Credit Scoring Methods Research Articles

Credit scoring: Does XGboost outperform logistic regression?A test on Italian SMEs

The old-fashioned logistic regression is still the most used method for credit scoring. Recent developments have evolved new instruments coming from the machine learning approach, including random forests.In this paper, we tested the efficiency of logistic regression and XGBoost methods for default forecasting on a sample of 35,535 cases from 7 different business sectors of Italian SMEs, on a set of 28 banking variables and 55 balance sheet ratios for verifying which approach is better supporting the lending decisions.With this aim, we developed an efficiency index for measuring each model's capability to correctly select good borrowers, balancing the different effects of refusing the loan to a good customer and lending to a defaulter. Also, we computed the balancing spread to quantify the different models' efficiency in terms of credit costs for the borrower firms.Results show that different sectors report different results. However, generally speaking, the two methods report similar capabilities, while the cutoff setting can make a substantial difference in the actual use of those models for lending decisions.

Innovative Credit Scoring Methods in India by the Fintech Companies to Increase Financial Inclusivity for Digital Lending

Abstract Fintech is part of the digital revolution of India which aims towards increasing the Indian economy and financial inclusivity of the population. Fintech has done significant contributions in digital lending and will continue to do in future, one of the contributions is in the unique model created for credit scoring of minorities who do not have a credit history or not eligible to attain credit score through the traditional method. The strategic changes in the new credit scoring method on the basis of machine learning and artificial intelligence can provide more accurate results and provide reports for the isolated population who did not have access to credit earlier. - Methodology: The article has been written with in depth research on the Indian Fintech market and the current state of Indian digital lending. Begin with introduction of fintech evolution in India and what does it mean. Post which it provides in depth understanding about the state of digital lending and why is there a need of amendment in the credit scoring method which is used for digital lending. The influence of fintech in credit scoring has been explored via existing published research papers and statistics from World Bank. In the end, there has been suggestions in form of inputs from the author for how India can reach financial inclusivity. - Purpose: The purpose of the article is to curate information about how the Indian economy should use fintech for its advantage. The research paper aims towards outlining potential limitations too if the non-traditional credit scoring methods are used as a major factor in digital lending. Overall, the paper seeks to understand what was the major reason behind the lack of inclusivity in digital lending in India and how can it be overcome. - Findings: The research paper helps to understand that Fintech has done significant contributions in digital lending and will continue to do in future, one of the contributions is in the unique model created for credit scoring of minorities who do not have a credit history or not eligible to attain credit score through the traditional method. The strategic changes in the new credit scoring method on the basis of machine learning and artificial intelligence are able to provide more accurate results and provide reports for the isolated population who did not have access to credit earlier. - Value: The research paper has cited all the sources which has used for the research on the topic in MLA9. It is a 100% AI free paper and has been written entirely by the author without the intervention of any third party in it.

How Credit Risk Management in Australia Can Affect Financial Institutions Growth: A Study

Ineffective credit risk management methods were largely responsible for the collapse, as well as financial problems, of many financial institutions. This particular research is designed to evaluate how insufficient credit risk management brought about the banking crisis of Australia in 2003/2004 determine other contributing factors. It found that inability to effectively manage credit risk was the most important element in the crisis, leading to ineffective management, insufficient risk control, poorly designed strategies for business expanding, persistent liquidity issues, external currency deficiency, as well as diversion from core banking pursuits to speculative non-banking activities. It suggests banks develop and implement credit scoring and assessment methods, update insider lending practices, and adopt prudential business governance methods.

