Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
66,424
result(s) for
"Credit scoring"
Sort by:
Your credit score : how to improve the 3-digit number that shapes your financial future
\"Improve your credit score, for real, with the #1 best-selling guide you can trust! Today, a good credit score is essential for getting credit, getting a job, even getting car insurance or a cellphone. Now, best selling journalist Liz Pulliam Weston has thoroughly updated her top-selling guide to credit scores, with crucial new information for protecting (or rebuilding) yours. Weston thoroughly covers brand-new laws and rules surrounding credit scoring - including some surprising good news and some frightening new risks.\" -- Publisher annotation.
Book
An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments
Machine learning and artificial intelligence have achieved a human-level performance in many application domains, including image classification, speech recognition and machine translation. However, in the financial domain expert-based credit risk models have still been dominating. Establishing meaningful benchmark and comparisons on machine-learning approaches and human expert-based models is a prerequisite in further introducing novel methods. Therefore, our main goal in this study is to establish a new benchmark using real consumer data and to provide machine-learning approaches that can serve as a baseline on this benchmark. We performed an extensive comparison between the machine-learning approaches and a human expert-based model—FICO credit scoring system—by using a Survey of Consumer Finances (SCF) data. As the SCF data is non-synthetic and consists of a large number of real variables, we applied two variable-selection methods: the first method used hypothesis tests, correlation and random forest-based feature importance measures and the second method was only a random forest-based new approach (NAP), to select the best representative features for effective modelling and to compare them. We then built regression models based on various machine-learning algorithms ranging from logistic regression and support vector machines to an ensemble of gradient boosted trees and deep neural networks. Our results demonstrated that if lending institutions in the 2001s had used their own credit scoring model constructed by machine-learning methods explored in this study, their expected credit losses would have been lower, and they would be more sustainable. In addition, the deep neural networks and XGBoost algorithms trained on the subset selected by NAP achieve the highest area under the curve (AUC) and accuracy, respectively.
Journal Article
Credit intelligence : boosting your credit smarts
The authors \"provide you with a roadmap to credit intelligence by sharing their shopping adventures and lessons learned about credit as 'Olympic level' shoppers who have fallen into and pulled each other out of many of the traps and pitfalls surrounding the use of credit and the behavioral buying manipulations by retailers\"--Amazon.com.
Book
Deep reinforcement learning based on balanced stratified prioritized experience replay for customer credit scoring in peer-to-peer lending
In recent years, deep reinforcement learning (DRL) models have been successfully utilised to solve various classification problems. However, these models have never been applied to customer credit scoring in peer-to-peer (P2P) lending. Moreover, the imbalanced class distribution in experience replay, which may affect the performance of DRL models, has rarely been considered. Therefore, this article proposes a novel DRL model, namely a deep Q-network based on a balanced stratified prioritized experience replay (DQN-BSPER) model, for customer credit scoring in P2P lending. Firstly, customer credit scoring is formulated as a discrete-time finite-Markov decision process. Subsequently, a balanced stratified prioritized experience replay technology is presented to optimize the loss function of the deep Q-network model. This technology can not only balance the numbers of minority and majority experience samples in the mini-batch by using stratified sampling technology but also select more important experience samples for replay based on the priority principle. To verify the model performance, four evaluation measures are introduced for the empirical analysis of two real-world customer credit scoring datasets in P2P lending. The experimental results show that the DQN-BSPER model can outperform four benchmark DRL models and seven traditional benchmark classification models. In addition, the DQN-BSPER model with a discount factor
γ
of 0.1 has excellent credit scoring performance.
Journal Article
THE NORMS OF ALGORITHMIC CREDIT SCORING
This article examines the growth of algorithmic credit scoring and its implications for the regulation of consumer credit markets in the UK. It constructs a frame of analysis for the regulation of algorithmic credit scoring, bound by the core norms underpinning UK consumer credit and data protection regulation: allocative efficiency, distributional fairness and consumer privacy (as autonomy). Examining the normative trade-offs that arise within this frame, the article argues that existing data protection and consumer credit frameworks do not achieve an appropriate normative balance in the regulation of algorithmic credit scoring. In particular, the growing reliance on consumers’ personal data by lenders due to algorithmic credit scoring, coupled with the ineffectiveness of existing data protection remedies has created a data protection gap in consumer credit markets that presents a significant threat to consumer privacy and autonomy. The article makes recommendations for filling this gap through institutional and substantive regulatory reforms.
