Credit Assessment Research Articles

Towards AI Dashboards in Financial Services: Design and Implementation of an AI Development Dashboard for Credit Assessment

Towards AI Dashboards in Financial Services: Design and Implementation of an AI Development Dashboard for Credit Assessment

Review of Literature on Determinants of Loan Repayment Performance in Microfinance Institutions

This article presents a comprehensive literature review on the determinants of loan repayment performance in microfinance institutions (MFIs). The review aims to enhance the understanding of borrower behaviour regarding loan repayment, focusing on socio-economic, institutional, and motivational factors. It synthesises findings from various studies, including the Indian Institute for Mother and Child (IIMC), a nonprofit Indian MFI, to identify critical determinants influencing borrowers' ability to repay loans. Factors such as income stability, education level, financial literacy, and family size are highlighted as significant socio-economic determinants. Loan-specific factors, including loan size, repayment period, and supervision, are also critical. Institutional practices like credit assessment techniques, loan structuring, and technological adoption are examined alongside social factors such as peer pressure, social cohesiveness, and community support systems. Additionally, the review explores borrowers' motivation for taking out additional loans, finding that the drive to reach the maximum loan level positively impacts repayment performance. Empirical evidence suggests that repayment rates increase as borrowers approach their loan limits. The article concludes with recommendations for MFIs to improve their operational strategies by addressing these determinants, thereby enhancing loan repayment rates and contributing to the sustainability and effectiveness of microfinance programs.

Utilizing the LightGBM algorithm for operator user credit assessment research

Mobile Internet user credit assessment is an important way for communication operators to establish decisions and formulate measures, and it is also a guarantee for operators to obtain expected benefits. However, credit evaluation methods have long been monopolized by financial industries such as banks and credit. As supporters and providers of platform network technology and network resources, communication operators are also builders and maintainers of communication networks. Internet data improves the users credit evaluation strategy. This paper uses the massive data provided by communication operators to carry out research on the operators user credit evaluation model based on the fusion LightGBM algorithm. First, for the massive data related to user evaluation provided by operators, key features are extracted by data preprocessing and feature engineering methods, and a multi-dimensional feature set with statistical significance is constructed; then, linear regression, decision tree, LightGBM, etc. The machine learning algorithm builds multiple basic models to find the best basic model; finally, integrates Averaging, Voting, Blending, Stacking and other integrated algorithms to refine multiple fusion models, and finally establish the most suitable fusion model for operator user evaluation.

Risk assessment when granting credit to non-financial legal entities

Purpose: This study aimed to verify risk assessment procedures when granting credit by a legal entity via financing in negotiations with its customers. Methodology: A documentary approach was used, emphasizing qualitative analysis, during the experimental study conducted at an XYZ organization linked to information technology. Results: They point out that the credit score and rating score, established through the evaluation stages, taking into account the company's history, documentation, guarantees, financial health, cash flow, indebtedness, corporate governance, and market prospects, enable the risk classification of their analyzed clients, ranging from AAA for the "alpha" client to D for the "beta" client. This result made it possible to deduce that credit assessment is appropriate automation in financing decisions for commercial activities, capable of promoting financial flexibility and commercial agility. Customer risk classification is therefore seen as a fundamental tool for improving XYZ's financial decisions, minimizing default risks, and guaranteeing the financial security of its commercial operations, resulting from establishing risk scores for both defaulting and non-defaulting companies. Contributions of the Study: This study makes a relevant theoretical contribution to the field of research by highlighting the importance of validating the criteria used in granting financing, as well as the contextual factors that can affect decisions. The practical contribution of analyzing a non-financial institution under the pandemic context and how they minimize their default by establishing credit analysis mechanisms provides improvements and consequently greater security as lenders.