Using data mining to integrate recency-frequency-monetary value analysis and credit scoring methods for bank customer behaviour analysis

Using data mining to integrate recency-frequency-monetary value analysis and credit scoring methods for bank customer behaviour analysis

ИЗМЕРЕНИЕ КРЕДИТНОГО РИСКА ЗАЕМЩИКА МИКРОФИНАНСОВОЙ ОРГАНИЗАЦИИ НА ОСНОВЕ АГРЕГИРОВАННОГО ПОКАЗАТЕЛЯ ПРОСРОЧКИ

The work revealed the absence in the scientific literature of a relevant methodology for measuring the credit risk of a borrower of a microfinance organization, and this is the novelty of the study. An overview of the credit scoring methods used, including microfinance organizations, is provided. A methodology is being developed for measuring of the microfinance organization borrower’s credit risk based on the aggregated default indicator.

Using data mining to integrate Recency-Frequency-Monetary value (RFM) analysis and credit scoring methods for bank customer behavior analysis

Using data mining to integrate Recency-Frequency-Monetary value (RFM) analysis and credit scoring methods for bank customer behavior analysis

A hybrid model with novel feature selection method and enhanced voting method for credit scoring

With the in-depth application of artificial intelligence technology in the financial field, credit scoring models constructed by machine learning algorithms have become mainstream. However, the high-dimensional and complex attribute features of the borrower pose challenges to the predictive competence of the model. This paper proposes a hybrid model with a novel feature selection method and an enhanced voting method for credit scoring. First, a novel feature selection combined method based on a genetic algorithm (FSCM-GA) is proposed, in which different classifiers are used to select features in combination with a genetic algorithm and combine them to generate an optimal feature subset. Furthermore, an enhanced voting method (EVM) is proposed to integrate classifiers, with the aim of improving the classification results in which the prediction probability values are close to the threshold. Finally, the predictive competence of the proposed model was validated on three public datasets and five evaluation metrics (accuracy, AUC, F-score, Log loss and Brier score). The comparative experiment and significance test results confirmed the good performance and robustness of the proposed model.

A novel method for credit scoring based on feature transformation and ensemble model.

Credit scoring is a very critical task for banks and other financial institutions, and it has become an important evaluation metric to distinguish potential defaulting users. In this paper, we propose a credit score prediction method based on feature transformation and ensemble model, which is essentially a cascade approach. The feature transformation process consisting of boosting trees (BT) and auto-encoders (AE) is employed to replace manual feature engineering and to solve the data imbalance problem. For the classification process, this paper designs a heterogeneous ensemble model by weighting the factorization machine (FM) and deep neural networks (DNN), which can efficiently extract low-order intersections and high-order intersections. Comprehensive experiments were conducted on two standard datasets and the results demonstrate that the proposed approach outperforms existing credit scoring models in accuracy.

Deep learning for credit scoring: Do or don’t?

Developing accurate analytical credit scoring models has become a major focus for financial institutions. For this purpose, numerous classification algorithms have been proposed for credit scoring. However, the application of deep learning algorithms for classification has been largely ignored in the credit scoring literature. The main motivation for this research is to consider the appropriateness of deep learning algorithms for credit scoring. To this end two deep learning architectures are constructed, namely a multilayer perceptron network and a deep belief network, and their performance compared to that of two conventional methods and two ensemble methods for credit scoring. The models are then evaluated using a range of credit scoring data sets and performance measures. Furthermore, Bayesian statistical testing procedures are introduced in the context of credit scoring and compared to frequentist non-parametric testing procedures which have traditionally been considered best practice in credit scoring. This comparison will highlight the benefits of Bayesian statistical procedures and secure empirical findings. Two main conclusions emerge from comparing the different classification algorithms for credit scoring. Firstly, the ensemble method, XGBoost, is the best performing method for credit scoring of all the methods considered here. Secondly, deep neural networks do not outperform their shallower counterparts and are considerably more computationally expensive to construct. Therefore, deep learning algorithms do not seem to be appropriate models for credit scoring based on this comparison and XGBoost should be preferred over the other credit scoring methods considered here when classification performance is the main objective of credit scoring activities.

