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This study conducts a comprehensive exploration of the neurocognitive processes underlying consumer credit decision-making using cutting-edge techniques from neuroscience and machine learning (ML). Employing functional Near-Infrared Spectroscopy (fNIRS), the research examines the hemodynamic responses of participants while evaluating diverse credit offers. The experimental phase of this study investigates the hemodynamic responses collected from 39 healthy participants with respect to different loan offers. This study integrates fNIRS data with advanced ML algorithms, specifically Extreme Gradient Boosting, CatBoost, Extra Tree Classifier, and Light Gradient Boosted Machine, to predict participants' credit decisions based on prefrontal cortex (PFC) activation patterns. Findings reveal distinctive PFC regions correlating with credit behaviors, including the dorsolateral prefrontal cortex (dlPFC) associated with strategic decision-making, the orbitofrontal cortex (OFC) linked to emotional valuations, and the ventromedial prefrontal cortex (vmPFC) reflecting brand integration and reward processing. Notably, the right dorsomedial prefrontal cortex (dmPFC) and the right vmPFC contribute to positive credit preferences. This interdisciplinary approach bridges neuroscience, machine learning and finance, offering unprecedented insights into the neural mechanisms guiding financial choices regarding different loan offers. The study's predictive model holds promise for refining financial services and illuminating human financial behavior within the burgeoning field of neurofinance. The work exemplifies the potential of interdisciplinary research to enhance our understanding of human financial decision-making.

In this paper, we adopt an ensemble machine learning framework—a Light Gradient Boosting Machine (LightGBM) and develop an algorithmic credit rating prediction model by innovatively incorporating firms’ extra supply chain information both from suppliers and customers. By utilizing data from listed firms in North America from 2006 to 2020, our results find that the accuracy of the prediction improves by incorporating supply chain information in the previous year, compared to the inclusion of supply chain information in the current year. Besides, we identify the most important factors the stakeholders should pay attention to. Interestingly, we show that the models utilizing the current year’s information perform better after the strike of the COVID-19, indicating that the epidemics may have accelerated the spread of credit risk along the supply chain. Furthermore, supplier information is found to be more valuable than customer information in predicting the focal firm’s credit rating. A comparison of our framework with the existing methods vindicates the robustness of our main results.

Over the past years, studies shed light on how social norms and perceptions potentially affect loan repayments, with overtones for strategic default. Motivated by this strand of the literature, we incorporate collective social traits in predictive frameworks on credit card delinquencies. We propose the use of a two-stage framework. This allows us to segment a market into homogeneous sub-populations at the regional level in terms of social traits, which may proxy for perceptions and potentially unravelled behaviours. On these formed sub-populations, delinquency prediction models are fitted at a second stage. We apply this framework to a big dataset of 3.3 million credit card holders spread in 12 UK NUTS1 regions during the period 2015–2019. We find that segmentation based on social traits yields efficiency gains in terms of both computational and predictive performance compared to prediction in the overall population. This finding holds and is sustained in the long run for different sub-samples, lag counts, class imbalance correction or alternative clustering solutions based on individual and socio-economic attributes.

Peer-to-peer lending (P2P lending) has proliferated in recent years thanks to Fintech and big data advancements. However, P2P lending platforms are not tightly governed by relevant laws yet, as their development speed has far exceeded that of regulations. Therefore, P2P lending operations are still subject to risks. This paper proposes prediction models to mitigate the risks of default and asymmetric information on P2P lending platforms. Specifically, we designed sophisticated procedures to pre-process mass data extracted from Lending Club in 2018 Q3–2019 Q2. After that, three statistical models, namely, Logistic Regression, Bayesian Classifier, and Linear Discriminant Analysis (LDA), and five AI models, namely, Decision Tree, Random Forest, LightGBM, Artificial Neural Network (ANN), and Convolutional Neural Network (CNN), were utilized for data analysis. The loan statuses of Lending Club’s customers were rationally classified. To evaluate the models, we adopted the confusion matrix series of metrics, AUC-ROC curve, Kolmogorov–Smirnov chart (KS), and Student’s t-test. Empirical studies show that LightGBM produces the best performance and is 2.91% more accurate than the other models, resulting in a revenue improvement of nearly USD 24 million for Lending Club. Student’s t-test proves that the differences between models are statistically significant.