A framework for generating explanations of machine learning models in Fintech industry

Artificial Intelligence is making significant inroads into various aspects of business and life bringing the transformation in many ways. The convolution of technology in finance is often called FINTECH rapidly growing area of transformation. In the FINTECH industry, AI can automate several financial processes and services such as fraud detection, customer services, credit assessment, price predictions, customer churning, trading services, risk management, underwriting, market forecasting. These processes and services are critical to financial sectors such as banking, insurance, currency, stock and commodity markets, wealth management, payment clearing houses, payment regulators etc. Regulations control these processes and should be transparent in their operations. AI models are inherently opaque in their outcomes and unable to be fully plugged into the financial processes and services. Explainable AI is the key area of research that can help to provide transparency to enable these AI models as fully operational business models to automate financial products and services. In this paper we will broadly outline the framework of explainable artificial intelligence (XAI) in finance sectors and services. We then look into one use case of credit assessment and develop an XAI framework to provide transparent outcomes from the AI models.

Civil Liability of Intermediaries in False Statements of Securities

Securities intermediaries such as sponsors, accounting firms, law firms and asset credit assessment agencies are important components of the capital market. In recent years, securities misrepresentation cases have occurred frequently, and securities intermediaries have been more or less identified as having certain faults and bearing corresponding civil liabilities. The judgment of some of these cases has caused a lot of disputes and discussions, which exposes the inadequacy of the law on the civil liability of securities intermediary agencies for false statements, which brings a lot of trouble to the securities market and judicial practice. This paper takes the civil liability system of securities intermediaries for false statements as the research object, adopts normative analysis method, case study method and comparative study method to sort out the legal system and judicial status quo of securities intermediaries for civil liability of false statements, finds and demonstrates the existing difficulties, and tries to put forward corresponding improvement paths.

Advanced risk management solutions for mitigating credit risk in financial operations

Advanced risk management solutions are essential for mitigating credit risk in financial operations, particularly in today's volatile economic environment. This Review explores the innovative approaches and technologies being utilized to enhance credit risk management and safeguard financial institutions against potential losses. Credit risk, the possibility that a borrower will default on their obligations, poses a significant threat to financial stability. Traditional methods of assessing and managing credit risk, such as credit scoring and historical data analysis, are no longer sufficient to address the complexities of modern financial markets. Advanced risk management solutions offer more robust and dynamic tools for identifying, assessing, and mitigating credit risk. One of the key advancements in this field is the integration of big data and machine learning algorithms. By analyzing vast amounts of data from diverse sources, financial institutions can gain deeper insights into borrower behavior, market trends, and economic indicators. Machine learning models can predict default probabilities with greater accuracy, allowing for proactive risk mitigation strategies. Additionally, real-time risk monitoring systems have become increasingly prevalent. These systems provide continuous oversight of credit portfolios, enabling financial institutions to detect early warning signs of potential defaults and take timely action. Advanced analytics and visualization tools facilitate the identification of emerging risks and the implementation of targeted interventions. Another crucial component of advanced risk management solutions is the use of stress testing and scenario analysis. These techniques simulate various economic conditions and assess the impact on credit portfolios, helping financial institutions prepare for adverse scenarios and develop contingency plans. Furthermore, blockchain technology is emerging as a promising tool for enhancing transparency and reducing fraud in credit transactions. By providing a secure and immutable record of credit histories and transactions, blockchain can improve trust and reliability in credit assessments. In conclusion, advanced risk management solutions are transforming the way financial institutions mitigate credit risk. By leveraging big data, machine learning, real-time monitoring, stress testing, and blockchain technology, financial institutions can achieve more accurate risk assessments and implement more effective risk mitigation strategies. These innovations not only enhance the stability and resilience of financial operations but also contribute to a more secure and trustworthy financial system.