A Novel Method for Credit Scoring Based on Cost-Sensitive Neural Network Ensemble

Most existing studies on credit scoring adapted a concept of classifier ensemble for solving an imbalanced dataset. They apply resampling methods to generate multiple training subsets for constructing multiple base classifiers. However, this approach leads to several problems that degrade the classification performance, such as problems of information loss, model overfitting, and computational cost. Thus, we propose a novel ensemble approach for developing a credit scoring model based on a cost-sensitive neural network, called Cost-sensitive Neural Network Ensemble (CS-NNE). In the proposed approach, multiple class weights are adapted to original training data, enabling the multiple base neural networks to consider imbalanced classes. Following this approach, a high diversity of multiple base classifiers without consequent problems can be achieved. The approach's effectiveness is evaluated on five real-world credit datasets. Among them is a loan-requesting dataset provided by a financial institution in Thailand. The remaining datasets are publicly available and widely used by several existing studies. The experimental results showed that the proposed CS-NNE approach improves the predictive performance over a single neural network based on imbalanced credit datasets, e.g., Thai credit dataset, by achieving 1.36%, 15.67%, and 6.11% Area under the ROC Curve (AUC), Default Detection Rate (DDR), and G-Mean (GM), respectively, and achieving the best Misclassification Cost (MC). The proposed CS-NNE approach can effectively solve a class of imbalance problems and outperform many existing models. The prediction model can well compromise between classes of default (bad credit applicants) and non-default (good credit applicants), whereas existing approaches preferred a class of non-default over default loans (having high specificity and low DDR), resulting in NPL.

A novel tree-based dynamic heterogeneous ensemble method for credit scoring

Ensemble models have been extensively applied to credit scoring. However, advanced tree-based classifiers have been seldom utilized as components of ensemble models. Moreover, few studies have considered dynamic ensemble selection. To fill the research gap, this paper aims to develop a novel tree-based overfitting-cautious heterogeneous ensemble model (i.e., OCHE) for credit scoring which departs from existing literature on base models and ensemble selection strategy. Regarding base models, tree-based techniques are employed to acquire a balance between predictive accuracy and computational cost. In terms of ensemble selection, the proposed method can assign weights to base models dynamically according to the overfitting measure. Validated on five public datasets, the proposed approach is compared with several popular benchmark models and selection strategies on predictive accuracy and computational cost measures. For predictive accuracy, the proposed approach outperforms the benchmark models significantly in most cases based on the non-parametric significance test. It also performs marginally better than several state-of-the-art studies. Our proposal remains robust in several scenarios. In terms of computational cost, the proposed method provides acceptable performance and benefits from GPU acceleration considerably.

A COST-SENSITIVE LOGISTIC REGRESSION CREDIT SCORING MODEL BASED ON MULTI-OBJECTIVE OPTIMIZATION APPROACH

Credit scoring is an important process for peer-to-peer (P2P) lending companies as it determines whether loan applicants are likely to default. The aim of most credit scoring models is to minimize the classification error rate, which implies that all classification errors bear the same cost; however, in reality, there is a significant cost-sensitive problem in credit scoring methods. Therefore, in this paper, a new cost-sensitive logistic regression credit scoring model based on a multi-objective optimization approach is proposed that has two objectives in the cost-sensitive logistic regression process. The cost-sensitive logistic regression parameters are solved using a multiple objective particle swarm optimization (MOPSO) algorithm. In the empirical analysis, the proposed model was applied to the credit scoring of a Chinese famous P2P company, from which it was found that compared with other common credit scoring models, the proposed model was able to effectively reduce type II error rates and total classification error costs, and improve the AUC, the F1 values (reconciliation average of Recall and Precision), and the G-means. The proposed model was compared with other multi-objective optimization algorithms to further demonstrate that MOPSO is the best approach for cost-sensitive logistic regression credit scoring models.