This paper proposes novel multi-objective optimization strategies to develop a weighted ensemble model. The comparison of the performance of the proposed strategies against simulated data suggests that the multi-objective strategy based on joint entropy is superior to other proposed strategies. For the application, generalization, and practical implications of the proposed approaches, we implemented the model on two real datasets related to the prediction of credit risk default and the adoption of the innovative business model by firms. The scope of this paper can be extended in ordering the solutions of the proposed multi-objective strategies and can be generalized for other similar predictive tasks.

With the improving banking sector in recent times and the increasing trend of taking loans, a large population applies for bank loans. But one of the major problem banking sectors face in this ever-changing economy is the increasing rate of loan defaults, and the banking authorities are finding it more difficult to correctly assess loan requests and tackle the risks of people defaulting on loans. The two most critical questions in the banking industry are (i) How risky is the borrower? and (ii) Given the borrower's risk, should we lend him/her? In light of the given problems, this paper proposes two machine learning models to predict whether an individual should be given a loan by assessing certain attributes and therefore help the banking authorities by easing their process of selecting suitable people from a given list of candidates who applied for a loan. This paper does a comprehensive and comparative analysis between two algorithms (i) Random Forest, and (ii) Decision Trees. Both the algorithms have been used on the same dataset and the conclusions have been made with results showing that the Random Forest algorithm outperformed the Decision Tree algorithm with much higher accuracy.

The credit card customer churn rate is the percentage of a bank’s customers that stop using that bank’s services. Hence, developing a prediction model to predict the expected status for the customers will generate an early alert for banks to change the service for that customer or to offer them new services. This paper aims to develop credit card customer churn prediction by using a feature-selection method and five machine learning models. To select the independent variables, three models were used, including selection of all independent variables, two-step clustering and k-nearest neighbor, and feature selection. In addition, five machine learning prediction models were selected, including the Bayesian network, the C5 tree, the chi-square automatic interaction detection (CHAID) tree, the classification and regression (CR) tree, and a neural network. The analysis showed that all the machine learning models could predict the credit card customer churn model. In addition, the results showed that the C5 tree machine learning model performed the best in comparison with the three developed models. The results indicated that the top three variables needed in the development of the C5 tree customer churn prediction model were the total transaction count, the total revolving balance on the credit card, and the change in the transaction count. Finally, the results revealed that merging the multi-categorical variables into one variable improved the performance of the prediction models.

The financial sector has experienced swift growth over recent years, leading to the escalating prominence of credit risk among publicly traded companies. Consequently, forecasting credit risk for these firms has emerged as a critical task for banks, regulatory bodies, and investors. Traditional models include the z-score, the logit (logistic regression model), the kernel-based virtual machine (KVM), and neural network approaches. Nevertheless, the outcomes from these methods have often fallen short of expectations. Three major challenges in previous works are feature selection, imbalanced classification, and hyperparameter optimization. This paper presents a method for credit risk prediction for listed companies that uses an off-policy proximal policy optimization (PPO) algorithm for feature selection and imbalanced classification. The off-policy PPO, a reinforcement learning (RL) approach, enhances sample efficiency by more effectively utilizing past experiences during policy updates. This approach improves feature selection and the management of imbalanced classification by optimizing data use, thereby enhancing model training outcomes. Moreover, we use the Bayesian optimization hyperband (BOHB) approach to refine the hyperparameters of the method. BOHB merges Bayesian optimization and Hyperband, significantly speeding up the optimization process. We assess our model using the China Stock Market and Accounting Research (CSMAR), MorningStar, KMV default, Give Me Some Credit (GMSC), and the University of California, Irvine Credit Card Default (UCICCD) datasets. Our experimental findings demonstrate the excellence of the model over existing state-of-the-art models, achieving F-measures of 90.763%, 86.358%, 87.047%, 90.576%, and 89.485% on these datasets. These findings validate the efficiency of the method in economic settings, signifying a major progression in systems for predicting credit risk and enhancing investigative approaches.

This study tries to identify the accuracy of individual investors’ capability to predict a borrower’s creditworthiness in peer-to-peer lending markets and examine whether their ability is likely to evolve over time. The results of this study show that there is no significant difference between the predictive power of investors’ ex-ante funding decision model and that of the ex-post repayment model over a borrower’s repayment performance. Furthermore, the predictive power of investors’ ex-ante funding decision over a borrower’s repayment performance is shown to improve over time. It is also found that the main reason why investors’ predictive power improves over time is because investors can assess more accurately the information provided by the platform operator and describe the borrower's characteristics. The results of this study are important as they confirm the possibility of optimizing and streamlining the P2P lending market, through the evolution of investors’ decision making.

Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.