Client Credit Research Articles

Role of Project Risk Assessment on Performance of Credit Products at Bank of Africa, Rwanda

The study examined the role of project risk assessment on performance of credit products at the Bank of Africa in Rwanda. The study was guided by the following specific objectives: To determine the contribution of technical feasibility on performance of credit product at Bank of Africa, to establish the contribution of project financial viability on performance of credit product at Bank of Africa and to evaluate the contribution of client’s credit rating on performance of credit product at Bank of Africa. This study applied descriptive design to get results related to the study, the target population as well sample size was 72 respondents. Researcher utilized census as sampling technique. The source of data was primary and secondary methods. Questionnaires were adopted to collect primary data and documentary review applied for collecting secondary data. The data was obtained using descriptive statistical analysis with use of frequency, percentage, mean and standard deviation, and inferential statistics by the use of Pearson correlation (r) and multiple linear regression analysis through Social Packages and Statistical Science version 23. The presentation of results was presented using tables, the pilot test was determined to ensure the validity and reliability of instrument used in data collection. The results were presented using Pearson Coefficient Correlation to indicate the relationship between variables, the findings showed that technical feasibility correlated with credit product at Bank of Africa at r=0.76; Furthermore, project financial viability influences performance of credits products at correlation coefficient of .738 while the findings showed that there is positive correlation between client’s credit rating and performance of credit product at Bank of Africa at 72.1%. The study concludes that project risk assessment contributes to the performance of credit products through technical feasibility, project financial viability and client’s credit rating. The study recommends that project implementors need to improve the client credit rating to enhance performance of credit products. Keywords: Project Risk Assessment, Performance, Credit Products, Rwanda

Sistem Pendukung Keputusan Persetujuan Pengajuan Kredit Mobil di PT Clipan Finance Indonesia Dengan Metode Simple Additive Weighting (SAW)

Abstract
 PT Clipan Finance Indonesia is a car loan financing leasing company. This company has a standard in determining whether or not a customer is eligible for car loan financing. In choosing clients who deserve for car loan financing, it must be done properly to avoid client credit congestion, so it is necessary to select client data analysis. In selecting this client data, a decision support system information was designed that can help determine the priority of clients who are eligible for car loan financing, by using the Simple Additive Weighting (SAW) method as a method of supporting decision making. The SAW method is used to support decision making to evaluate the feasibility value of clients in selecting data for clients who apply for car loans based on the criteria and weight of decision making. Through this system, it is hoped that it can help determine the feasibility of applying for a client's car loan more effectively and efficiently by making the ranking value obtained as a reference for determining client creditworthiness approval.
 Keywords: Car Loan Financing, Decision Support System, Simple Additive Weighting

ANALYSIS OF RISK MITIGATION EFFORTS FOR ISLAMIC EQUITY AND DEBT FINANCING IN ISLAMIC MICROFINANCE INSTITUTIONS

This paper investigates risk mitigation practices and alternative measures for non-performing financing in sharia rural banks (BPRS) in Indonesia. Using in-depth interviews with Islamic microfinance practitioners who are expert in the area, this study explores two categories of financing which are equity financing (mudharabah and musyarakah) and debt financing (murabahah). The mechanism will advise on the themes of equity financing, debt financing, risk identification and mitigation, risk appraisal, risk control and monitoring, and digital systems. Interviews were conducted at 6 BPRS in West Java and Banten, Indonesia. The data will inform researchers about success stories of equity and debt financing by implementing risk mitigation to reduce the magnitude of the risk of each financing and alternative steps to mitigate non-performing financing. The results show that BPRS prefer musyarakah financing for equity over mudharabah, because they consider mudharabah to be riskier. For debt financing, all BPRS apply murabahah financing. Risk mitigation is carried out properly, starting from analyzing the client's credit history to assessing the required collateral. In the event of non-performing financing, the BPRS first assesses the source of the problem and the BPRS provides easy payments to clients through relaxation and restructuring. The digital system also plays an important role in mitigating BPRS risk and facilitating client access to BPRS.

Loan Delinquency in Microfinance Institutions (MFIs): Ways to Overcome the Problem

Microfinance institution (MFI) deliquency management is a new topic in the world of microfinance. The major goal of the article is to examine at delinquency management strategies in MFIs in Nepal, as well as the causes of loan delinquency and how to improve loan delinquency. The study’s goal necessitated an evaluation of relevant literature. In Nepal, the ratio of non-performing loans (NPLs) in the microfinance sector has been rising. The study found that adopting governance in loan delivery procedures, client identification, effective credit appraisal, and review of client credit history, professional knowledge of clients, client literacy, and identification of clients over indebtedness, credit monitoring and controls, and loan utilization can reduce MFI delinquency. The aim of this research is to help MFIs improve the quality of their loan portfolios. It shows that institutionally efficient MFIs may be able to meet both their social and financial goals. The conclusions of this article will aid MFIs in reducing NPL and better managing delinquency.

