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Journal articles on the topic 'Credit risk assessment – Nigeria'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Ojeka, Stephen A., Alex Adeboye, and Olajide Dahunsi. "Does Audit Committee Characteristics Promote Risk Management Practices in Nigerian Listed Firms?" Accounting and Finance Research 10, no. 2 (May 26, 2021): 70. http://dx.doi.org/10.5430/afr.v10n2p70.

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There has been a huge and deluge of risk threatening industries at an unequalled magnitude in recent times. As such, the board of directors and senior executives are increasingly expected to manage their various organizations' risk portfolios, affecting their financial performance. This has led to the assigning of the risk assessment role to the audit committee. The board of directors and its audit committee play an essential function in Enterprise Risk Management (ERM) by building up the right condition or tone-at-the-top. Given the board's responsibilities for representing the interests of shareholders, it plays a vital role in overseeing management's approach to ERM. This study examined the relationship between audit committee characteristics and risk management of some selected listed firms in a developing country like Nigeria. The study used secondary data to describe the dependent variable (financial risk decomposed into credit risk and liquidity risk) and the explanatory variables (decomposed into audit committee accounting expertise, audit committee meetings, audit committee independence and audit committee gender). The study used pair sample t-test, student t-test, Pearson Moment Correlation and random panel data estimator for twenty (20) selected listed firms for 2012-2016. Findings indicate that there is a negative between audit committee accounting expertise and financial risk. This revealed that Accounting Expertise in Audit Committees are likely to involve in activities and practices to curb financial risk. In addition, the Audit committee meeting indicates a negative relationship with credit risk. Audit committee gender and audit committee independence have a negative effect on liquidity risk. Therefore, this study recommends that Audit committees embrace Enterprise Risk Management (ERM) to manage risks effectively across the organization. Risk management processes should be one of the major points of discussion during audit committee meetings.

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2

Gustafson, Cole R., Glenn D. Pederson, and Brent A. Gloy. "Credit risk assessment." Agricultural Finance Review 65, no. 2 (November 2005): 201–17. http://dx.doi.org/10.1108/00214660580001173.

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3

Bonsall, Samuel B., Eric R. Holzman, and Brian P. Miller. "Managerial Ability and Credit Risk Assessment." Management Science 63, no. 5 (May 2017): 1425–49. http://dx.doi.org/10.1287/mnsc.2015.2403.

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4

Chatzoglou, Prodromos D., Ioannis Eleftheriades, and Evdokia Tsifora. "Credit risk assessment: a field research." International Journal of Economic Policy in Emerging Economies 2, no. 4 (2009): 372. http://dx.doi.org/10.1504/ijepee.2009.030938.

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5

Pinson, Suzanne. "Credit risk assessment and meta-judgment." Theory and Decision 27, no. 1-2 (1989): 117–33. http://dx.doi.org/10.1007/bf00133991.

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6

UKWUABA, Ikenna Charles. "ASSESSMENT OF AGRICULTURAL CREDIT SOURCES AND ACCESSIBILITY IN NIGERIA." Review of Agricultural and Applied Economics 23, no. 2 (October 2020): 3–11. http://dx.doi.org/10.15414/raae.2020.23.02.03-11.

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7

Iyer, K. C., and Dhruba Purkayastha. "Credit Risk Assessment in Infrastructure Project Finance:Relevance of Credit Ratings." Journal of Structured Finance 22, no. 4 (January 31, 2017): 17–25. http://dx.doi.org/10.3905/jsf.2017.22.4.017.

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8

UJUJU, LUCKY EJIEH, and JULIUS OVUEFEYEN EDORE. "CREDIT RISK MANAGEMENT PRACTICES AND PERFORMANCE OF NIGERIA BANKS." International Journal of Social Science and Economic Research 05, no. 05 (May 30, 2020): 1223–52. http://dx.doi.org/10.46609/ijsser.2020.v05i05.010.

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9

Luo, Na, Jiayi Yang, Yuanfeng Zhu, and Yu Zhang. "The Risk Management of Commercial Banks——Credit-Risk Assessment of Enterprises." International Journal of Economics and Finance 8, no. 9 (August 24, 2016): 69. http://dx.doi.org/10.5539/ijef.v8n9p69.