Machine learning models for credit analysis improvements: Predicting low-income families’ default

The main objective of this study is to investigate the behaviour of default prediction models based on credit scoring methods and computational techniques with machine learning algorithms. The predictive capabilities of the models were compared to identify default-prediction mechanisms in the “My Home, My Life” Program (Programa “Minha Casa, Minha Vida” — PMCMV). The PMCMV is one of the largest government initiatives in the world to finance home ownership in the low-income population. Implemented by the Brazilian government, the programme has provided financing in excess of USD 84 billion and by 2016 had already contracted for the construction of over 4.5 million housing units, with 3.3 million units already delivered. The models developed in this study involve different time intervals for default prediction as well as analysis without the use of traditional discriminatory variables (gender, age, and marital status). Three measurements were used to evaluate the quality of the prediction models: area under the ROC curve, the Kolmogorov–Smirnov index, and the Brier score. The results indicated that (1) the accuracy of the models improves as the number of days overdue used to define the default variable increases; (2) the best prediction results were obtained with traditional ensemble techniques — in this case Bagging (BG), Random Forest (RF), and Boosting; and (3) there was a negative impact on all criteria when a smaller number of observations was used, especially on the type II error. It was also found that the discriminatory power of the credit risk rating system is preserved when removing discriminatory variables from the models. Applying the BG algorithm, which is the best prediction method, a default rate of 11.80% could be reduced to 2.95%, which leads to a selection that would result in 197,905 fewer delinquent contracts in the PMCMV, thus representing a savings of approximately USD 3.0 billion in credit losses.

A Novel Classification Approach for Credit Scoring based on Gaussian Mixture Models

Credit scoring is a rapidly expanding analytical technique used by banks and other financial institutions. Academic studies on credit scoring provide a range of classification techniques used to differentiate between good and bad borrowers. The main contribution of this paper is to introduce a new method for credit scoring based on Gaussian Mixture Models. Our algorithm classifies consumers into groups which are labeled as positive or negative. Labels are estimated according to the probability associated with each class. We apply our model with real world databases from Australia, Japan, and Germany. Numerical results show that not only our model's performance is comparable to others, but also its flexibility avoids over-fitting even in the absence of standard cross validation techniques. The framework developed by this paper can provide a computationally efficient and powerful tool for assessment of consumer default risk in related financial institutions.

Dynamic weighted ensemble classification for credit scoring using Markov Chain

As the ensemble methods achieve significantly better performances than individual models do, they have been widely applied to credit scoring. However, most of them employ a static combiner to combine base classifiers, which do not consider the base classifiers’ characters and their dynamic classification ability. Though some dynamic ensemble methods are proposed, they need to produce a large number of base classifiers or employ a fixed combiner, which limit the generality of the ensemble methods. In this paper, we propose a new dynamic weighted ensemble method for credit scoring. Markov Chain is employed to model the change of each classifier’s classification ability and build a dynamic weighted trainable combiner, which dynamically assign weights to the base classifiers for each sample in the testing set. Through eight credit data sets from the real world, the experimental study demonstrates the ability and efficiency of the dynamic weighted ensemble method to improve prediction performance against the benchmark models, including some well-known individual classifiers and dynamic ensemble methods. Moreover, the proposed method can effectively decrease the misclassification cost, which can reduce risks for the financial institutions.

Assuring explainability on demand response targeting via credit scoring

As data-driven innovation becomes a main trend in the energy sector, explainability of data-driven actions is becoming a major fairness issue for the residential applications, and it is expected to become a requirement for regulatory compliance. Explainability, however, often demands a sacrifice in prediction performance and affects the effectiveness of data-driven actions. In this study, we consider data-driven customer targeting in an incentive-based residential demand response program, and investigate the explainability-performance tradeoff when using simple-rule based, machine learning, and credit scoring methods. Credit scoring, that has been a popular solution in the finance discipline for over 60 years, is a scorecard based modeling method that can surely provide explainability. We first provide the detailed steps of applying credit scoring to the demand response problem. Then, we use a dataset of 14,525 households obtained from a real demand response program and analyze two prediction problems – participation prediction and behavior change prediction. The results show that credit scoring can achieve a comparable performance as the best-performing machine learning methods while providing full explainability. Our results suggest that credit scoring can be a promising explainability option for broader energy sector problems.