Credit Management Practices and Loan Performance of Microfinance Banks in Kenya

Loan performance of microfinance firms has declined recently. This pattern compromises microfinance organizations sustainability, viability and hinders them achieving their objectives. The purpose of this study was to see how credit management strategies affected loan performance at Kenyan microfinance institutions. The particular objectives of the study were to assess the impact of credit policy, customer evaluation, collection policy, credit conditions, and credit risk management on loan performance. Thirteen Kenyan microfinance banks are the subject of this initiative. Financial Intermediation theory, Information Asymmetry theory, and Transaction Cost theory will be used in this research. The study employed a descriptive research approach. In this study, both primary and secondary data will be employed. The basic data will be evaluated using the statistical metrics of mean and standard deviation. A direct model will be used to evaluate the impact of credit management strategies on loan performance (without moderation) and a moderating effect (with moderation). It was also utilized inferential statistics with linear regression models. Structured questionnaires were utilized in primary data collection. Financial reports for Microfinance banks and supervisory reports from the Central Bank of Kenya (CBK) will be used for secondary data collection. The study found that that their firms conduct client appraisal. The study also found that client appraisals were effective. The study found that the firm checks at the client’s credit worthiness before issuing a loan. The study found that the bank has credit analyst whose work is appraise all potential loan customers who seek to borrow from the bank. There are various services providers contracted by the bank e.g. for credit tracking to help with tracking of vehicles used as collateral, insurance firms, law firms who assist in security perfection in case where land has been used as collateral, CRB firms who assist in giving information on clients CRB performance, company search firms e.g. credit info who assist in ascertaining the authenticity of companies belonging to clients who seek to borrow a loan from the firm. The study found that that the loan default rate is below 20%. The study also found that loan performance can be improved by calling customers frequently to remind them off their loans obligation particularly the perennial defaulters. Credit policy, client evaluation, collection policy, credit conditions, and credit hazard control were all huge on advance exhibition of microfinance banks in Kenya, as per the review, at a 5% degree of importance and a 95% degree of certainty. Their organizations perform customer assessment, as indicated by the review's discoveries. Client evaluations were also shown to be beneficial in the study. Before granting a loan, the business examines the client's credit worthiness, according to the research. Following are some recommendations based on the study's results and conclusions: On the impact of microfinance banks' credit policies and decision-making on loan performance, it is recommended that effective credit risk management procedures be developed and executed, particularly through credit risk management information systems. Keywords: Credit Management, Loan Performance, Microfinance

The effect of working capital management and credit management policy on Jordanian banks’ financial performance

This study investigated the impact of Working Capital Management (WCM) and Credit Management Policy (CMP) on the Financial Performance (FP) of Jordanian banks (JB). The study data were obtained from 16 Jordanian banks listed on the Amman Stock Exchange (ASE) between 2017 and 2020. The study used panel data to investigate the relationship between the two independent variables, WCM and CMP, and the dependent variable FP; 64 financial reports to Jordanian banks were analyzed to measure this relationship. To test hypotheses, multiple regression was used. The study found a statistically significant relationship between WCM and FP, and the independent variable was able to explain 34.1% of the changes that occur in the dependent variable. In addition, the outcome approved that there is a statistically significant relationship between CMP and FP. Furthermore, CMP explained about 41.8% of changes in the dependent variable. The findings of this study indicate support for the banks’ performance; a bank may need to lengthen client credit terms, prolong the cash transfer cycle, and require a more extended payment period when judging on WCM. Acknowledgment The publication of this research has been supported by the Deanship of Scientific Research and Graduate Studies at Philadelphia University – Jordan.

