a large number of people apply for loans for various reasons, but not all of them can be approved due to the risk of loan default. It is not uncommon for people to default on their loans, causing significant losses to banks. The purpose of this article is to determine whether to grant loans to specific individuals or organizations. The Random Forest Regressor model has been utilized to measure performance and identify suitable customers for loan approval. The model suggests that banks should not only target affluent clients but also consider other customer characteristics that are critical in credit granting and predicting loan default. The research examines various loan approval parameters such as gender, educational qualification, employment type, business type, loan term, and marital status. Additionally, the study analyzes the number of approved, drawn, and rejected loans, which provides valuable insights into loan approval and prediction.

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Machine learning predictivity applied to consumer creditworthiness

Cited by 21 publications

References 33 publications

Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector

Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector

Designing AI Using a Human-Centered Approach: Explainability and Accuracy Toward Trustworthiness

Analyzing the impact of loan features on bank loan prediction using Random Forest algorithm

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