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With the diversified developments of the financial market, commercial banks are confronted with various risks, among which the credit risk is the core, and thus the assessment of enterprises’ credit risks is especially important in the credit process of the commercial banks. Based on the relevant researches about commercial banks’ credit risk management, the paper carries out a deep analysis on the factors that may affect the credit risk assessment and then establishes a relatively comprehensive credit risk assessment system. In this paper, we apply our risk assessment model, which is established on the basis of GRNN neural network model, to make an empirical analysis with the selected sample data. And the results suggest that the hit rates of identifying high quality enterprises and low quality enterprises are 92.16 percent and 93.75 percent, respectively, indicating that the model has realized a good prediction.

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10

Charles, Onyeiwu, Gideon Ajayi, and Obumneke Muoneke B. "The Impact of Credit Risk on Bank Profitability in Nigeria." Journal of Banking and Financial Economics 1/2020, no. 13 (August 30, 2020): 5–22. http://dx.doi.org/10.7172/2353-6845.jbfe.2020.1.1.

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This study examines the impact credit risk management has on the profitability of commercial banks in Nigeria. The main objective of this material is to show how credit risk parameters are related to the expected performance of commercial banks in Nigeria. Using the regression analysis, relationship was drawn between credit risk parameters (which include capital adequacy ratio and non-performing loan ratio) and the profitability ratio (return on average asset, in particular) of five big Nigerian banks. Mixed research methodology was adopted in that primary data were sourced via questionnaires and secondary data were used via annual report of selected banks. Regression analysis was used to analyse the data. The conclusion drawn from the data analysis shows that there is a strong relationship between credit risk parameters and returns of the bank implying that credit risk management has a strong impact on the profitability of commercial banks in Nigeria. The study recommends that banks’ capital should be matched with their total risk exposure and if there is an imbalance, new capital requirements are necessary. Insider-related interests in loan applications should be closely monitored by the regulators to ensure continuous performance of the loan facility. Also, there should be an extant profiling of loan defaulters whether individuals or corporate entities.

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11

Erdogan, Olcay, and Zafer Konakli. "Corporate Credit Risk Assessment of BIST Companies." European Scientific Journal, ESJ 14, no. 1 (January 31, 2018): 122. http://dx.doi.org/10.19044/esj.2018.v14n1p122.

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Assessing credit risk allows financial institutions to plan future loans freely, to achieve targeted risk management and gain maximum profitability. In this study, the constructed risk assessment models are on a sample data which consists of financial ratios of enterprises listed in the Bourse Istanbul (BIST). 356 enterprises are classified into three levels as the investment, speculative and below investment groups by ten parameters. The applied methods are discriminant analysis, k nearest neighbor (k-NN), support vector machines (SVM), decision trees (DT) and a new hybrid model, namely Artificial Neural Networks with Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This study will provide a comparison of models to build better mechanisms for preventing risk to minimize the loss arising from defaults. The results indicated that the decision tree models achieve a superior accuracy for the prediction of failure. The model we proposed as an innovation has an adequate performance among the applied models

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12

Attigeri, Girija V., M. M. Manohara Pai, and Radhika M. Pai. "Credit Risk Assessment Using Machine Learning Algorithms." Advanced Science Letters 23, no. 4 (April 1, 2017): 3649–53. http://dx.doi.org/10.1166/asl.2017.9018.

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13

Lin, Ching Wen. "Risk indicators of e-credit assessment system." International Journal of Electronic Finance 5, no. 3 (2011): 235. http://dx.doi.org/10.1504/ijef.2011.041338.

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14

Ayayi, Ayi Gavriel. "Credit risk assessment in the microfinance industry." Economics of Transition 20, no. 1 (October 19, 2011): 37–72. http://dx.doi.org/10.1111/j.1468-0351.2011.00429.x.

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15

Cantor, Richard, and Frank Packer. "Sovereign risk assessment and agency credit ratings." European Financial Management 2, no. 2 (July 1996): 247–56. http://dx.doi.org/10.1111/j.1468-036x.1996.tb00040.x.

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16

Yamanaka, Suguru. "Credit risk assessment using purchase order information." International Journal of Financial Engineering 05, no. 04 (December 2018): 1850041. http://dx.doi.org/10.1142/s242478631850041x.