Fraud in the banking system of Ukraine: ways to combat taking into account foreign experience

The article aims to identify effective ways to combat bank fraud in Ukraine. Using the method of systematic analysis of theoretical experience, practical measures and international legislation in the field of combating banking fraud, the main factors influencing the bank fraud level are identified: financial instability of the banking institution, lack of information about the client's credit history, poor internal control, low level of corporate governance, lack of a standard procedure for customers verification, lack of a unified register of bank employees, imperfect legal regulators and a banking control system. The study allowed to identify the characteristics of an effective banking fraud combating system: a clear definition of the legal framework, regulation subjects and objects, strict and guaranteed liability for violations of laws, cooperation of structural units and regulatory authorities at the domestic and international level, state control of financial monitoring. As a result of the study, it was concluded that the successful experience of developed countries, comprehensive implementation of measures to combat bank fraud (at the legislative, bank management and technological level) will not only reduce the level of bank fraud, but also corruption, money laundering and terrorist financing rate.

Client Engineering of XVA in Crisis and Normality: Restructuring, Mandatory Breaks and Resets

Crises challenge client XVA management because the prices of derivatives contain the XVA hedges that the provider requires, as well as the functional hedge the client requires. By functional hedge we mean the hedge linked to the client's business, e.g. FX, inflation, interest rate. By XVA hedge we mean the hedge linked to the client's credit risk and the provider's funding risk. The issue is that a derivative locks in the client credit level and the provider's funding level on the trade date, for the life of the trade. During a crisis both levels may be elevated. The standard methods for separating XVA hedges from the functional hedge is to use a Mandatory Break at inception, limiting the lock in, or to restructure post-trade assuming XVA can be rebated. Alternatively, in normal times resets can be used and are better because the client pays XVA on the continuation after the reset using the provider's view of client survival probability from original trade inception. We quantify these strategies from the client point of view to find where a Mandatory Break, or equivalently restructuring, is a better client strategy and where a reset is better. This quantification combines risk-neutral and risk-neutral conditional on physical measures. We provide numerical examples using Mandatory Breaks/restructuring and resets, informed by credit shocks and recovery from past crises. In normal times a reset can be twice as effective as a Mandatory Break or restructuring, whereas provided there is at least a 1/3 recovery from a CDS shock a Mandatory Break is better in our examples.

Online Learning Method for Drift and Imbalance Problem in Client Credit Assessment

Machine learning algorithms have been widely used in the field of client credit assessment. However, few of the algorithms have focused on and solved the problems of concept drift and class imbalance. Due to changes in the macroeconomic environment and markets, the relationship between client characteristics and credit assessment results may change over time, causing concept drift in client credit assessments. Moreover, client credit assessment data are naturally asymmetric and class imbalanced because of the screening of clients. Aiming at solving the joint research issue of concept drift and class imbalance in client credit assessments, in this paper, a novel sample-based online learning ensemble (SOLE) for client credit assessment is proposed. A novel multiple time scale ensemble classifier and a novel sample-based online class imbalance learning procedure are proposed to handle the potential concept drift and class imbalance in the client credit assessment data streams. The experiments are carried out on two real-world client credit assessment cases, which present a comprehensive comparison between the proposed SOLE and other state-of-the-art online learning algorithms. In addition, the base classifier preference and the computing resource consumption of all the comparative algorithms are tested. In general, SOLE achieves a better performance than other methods using fewer computing resources. In addition, the results of the credit scoring model and the Kolmogorov–Smirnov (KS) test also prove that SOLE has good practicality in actual client credit assessment applications.

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.

Fraud prediction for credit card using classification method

With the improvement of innovation like credit cards, debit cards, mobile banking, Internet managing an account is the mainstream medium to exchange the cash starting with one record then onto the next. Credit card is picking up fame day by day which expands the online exchange with the expansion in online shopping, online charge payment, insurance premium and different charges so the extortion cases identified with this are likewise expanding and it puts an extraordinary anxiety on the economy, affecting the two clients and budgetary bodies. It costs cash as well as an awesome measure of time to reestablish the damage done. In this paper, we look whether data mining procedures are valuable to estimate and categorize the client's credit risk score (normal/fraud) to beat the future dangers. The reason for existing is to keep the clients from online exchange by utilizing particular Data mining classification methods. The fakes are ascertained by Naïve Bayes method way to deal with break down the exchange is actual or fake. The exploratory outcome demonstrates that our model has great classification accuracy, recall and precision.