Journal Article
Quantum Machine Learning for Credit Scoring
This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs (350) compared to its classical counterpart (3500) for a similar predictive accuracy. However, we observed a decrease in performance when expanding the model beyond 12 qubits or when adding additional quantum classifier blocks. This paper also considers practical challenges faced when deploying such models on quantum simulators and actual quantum computers. Overall, our quantum–classical hybrid model for credit scoring reveals its potential in industry, despite encountering certain scalability limitations and practical challenges.
Journal Article
Research on Personal Credit Scoring Based on Histogram of Oriented Gradient
The topic of personal credit scoring is a prerequisite for the development of personal credit business and has become an important research area in the field of financial risk management. However, little attention has been paid to the field of personal credit scoring research on how to introduce additional features in improving personal credit scoring services. To solve this problem, a new method based on Histogram of Gradients Oriented (HOG) for personal credit scoring is proposed for enhancing the scoring capability. The proposed method utilizes the gradient relationship between different features of personal credit data and introduces additional features on the basis of the original features of personal credit data to construct a set of personal credit datasets based on HOG. The experimental results show that the personal credit dataset based on HOG not only has higher values but also better stability in terms of the four metrics (accuracy, recall, precision, and F1-value) for personal credit scoring when compared to the original personal credit dataset. Therefore, the proposed personal credit scoring method has been demonstrated to be both reliable and feasible.
Journal Article
Developing a multi-criteria sustainable credit score system using fuzzy BWM and fuzzy TOPSIS
Sustainability has emerged as a dominating paradigm across global institutions as a critical component for their prospects. Organisations all across the globe must commit to strengthen the values as a participant in sustainable development. Financial institutions are no exception; they are also being pushed to undertake many sustainable initiatives, such as increasing socially meaningful, relevant, and sustainable projects. In addition, they may contribute to sustainable growth by enacting a green banking policy. To promote the green financing strategy, this study proposes a multi-criteria sustainable credit scoring model considering triple bottom line attributes (economic, environmental, and social) besides managerial attributes. The model is built on a novel hybrid approach that combines the fuzzy best–worst method (BWM) with the fuzzy technique for order preferences by similarity to an ideal solution (TOPSIS). The fuzzy BWM was used to weigh factors, while the fuzzy TOPSIS was utilised to evaluate borrowers. The integration of fuzzy set theory assisted in overcoming decision-making ambiguity. An empirical analysis was performed to demonstrate the utility of the proposed model. According to the study’s findings, the most important attribute for sustainable credit scoring is environmental and social sustainability and financial sustainability. On policy implications, regulators could also use the framework as a benchmark to counsel financial institutions on how to include different sustainable criteria into their credit lending process. Furthermore, financial institutions could use the proposed technique as a part of a sustainable lending policy to identify potential borrowers engaged in sustainable business.
Journal Article
Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review
Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking transactions. Machine learning-based technology is giving a new hope to these individuals. However, it is the banking or non-banking institutions that decide how they will adopt this advanced technology, to have reduced human biases in loan decision making. Therefore, the scope of this study is to highlight the various AI-ML- based methods for credit scoring and their gaps currently in practice by banking or non-banking institutions. For this study, systematic literature review methods have been applied; existing research articles have been empirically reviewed with an attempt to identify and compare the best fit AI-ML-based model adopted by various financial institutions worldwide. The main purpose of this study is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history. However, it would be interesting to recognize further how the financial institutions could be able to blend the traditional and digital methods successfully without any ethical challenges.
Journal Article
A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS
Small- and medium-sized enterprises (SMEs) have a crucial influence on the economic development of every nation, but access to formal finance remains a barrier. Similarly, financial institutions encounter challenges in the assessment of SMEs’ creditworthiness for the provision of financing. Financial institutions employ credit scoring models to identify potential borrowers and to determine loan pricing and collateral requirements. SMEs are perceived as unorganized in terms of financial data management compared to large corporations, making the assessment of credit risk based on inadequate financial data a cause for financial institutions’ concern. The majority of existing models are data-driven and have faced criticism for failing to meet their assumptions. To address the issue of limited financial record keeping, this study developed and validated a system to predict SMEs’ credit risk by introducing a multicriteria credit scoring model. The model was constructed using a hybrid best–worst method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Initially, the BWM determines the weight criteria, and TOPSIS is applied to score SMEs. A real-life case study was examined to demonstrate the effectiveness of the proposed model, and a sensitivity analysis varying the weight of the criteria was performed to assess robustness against unpredictable financial situations. The findings indicated that SMEs’ credit history, cash liquidity, and repayment period are the most crucial factors in lending, followed by return on capital, financial flexibility, and integrity. The proposed credit scoring model outperformed the existing commercial model in terms of its accuracy in predicting defaults. This model could assist financial institutions, providing a simple means for identifying potential SMEs to grant credit, and advance further research using alternative approaches.
Journal Article