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This paper proposes advanced credit risk assessment and lending operations using purchase order information from borrower firms. Purchase order information from a borrower firm is useful for financial institutions to evaluate the actual business conditions of the firm. This paper shows the application of purchase order information to lending operations and credit risk assessment, and reveals its effectiveness. First, we propose a “purchase order based” credit risk model for real-time credit risk monitoring of firms. Financial institutions can monitor the actual business conditions of borrower firms by evaluating the firm’s asset value using purchase order information. A combination of traditional firm monitoring using financial statements and high-frequency monitoring using purchase order information enables financial institutions to assess the business conditions of borrower firms more precisely and efficiently. Then, with high-frequency data, financial institutions can give borrower firms appropriate support if necessary on a timely basis. Second, we illustrate purchase order financing, which is the lending method backed by purchase order information from borrowers. With purchase order financing, firms that consistently receive purchase orders from credit-worthy firms can borrow money under more favorable lending terms than the usual lending terms based on the financial statements of the borrower firm.

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17

Batta, George, Ananda Ganguly, and Joshua Rosett. "Financial statement recasting and credit risk assessment." Accounting & Finance 54, no. 1 (November 9, 2012): 47–82. http://dx.doi.org/10.1111/acfi.12002.

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18

Kong, Dequan, Robert L. Tiong, Charles Y. Cheah, Andre Permana, and Matthias Ehrlich. "Assessment of Credit Risk in Project Finance." Journal of Construction Engineering and Management 134, no. 11 (November 2008): 876–84. http://dx.doi.org/10.1061/(asce)0733-9364(2008)134:11(876).

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19

Figini, Silvia, and Paolo Giudici. "Credit risk assessment with Bayesian model averaging." Communications in Statistics - Theory and Methods 46, no. 19 (September 12, 2016): 9507–17. http://dx.doi.org/10.1080/03610926.2016.1212070.

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20

Ghodrati, Hassan, and Gholamhassan Taghizad. "Credit risk assessment: Evidence from banking industry." Management Science Letters 4, no. 8 (2014): 1765–72. http://dx.doi.org/10.5267/j.msl.2014.7.007.

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21

Chen, Ning, Bernardete Ribeiro, and An Chen. "Financial credit risk assessment: a recent review." Artificial Intelligence Review 45, no. 1 (October 27, 2015): 1–23. http://dx.doi.org/10.1007/s10462-015-9434-x.

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22

Twala, Bhekisipho. "Multiple classifier application to credit risk assessment." Expert Systems with Applications 37, no. 4 (April 2010): 3326–36. http://dx.doi.org/10.1016/j.eswa.2009.10.018.

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23

Crook, Jonathan N., David B. Edelman, and Lyn C. Thomas. "Recent developments in consumer credit risk assessment." European Journal of Operational Research 183, no. 3 (December 2007): 1447–65. http://dx.doi.org/10.1016/j.ejor.2006.09.100.

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24

Yang, Zhen. "Utilization of Quantization Method on Credit Risk Assessment." Applied Mechanics and Materials 472 (January 2014): 432–36. http://dx.doi.org/10.4028/www.scientific.net/amm.472.432.

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Credit risk assessment is critical factor in credit risk management, which has played a key role in financial and banking industry. Many classification methods are used in credit risk assessment aiming to establish classifiers to predict the credit state of the corporate (good or bad). However, most of classification methods can not handle continuous variables. So, continuous variables must be quantified. In this paper, we first propose an improved quantization method, namely IDM, based on the statistical independence; then we use data mining techniques, i.e., C4.5 decision tree, Naive-Bayes and SVM classifier, to classify and predict the quantified credit data. The aim is to investigate the effect of quantization method on the classification of credit approval data. The Experimental results show that our approach significantly improves the mean accuracy of classification than other known quantization methods. This denotes that the proposed method can make an effective interpretation and point out the ability of design of a new intelligent assistance credit approval data system.

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25

Leggio, Karyl B., Yoon S. Shin, and Yuxing Yan. "Assessment of Credit Ratings and Credit Risk Models on Public Bonds." Journal of Fixed Income 30, no. 4 (January 28, 2021): 65–80. http://dx.doi.org/10.3905/jfi.2021.1.104.

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26

Minh, Hieu. "Analysing Liquidity, Credit Risk and Deposit Money Banks Profitability in Nigeria." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 2436–42. http://dx.doi.org/10.17762/turcomat.v12i3.1235.