An Empirical Research on Bank Client Credit Assessments

Individual microcredit loans involve large quantities and small amounts and necessitate rapid approval, therefore making simple and fast application approvals rather critical. Creditors must evaluate clients’ credit status and default risk within the shortest time when determining whether to approve or decline their applications, preventing overdue responses that negatively impact bank profits and management practices, and could trigger domestic financial crises. This study investigates client credit quality criteria, focusing on the expert opinions of bank managers. The decision-making trial and evaluation laboratory method is adopted to enable a comparison and analysis of the similarities and differences in how banks evaluate their clients’ character, ability, financial capability, and collateral. Based on causality and correlations among the criteria, we also identify the core problems and key improvement criteria in the evaluation system. Through survey results of professional managers from Taiwanese banks, this study uses the DEMATEL method to compare the differences in bank evaluation methods based on the four dimensions of clients’ character, ability, pockets, and collateral, as well as the corresponding 14 criteria. In order to improve the reliability and usefulness in bank client credit risk assessment, the assessment dimensions and indicators of bank client credit risk assessment are first discussed; second, the causal relationship and degree of mutual influence between different dimensions and criteria are researched and assessed; in the end, the paper discusses how to improve the function and the benefits of bank client credit risk assessment.

Soft Data Modeling Via Type 2 Fuzzy Distributions for Corporate Credit Risk Assessment in Commercial Banking

The work reported in this paper aims to present possibility distribution model of soft data used for corporate client credit risk assessment in commercial banking by applying Type 2 fuzzy membership functions (distributions) for the purpose of developing a new expert decision-making fuzzy model for evaluating credit risk of corporate clients in a bank. The paper is an extension of previous research conducted on the same subject which was based on Type 1 fuzzy distributions. Our aim in this paper is to address inherent limitations of Type 1 fuzzy distributions so that broader range of banking data uncertainties can be handled and combined with the corresponding hard data, which all affect banking credit decision making process. Banking experts were interviewed about the types of soft variables used for credit risk assessment of corporate clients, as well as for providing the inputs for generating Type 2 fuzzy logic membership functions of these soft variables. Similar to our analysis with Type 1 fuzzy distributions, all identified soft variables can be grouped into a number of segments, which may depend on the specific bank case. In this paper we looked into the following segments: (i) stability, (ii) capability and (iii) readiness/willingness of the bank client to repay a loan. The results of this work represent a new approach for soft data modeling and usage with an aim of being incorporated into a new and superior soft-hard data fusion model for client credit risk assessment.

Encryption through Labeling using Efficient Domination

Numbers play a vital role in identifications and transactions, now a day's most of the field say client credit card numbers, social security numbers, tax file numbers etc need to kept secret when they send it electronically. It has been recognized that encryption and decryption mostly emerges from mathematical disciplines. In this paper, we present a new combinatorial technique to encrypt and decrypt the secret number through labeled graph using domination theory.

The Impact of Information Asymmetry and Client Credit on Lending Performance - Taiwan’s Evidence

Using panel data from Taiwan, this paper examines the impact of information asymmetry and client credit on lending performance. Banks without huge losses from bad debts and from credit card lending are categorized as high lending performance banks, whereas banks with heavy losses from bad debts and from credit card lending are categorized as low lending performance banks. Firms are also divided into micro and small businesses (MSBs) and medium and large businesses (MLBs). Logit regression is used to identify the determinants of lending performance, including the levels of information asymmetry and client credit records. The empirical results show that: (1) MLBs with good information transparency tend to establish relationships with banks that are characterized by huge losses from bad debts and from credit card lending. (2) Small foreign firms, as well as MLBs with high profitability, cash and R&D expenditure ratios prefer having relationships with banks with good lending performance and low credit risk. (3) MLBs and MSBs with poor credit records prefer having relationships with banks that have good lending performance and low credit risk.

Supply chain finance

The concept of supply chain finance is a response to global illiquidity, intensified through the global economic crisis and globalization of commercial and financial flows. The growing illiquidity undermines credit ratings of economic entities, thereby reducing the potential for achieving the projected goals (profitability and portfolio quality). In order to overcome this, banks have introduced certain products flexible to the requirements of specific transactions. The concerned products redirect the focus from a client's credit rating and risk to the credit rating and risk of a business partner (buyer), resulting in benefits for all transaction participants ('win-win-win'). Moreover, the activities are targeted at transaction analysis, i.e. the isolation and protection of the cash flow as the source of financial instrument's repayment. On the other hand, there has been an increasing number of transactions based on the risk of the commercial bank of the client's business partner, or on the risk of collateral (inventory). The focus is actually placed on the financing of adequate supply chain stages, given that counterparty relationship management has been proven to be crucial for efficient management of one's own business. The tensions existing in the relations between partners (increasingly long payment deadlines) are in the basis of the supply chain finance concept. Decisions made by banks are based on the entire supply chain (wide information basis), thereby shifting the focus from the product (as was the case before the crisis) to the client's needs. Thus, decisions become increasingly comprehensive, quicker, and more precise, and portfolios less risky. Through the individual portfolio of banks, the market of national economies also becomes safer and more liquid. These are rather profitable transactions, because, due to the risk transfer, financing is enabled to companies to whom classic crediting in most cases is not available.