Application and Impact Analysis of Intelligent Financial Systems in Small and Medium-Sized Manufacturing Industries

This paper explores how smart financial systems can empower small and medium-sized manufacturing industries by integrating artificial intelligence, big data analytics and cloud computing technologies to provide customised financial service solutions for the sector. The study outlines the core challenges faced by small and medium-sized manufacturing industries, such as financing difficulties, lagging risk management and operational inefficiencies. The core components of the smart financial system are detailed, including functional modules such as automated credit assessment, supply chain finance optimisation, cost prediction models and personalised insurance product recommendations. This paper proposes strategies to address data security, technology acceptance, and regulatory compliance issues that may be encountered during the implementation process, and emphasises the importance of building a good ecosystem between the government, financial institutions, and technology enterprises to comprehensively support the intelligent transformation and sustainable development of the small and medium-sized manufacturing industry.

Credit Scoring System Using Machine Learning

This research paper offers a comprehensive examination of credit scoring systems. We will explore the current methodologies employed, the challenges encountered, and potential avenues for improvement. Credit scoring plays a critical role in financial decision-making, impacting both individual access to credit and lender risk management strategies. The paper will analyze traditional credit scoring models alongside emerging trends designed to enhance the accuracy and fairness of credit assessments. Additionally, we will discuss the ethical considerations surrounding credit scoring and propose recommendations for future advancements.

Innovation in Corporate Lending: A Case Study of Shriram Finance Corporation Private Limited

This case study explores the innovative practices in corporate lending adopted by Shriram Finance Corporation Private Limited, a leading financial services provider in India. The study examines the company’s approach to addressing the unique financial needs of small and medium enterprises (SMEs) and large corporate clients. Through a detailed analysis of Shriram Finance’s product offerings, risk management strategies, and use of technology, the research highlights how the company has differentiated itself in a competitive market. Key innovations include customized loan products, advanced credit assessment models, and the integration of digital platforms to streamline lending processes. The findings underscore the impact of these innovations on improving financial accessibility, reducing operational costs, and enhancing customer satisfaction. This case study contributes to a deeper understanding of the evolving landscape of corporate lending and provides insights into effective strategies for financial institutions aiming to foster innovation and growth in this sector.

Application of AdaBound-Optimized XGBoost-LSTM Model for Consumer Credit Assessment in Banking Industries

Consumer credit assessment has always been a crucial concern in the financial industry. It involves evaluating an individual's credit history and their ability to repay loans, playing a pivotal role in the risk management and lending decisions made by credit institutions. In the present landscape, traditional credit assessment methods confront various shortcomings. Firstly, they typically only consider static features and are unable to capture the dynamic changes in an individual's credit profile over time. Secondly, traditional methods struggle with processing complex time series data, failing to fully exploit the importance of time-related information. To address these challenges, we propose an innovative solution – the XGBoost-LSTM model optimized with the AdaBound algorithm. This hybrid model combines two powerful machine learning techniques, XGBoost and LSTM, to leverage both static and dynamic features effectively.

Decoding Creditworthiness: Leveraging Big Data Analytics for Risk Assessment in Credit Appraisals

The aim of the present study is to explore the role of Big Data Analytics (BDA) in assessing the risk in credit appraisals also, to discuss credit assessment and its importance. The study also explores the Credit Risk Analytics (CRA) the threats and restrictions of BDA in credit appraisal as well as opportunities for data in banks.

A 2-order additive fuzzy measure identification method based on hesitant fuzzy linguistic interaction degree and its application in credit assessment

