The Role of Artificial Intelligence, Financial and Non-Financial Data in Credit Risk Prediction: Literature Review

Small and medium-sized enterprises (SMEs) are of major importance in world economies and job creation. Financing is one of the key issues for SME development since SMEs are often considered riskier than large companies. It is argued in the literature that artificial intelligence (AI) and non-financial data could increase the financial inclusion of disadvantaged groups, such as SMEs. This article presents an overview of selected studies on credit risk prediction from the 1960s to 2022, covering topics of research work applying classical statistical methods, studies using AI methods on traditional financial data and studies applying AI methods on non-financial data. Literature overview results showed that the inclusion of non-financial data in credit risk prediction models could increase credit risk prediction performance, while AI methods can enable the inclusion of non-financial data. Since non-financial data potentially could be used as alternative data in credit prediction models, AI and non-financial data could help to increase access to finance for SME

Identifying Optimal Parameters And Their Impact For Predicting Credit Card Defaulters Using Machine-Learning Algorithms

Data mining and Machine learning are the emerging technologies that are rapidly spreading in every field of life due to their beneficial aspects. The financial sector also makes use of these technologies. Many research studies regarding banking data analysis have been performed using machine learning techniques. These research studies also have many Problems as the main focus of these studies was to achieve high accuracy and some of them only perform comparative analysis of different classifier's performance. Another major drawback of these studies was that they do not identify any optimal parameters and their impact. In this research, we have identified optimal parameters. These parameters are valuable for performing the credit scoring process and might also be used to predict credit card defaulters. We also find their impact on the results. We have used feature selection and classification techniques to identify optimal parameters and their impact on credit card defaulters identification. We have introduced three classifiers which are Kstar, SMO and Multilayer perceptron and repeat the process of classification and feature selection for every classifier. First, we apply feature selection techniques to our dataset with each classifier to find out possible optimal parameters and In the next phase, we use classification to find the impact of possible optimal parameters and proved our findings. In each round of classification, we have used different parameters available in the dataset every time we include and exclude some parameters and noted the results of each run of classification with each classifier and in this way, we identify the optimal parameters and their impact on the results Whereas we also analyze the performance of classifiers. To perform this research study, we use the “credit card defaults” dataset which we obtained from UCI Machine learning online repository. We use two feature selection techniques that include ranker approach and evolutionary search method and after that, we also apply classification techniques on the dataset. This research can help to reduce the complexities of the credit scoring process. Through this study, we identify up to six optimal parameters and also find their impact on the performance of classifiers. Further We also identify that multilayer perceptron was the best performing classifier out of three. This research work can also be extended to other fields in the future where we use this mechanism to find out optimal parameters and their impact can help us to predict the  results.  &nbsp

A data mining application in credit scoring processes of small and medium enterprises commercial corporate customers

The constant need to assess loans makes risk evaluation a very important problem for the banking sector. A crucial function of the banks is to fund households and companies from various industries in the economy. Risk is taken by the banks as soon as a loan is given to an entity. Currently, there are sector-and-experience based methods of analysis employed by the banks to estimate the risks to be taken. For the credit process, there exist a large number of studies in the literature on scoring individual clients but there are very few studies on scoring small and medium enterprises (SME) commercial corporate customers. In this study, we propose an objective risk measurement method for the lending process of SME commercial corporate customers and performed classification task of data mining by collecting current customer data on credit evaluation process of a bank. For this purpose, we first create a risk measure by looking into the risks identified for existing customers by the analysts of a bank. These scores are used as target variable in the classification process. Then, we extract rules for estimating these scores using Weka software. We used six different algorithms, and compared results in terms of test accuracy, the number of rules, recall, precision and Kappa statistic. We obtained high accuracy rates on real life data by our approach. As a result, we showed that an objective evaluation strategy is possible to use in the lending process for SME commercial corporate customers in the banking system using data mining. This article is categorized under: Application Areas > Business and Industry Technologies > Classification Application Areas > Industry Specific Application

Previsão de anulação de projetos financiados por fundos públicos

A atribuição de fundos públicos é de extrema importância para o aumento de competitividade das empresas. A justiça na sua aplicação e a garantia da sua boa e completa utilização é uma preocupação constante das agências competentes, IAPMEI e AICEP. A análise dos dados das candidaturas por sistemas automáticos, pode ajudar a focar as fiscalizações em projetos de maior risco. O problema que será minimizado com este estudo será a previsão de projetos anulados no momento da candidatura. Os projetos anulados são aqueles que durante a sua execução são cancelados, podendo dar lugar a devoluções de valores monetário. Todos os projetos cativam o valor elegível, desde que são aceites até ao fim ou cancelamento da sua execução. Esta dissertação apresenta um estudo usando os dados das candidaturas de projetos do IAPMEI. O objetivo foi criar modelos de previsão de anulação de projetos e identificação das principais características envolvidas nesta tarefa. Para além disto, está englobada toda a tarefa de extração e transformação de dados relativos a todos os ficheiros de um ciclo de vida de um projeto. Por fim, os modelos de previsão das anulações que resultaram deste estudo foram integrados num protótipo que visa automatizar a tarefa de classificação dos projetos no momento da candidatura. Através desta dissertação, as instituições que gerem os fundos públicos serão capazes de gerir melhor os fundos disponíveis de forma a otimizar a sua aplicação e criar mais oportunidades e maior eficiência para as empresas que usufruem dos mesmos.The allocation of public funds is extremely important to increase the competitiveness of companies. Fairness in its application and the guarantee of its good and complete use is a constant concern of the competent agencies, IAPMEI and AICEP. The analysis of application data by automated systems can help focus inspections on higher risk projects. The problem that will be minimized with this study will be the forecast of canceled projects at the time of application. Canceled projects are those that are canceled during their execution, which may give rise to refunds of monetary values. All projects captivate the eligible value, as long as they are accepted until the end or cancellation of their execution. This dissertation presents a study using data from IAPMEI project applications. The objective was to create models to forecast cancellation of projects and identify the main characteristics involved in this task. In addition, the entire task of extracting and transforming data relating to all files in a project's lifecycle is included. Finally, the cancellation prediction models that resulted from this study were integrated into a prototype that aims to automate the task of classifying projects at the time of application. Through this dissertation, the institutions that manage public funds will be able to better manage the available funds in order to optimize their application and create more opportunities and greater efficiency for the companies that use them