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This study examined the impact of liquidity and credit risk on profitability of selected deposit money banks(DMBs) in Nigeria. The study adopts an empirical longitudinal research design and used data set covering 7-year period from 2012 to 2018 from the annual reports of five DMBs. Pearson Correlation and multiple regression techniques were used in empirical analysis and testing of hypotheses. The result shows that there is a significant relationship between liquidity and profitability measured as return on equity (ROE). There is an insignificant relationship between credit risk variables and profitability (ROE). The paper recommends that optimal level of liquidity should be maintained to reduce cash sterility in the assets of banks. Though credit risk is not a main determinant of bank profitability; effective strategies should be put in place to monitor, control and manage credit risk.

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27

Niklis, Dimitrios, Michalis Doumpos, and Constantin Zopounidis. "Credit Risk Modelling." International Journal of Sustainable Economies Management 7, no. 3 (July 2018): 50–64. http://dx.doi.org/10.4018/ijsem.2018070105.

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The assessment of businesses' credit risk is a difficult and important process in the area of financial risk management. In a classical multivariate model, financial ratios are combined in order to achieve a credit risk score, which signals if a loan application is approved or discarded. Despite their good performance, the developed multivariate models using statistical methods have been widely criticized. They are based on models that use accounting data, which have the disadvantage of being static and so often fail to follow the changes in the economic and business environment. In recent years, market models (structural and reduced form models) have become popular among banks and financial institutions, because of their theoretical background and the use of updated information. The aim of this article is to present an overview of basic market models (structural models, reduced form models and market models used from credit institutions) together with their characteristics in order to outline their development throughout the last decades.

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28

Poudel, Shiva Raj. "ASSESSMENT OF CREDIT RISK IN NEPALI COMMERCIAL BANKS." Journal of Applied and Advanced Research 3, no. 3 (May 14, 2018): 65. http://dx.doi.org/10.21839/jaar.2018.v3i3.137.

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The main objective of the study is to identify the major indicators of credit risk among the Nepali commercial banks. The study is conducted using the sample of 15 commercial banks operated in Nepali economy. One way Fixed Effect Model (FEM) of panel data analysis is used as a major tool of analysis. All the data for the study were obtained from the database of Nepal Rastra Bank for bank specific variables and database of World Bank for macroeconomic variables for the year 2002/03 to 2014/15. The credit risk among the commercial banks in Nepal was regressed on bank specific variables such as liquidity, capital adequacy ratio, bank size, and interest spread. Similarly, the effects of macro-economic variables such as GDP growth, rate of inflation and interbank interest rate were also examined along with bank specific variables in identifying credit risk in Nepali commercial banks. The study reveals that liquidity has the significant positive impact on credit risk in Nepali commercial banks. In contrast, capital adequacy ratio and interest spread have the significant negative impact on credit risk. The analysis further confirmed that bank size and interest spread both have no any clear direction of impact on credit risk. Moving towards the GDP growth, credit risk in Nepali commercial banks is negatively fluctuates with GDP growth, however, the statistics show the coefficients are insignificant at 5% level. Contrarily, Inter-bank interest rate has insignificant negative impact on credit risk in Nepali commercial banks.

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29

Ferreira, Fernando A. F., Ieva Meidutė-Kavaliauskienė, Edmundas K. Zavadskas, Marjan S. Jalali, and Sandra M. J. Catarino. "A Judgment-Based Risk Assessment Framework for Consumer Loans." International Journal of Information Technology & Decision Making 18, no. 01 (January 2019): 7–33. http://dx.doi.org/10.1142/s021962201850044x.

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Credit to personal consumption is an important activity of the financial system and crucial to the socioeconomic development of a country. It is important, therefore, that the methods and techniques used to evaluate consumer credit risk be as efficient and informative as possible, in order to strengthen decisions to approve or reject credit and promote sustainable economic growth. This study aims to create a multiple criteria expert system which integrates cognitive maps and the measuring attractiveness by a categorical-based evaluation technique (MACBETH) to create a complementary framework for consumer credit risk assessment. The results show that this integrated approach allows the evaluation process of consumer credit risk to be more informed and transparent, providing value for the evaluation processes of this type of credit application as a result of the privileged contact established with a panel of credit analysts. Advantages, limitations, and managerial implications are also discussed.