RFMS Method for Credit Scoring Based on Bank Card Transaction Data

Microcredit refers to small loans to borrowers who typically lack collateral, steady employment, or a verifiable credit history. It is designed not only for start-ups but also for individuals. The microcredit industry is experiencing fast growth in China. In contrast with traditional loans, microcredit typically lacks collateral, which makes credit scoring important. Due to the fast development of on-line microcredit platforms, there are various sources of data that could be used for credit evaluation. Among them, bank card transaction records play an important role. How to conduct credit scoring based on this type of data becomes a problem of importance. The key issue to be solved is feature construction: how to construct meaningful and useful features based on bank card transaction data. To this end, we propose here a so-called RFMS method. Here “R” stands for recency, “F” stands for frequency, and “M” stands for monetary value. Our method can be viewed as a natural extension of the classical RFM model in marketing research. However, we make a further extension by taking “S” (Standard Deviation) into consideration. The performance of the method is empirically tested on a data example from a Chinese microcredit company.

Dynamic ensemble classification for credit scoring using soft probability

In recent years, classification ensembles or multiple classifier systems have been widely applied to credit scoring, and they achieve significantly better performance than individual classifiers do. Selective ensembles, an important part of this group of systems, are a promising field of research. However, none of them considers the relative costs of Type I error and Type II error for credit scoring when selecting classifiers, which bring higher risks for the financial institutions. Moreover, earlier dynamic selective ensembles usually select and combine classifiers for each test sample dynamically based on classifiers’ performance in the validation set, regardless of their behaviors in the testing set. To fill the gap and overcome the limitations, we propose a new dynamic ensemble classification method for credit scoring based on soft probability. In this method, the classifiers are first selected based on their classification ability and the relative costs of Type I error and Type II error in the validation set. With the selected classifiers, we combine different classifiers for the samples in the testing set based on their classification results to get an interval probability of default by using soft probability. The proposed method is compared with some well-known individual classifiers and ensemble classification methods, including five selective ensembles, for credit scoring by using ten real-world data sets and seven performance indicators. Through these analyses and statistical tests, the experimental results demonstrate the ability and efficiency of the proposed method to improve prediction performance against the benchmark models.

A novel ensemble method for credit scoring: Adaption of different imbalance ratios

In the past few decades, credit scoring has become an increasing concern for financial institutions and is currently a popular topic of research. This study aims to generate a novel ensemble model for credit scoring, to obtain superior performance and high robustness, adapting to different imbalance ratio datasets. First, according to the credit scoring data characteristics, the proposed model extends the BalanceCascade approach to generate adjustable balanced subsets based on the imbalance ratios of training data. Further, it reduces the negative effect of imbalanced data and improves the comprehensive performance of the predictive model. Second, the proposed model adopts two kinds of tree-based classifiers, random forest and extreme gradient boosting, as the base classifiers for a three-stage ensemble model. This includes the use of stacking to generate predicted results of the former layer as new explanatory features in the latter layer, and the use of a particle swarm optimization algorithm for parameters optimization of the base classifiers. Finally, the results indicate that the average performance of the proposed model is superior to other comparative algorithms as reflected in most evaluation measures for different datasets. It demonstrates that the proposed model is robust and represents a positive development in credit scoring.

Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment

Many households in developing countries lack formal financial histories, making it difficult for banks to allocate capital, and for potential borrowers to obtain loans. However, many unbanked households have mobile phones, and even prepaid phones generate rich data about their behavior. This project shows that behavioral signatures in mobile phone data predict default with accuracy approaching that of credit scoring methods that rely on financial histories. The method is demonstrated using call records matched to loan outcomes for a sample of borrowers in a Caribbean country.