Determinants of bank advisor revenues

PurposeThe focus of this paper is to highlight the research findings with regard to the performance of client advisors in retail banking by analyzing their revenues and the underlying determinants of those revenues. Retail banking activities are increasingly important to understand in terms of productivity and performance management due to the high degree of competitiveness. This paper takes external and internal determinants of bank advisor revenue performance comprehensively into account. The author also derives practical implications for bank managers.Design/methodology/approachBased on the theoretical framework, empirical models are developed, which are based on a cross‐sectional ordinary least squares analysis. In total, four regression models are employed – being the base model, and the three variants of that using differing parameters in order to ensure the robustness of the results. The database encompasses quite sensitive and specific data on 521 retail banking client advisors in Switzerland. Additionally, it is enriched by explanatory variables on a regional level considering the degree of competitiveness and the population in a region, which are expected to be important determinants in retail banking.FindingsFirst, bank advisors with closer proximity to clients and less distance to the community, combined with a longer period of work experience in that field, are more successful with regard to revenue performance. Second, the size of client portfolios, measured in number of clients and assets under management, client acquisition, client retention, the upgrades and downgrades across client segments, all have significant effects on revenue performance. Third, the competition and the population in a specific region need to be included in performance measurement and management by bank managers in order to ensure useful comparability across regions.Practical implicationsThe hypotheses, as well as the findings, are also discussed with bank managers in order to validate the results and to enhance their practical relevance to the banking industry. Important practical implications are: first, regional differences and competitive pressures need to be taken into account in the performance measurement systems in order to ensure comparability. Second, collaboration across client segments is crucial and needs to be fostered by appropriate organizational structures and incentives. Third, retail bank advisors, which are close to the clients and have more work experience are most successful, which is important for hiring activities.Social implicationsA better understanding of the determinants of bank advisor revenue performance is crucial as performance management systems in banking are difficult to predict due to the varying methods of implementation by bank managers in their daily business. This is especially the case for performance measurement and incentive systems, which also entail social implications with view on potentially detrimental effects, for instance for too aggressive targets without properly taking the client's credit worthiness into account. Furthermore, retail banking is a pivotal area in banking as most people are depending on retail banking infrastructure, services and products.Originality/valueThis paper contributes to the current research by improving the understanding of a bank advisor's performance, as there is very limited research in this field to date, especially when considering the quality of empirical data. The paper adds to research by improving bank managers’ understanding of the determinants of a bank advisor's revenue performance. Especially original is the detailed inclusion of external factors such as competition and population, and their effect on the revenues. In addition the analysis is comprehensive and includes a broad range of relevant factors with a high degree of data quality.

SMALL BUSINESSES CREDITWORTHINESS ASSESSMENT

Summary: The article is devoted to the problem of a choice of businesses’ creditworthiness estimation method, criteria of creditworthiness, defining the ways of its estimation based on a system of financial ratios (financial stability, cash flow analysis, business risk etc.)The main attention is paid to small businesses (SB) creditworthiness assessment and a choice of financial ratios, considering credit policy of a bank and operating conditions of SB. For an assessment of SB creditworthiness the authors single out five groups of ratios including liquidity rates, efficiency, financial leverage, profitability and debt servicing. Research of commercial banks experience allowed choosing business risk as an assessment of a client's credit worthiness. The authors focus attention on business risk factors; offer their grouping on stages of funds circulation and SB creditworthiness analysis procedure as follows: carrying out the general analysis of a business and its rating assessment - definition of a credit worthiness class.

A web transaction security scheme based on disposable credit card numbers

Today, an e-commerce transaction is performed by sending the client's credit card details over a SSL/TLS connection. This form of transaction raises many security threats. The most important one is the client authentication. Because the client normally does not have a public key certificate, the client's authentication is achieved only by the credit card details. In addition, these details are stored unencrypted in the merchant's database. A Disposable Credit Card Number (DCCN) is a technique to overcome these security threats. This paper presents a scheme for off-line generation of the DCCNs. The scheme uses a pre-shared secret key between the issuer and the client to generate Hashed Message Authentication Code for some selected data from the transaction. The scheme is secure and adds minimum overhead on the issuer and client. Furthermore, the generated DCCN has all the features of classical credit card details.