To reflect both fuzziness and hesitation in the evaluation of interactivity between attributes in the identification process of 2-order additive fuzzy measure, this work uses the hesitant fuzzy linguistic term set (HFLTS) to describe and depict the interactivity between attributes. Firstly, the interactivity between attributes is defined by the supermodular game theory. According to this definition, a linguistic term set is established to characterize the interactivity between attributes. Under the linguistic term set, the experts employ linguistic expressions generated by context-free grammar to qualitatively describe the interactivity between attributes. Secondly, through the conversion function, the linguistic expressions are transformed into the hesitant fuzzy linguistic term sets (HFLTSs). The individual evaluation results of all experts were further aggregated with the defined hesitant fuzzy linguistic weighted power average operator (HFLWPA). Thirdly, based on the standard Euclidean distance formula of the hesitant fuzzy linguistic elements (HFLEs), the hesitant fuzzy linguistic interaction degree (HFLID) between attributes is defined and calculated by constructing a piecewise function. As a result, a 2-order additive fuzzy measure identification method based on HFLID is proposed. Based on the proposed method, using the Choquet fuzzy integral as nonlinear integration operator, a multi-attribute decision making (MADM) process is then presented. Taking the credit assessment of the big data listed companies in China as an application example, the analysis results of application example prove the feasibility and effectiveness of the proposed method. This work successfully reflects both the fuzziness and hesitation in evaluating the interactivity between attributes in the identification process of 2-order additive fuzzy measure, enriches the theoretical framework of 2-order additive fuzzy measure, and expands the applicability and methodology of 2-order additive fuzzy measure in multi-attribute decision making.

Decoding CIBIL: Understanding the Structure and Strategies for Improvement

This research paper delves into the intricate dynamics of credit scoring systems, particularly focusing on understanding the structure of CIBIL scores and delineating strategies for improving creditworthiness. Drawing upon theoretical frameworks from financial economics and regulatory economics, the study elucidates key factors influencing credit scores, including payment history, credit utilization, credit type, and duration. Through a mixed-methods approach encompassing secondary data analysis from the official websites of TransUnion CIBIL India and Bajaj Finance Limited, as well as qualitative exploration through thematic analysis, the research uncovers actionable insights for individuals seeking to enhance their credit profiles.By bridging theoretical insights with empirical findings, this study contributes to the literature on credit management and equips individuals with knowledge essential for navigating the complexities of credit assessment and improving financial resilience

A multi-stage integrated model based on deep neural network for credit risk assessment with unbalanced data

PurposeWith the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.Design/methodology/approachIn this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.FindingsOn two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.Originality/valueIn this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

Financial Management Practices and Financial Performance of Table Banking Self-Help Groups in Kenya

Effective financial management is critical to the sustainability and growth of organizations, including support groups such as table banking groups. Table banking involves pooling members' financial resources to provide loans at reasonable interest rates to improve their socioeconomic status. This study examines the impact of financial management practices on the performance of self-help groups using the table banking model in Kenya. Theoretical frameworks, including monetary theory and financial theory, guide the investigation alongside empirical findings from previous studies. The study examines the importance of record keeping, debt management, credit assessment and cash flow management in improving the financial performance of table banking self-help groups. The results highlight the importance of these practices in maximizing savings, facilitating credit, and ultimately promoting economic empowerment within communities. The study concludes that financial management practices play a crucial role in shaping the performance of self-help groups engaged in table banking. By adopting effective strategies such as record keeping, debt management, credit assessment, and cash flow management, these groups can improve their financial stability, promote prudent lending, and ultimately contribute to poverty alleviation and economic development.

Transductive Semi-Supervised Metric Network for Reject Inference in Credit Scoring

Credit scoring is an essential technique for credit risk management in the financial industry. However, most credit scoring models face the challenge of reject inference, which refers to the lack of post-loan performance data for rejected applicants, leading to sample selection bias and inaccurate credit assessment. Traditional credit scoring methods tackle this issue by assuming that the missing labels for rejected samples are missing at random (MAR) and by measuring sample similarity directly in the original feature space. Nevertheless, these strategies are not suitable for real-world business scenarios. Inspired by metric learning and transductive learning, we propose a novel credit scoring model called transductive semi-supervised metric network (TSSMN), which formalizes reject inference as a semi-supervised binary classification problem with the prior assumption of missing not at random (MNAR). TSSMN consists of two interconnected modules: the embedding metric network (EMN) that maps samples from the original feature space to the metric space for similarity measurement, and the transductive propagation network (TPN) that performs label propagation based on sample similarity. We evaluate TSSMN on a real-world credit dataset and compare it with traditional credit scoring methods. The results indicate that TSSMN can overcome sample selection bias and more accurately classify credit applicants. Therefore, TSSMN has the potential to enhance credit risk assessment in real-world business scenarios.

Peran Analisis Laporan Keuangan Dalam Proses Evaluasi Permohonan Kredit: Tinjauan Kasus Di Pt. Bank Rakyat Indonesia (PERSERO) TBK.

The most important aspect above when evaluating the performance of a company is its satisfactory financial position. For this reason, companies must manage their financial management carefully and manage it well. Financial ratio analysis is used to analyze financial performance. When analyzing company performance from a financial perspective, ratio analysis is a tool for analyzing financial data contained in financial reports. The analytical method used in this research is a quantitative method which aims to obtain information about various field conditions and interpret them based on interrelated theory and literature. The analytical tools used to analyze customer financial reports include liquidity ratios, solvency ratios and profitability ratios. Based on research results obtained from PT. Bank Rakyat Indonesia (Persero) Tbk., the author concludes that there is a very effective financial report analysis to find out whether banks carry out credit assessments of prospective debtors by using ratio analysis to manage liquidity, profitability and profitability. The financial ratios calculated include the current ratio, quick ratio, debt to equity ratio, debt to equity ratio, profit margin, investment returns and return on equity. Improve and improve the quality of creditor financial report analysis in evaluating credit applications, as a basis for considering and completing further financial ratio analysis.

Designing an Intelligent Scoring System for Crediting Manufacturers and Importers of Goods in Industry 4.0

Background: The modern credit card system is critical, but it has not been fully examined to meet the unique financial needs of a constantly changing number of manufacturers and importers. Methods: An intelligent credit card system integrates the features of artificial intelligence and blockchain technology. The decentralized and unchangeable ledger of the Blockchain technology significantly reduces the risk of fraud while maintaining real-time transaction recording. On the other hand, the capabilities of AI-driven credit assessment algorithms enable more precise, effective, and customized credit choices that are specifically tailored to meet the unique financial profiles of manufacturers and importers. Results: Several metrics, including predictive credit risk, fraud detection, credit assessment accuracy, default rate comparison, loan approval rate comparison, and other important metrics affecting the credit card system, have been investigated to determine the effectiveness of modern credit card systems when using Blockchain technology and AI. Conclusion: The study of developing an intelligent scoring system for crediting manufacturers and importers of goods in Industry 4.0 can be enhanced by incorporating user adoption. The changing legislation and increasing security threats necessitate ongoing monitoring. Scalability difficulties can be handled by detailed planning that focuses on integration, data migration, and change management. The research may potentially increase operational efficiency in the manufacturing and importing industries.

Analisa Laporan Keuangan Nasabah dan Peranannya dalam Pemberian Kredit pada PT Bank Rakyat Indonesia (Persero) Tbk Unit Lubuk Begalung Padang

Financial reports have a dual role as a guide for customers in preparing financial reports and as a source of inspiration for BRI's credit department. Analysis of customer financial reports, supported by financial reports, becomes an integral part of the credit process. Research involves credit monitoring procedures and analysis of customer financial reports. Case studies on Toko Rhio Yudha Gama and Toko Dikcy Oksani Junaidi show positive results in liquidity, solvency and profitability, validating their suitability for obtaining credit with low risk. Analysis of financial ratios, such as current ratio, quick ratio, debt to total asset ratio, and return on equity, provide a satisfactory performance picture. The research conclusion confirms that credit assessment based on analysis of financial statements and profit and loss has great significance. Financial ratios are the main indicator, reflecting whether a customer is worthy of receiving a loan. In addition, BRI Bank determines the loan nominal using Repayment Capacity, which is the customer's repayment capacity of 50% of the net profit in the profit and loss statement.