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30

A. Obalade, Adefemi, Babatunde Lawrence, and Joseph Olorunfemi Akande. "Political risk and banking sector performance in Nigeria." Banks and Bank Systems 16, no. 3 (July 9, 2021): 1–12. http://dx.doi.org/10.21511/bbs.16(3).2021.01.

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Political risk is prevalent in Nigeria and tends to influence business outcomes and the stability of the banking system. As a result of this study, it was determined whether political risk matters to the performance of the banking sector in Nigeria. The effect of political risk on different banks’ performance measures, such as return on assets, return on invested capital, credit risk and stock price, were examined in a panel of 12 selected commercial banks for the period 2006–2018. Data was analyzed using a two-stage system of generalized method of moments. The results provided evidence that the effect of political risk on bank performance depends on the performance proxies. Specifically, political risk was found to be negatively related to banks’ returns on invested capital and positively related to deteriorating credit risk. Hence, it can be concluded that political risk induces poor banking system performance in Nigeria. The study provides a critical insight into the management of a country’s political systems in terms of their potential to create unfavorable conditions for banking systems to thrive.

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31

Každailienė, Jūratė, and Dalia Daujotaitė. "Assessment of trade credit risk in business companies." Buhalterinės apskaitos teorija ir praktika, no. 15A (July 9, 2014): 133–48. http://dx.doi.org/10.15388/batp.2014.15a.11.

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The article‘s topic is relevant, because the importance of trade credit constantly increases. Trade credit can be one of the most important preconditions for the competitiveness of the enterprise and business development. There is a lack of scientific sources in the field of estimation of trade credit risk – there is no any particular, simple to use methodology to assess company trade credit risk. The aim of the article – to compose a methodology of assessment company trade credit risk for Lithuanian small and medium enterprises. In the article, following scientific sources approach, the advantages, risks and key financial and non-financial trade credit risk factors were identified. Based on the scientific sources and expert evaluation, the methodology of company trade credit risk assessment was created. The methodology is based on 10 key indicators identified by the experts. 12 financial ratios and non-financial indicators are being used – current and quick ratio, gross and net profit margin, Altman Z model, debt ratio, stock and debtors turnover, enterprise age, reputation, number and dynamics of employees. The indicators have been scored. The highest possible score is 100. The research approves that the methodology suits to be used in practice. It is simple, reliable, cheap, non-time consuming; it is easy to collect data, the data is being formalized and quantified. The disadvantages of methodology – the data is not always reliable and some ratios are of different importance in the different economic sectors. The methodology should be modified to adapt for the different sectors of business.

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32

Bahago, Kashema, Gylych Jelilov, and Bilal Celik. "IMPACT OF BANKING SUPERVISION ON LIQUIDITY RISK AND CREDIT RISK: EVIDENCE FROM NIGERIA." International Journal of Economics and Financial Issues 9, no. 3 (May 1, 2019): 200–204. http://dx.doi.org/10.32479/ijefi.8073.

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33

Datti, M. I., R. Said, N. W. Ismail, and A. Abd. Rahman. "Assessment of major requirements for accessing credit among paddy farmers in Jigawa state, Nigeria." Food Research 5, no. 2 (February 27, 2021): 74–79. http://dx.doi.org/10.26656/fr.2017.5(2).339.

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This paper examined major credit requirements of financial institutions in providing credit to paddy farmers of Jigawa state, Nigeria. Data were collected in 2019 from three selected paddy farming local government areas of the state. A total of 120 respondents were randomly selected through a multistage sampling technique, and a questionnaire. The binary logit model and the marginal effect were applied in the analysis. The results indicated that paddy farmers' educational level, family size, and guarantor requirements were statistically significant on access to credit, with their P-value signifies 0.041, 0.060, and 0.000, respectively. While, farm size, administrative process, collateral requirement, interest charge, and principal repayment duration were insignificant on accessing credit. Failure to address these problems may continue to worsen the Nigerian government's effort on food self-sufficient and poverty alleviation. The study suggests similar research to consider more years to see the impact in the long term. The study further recommends credit providers to modify the guarantor requirement and to delegate a staff who can translate and guide the applicants on how to fill the credit application forms

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34

Inegbedion, Henry, Bello Deva Vincent, and Eseosa Obadiaru. "Risk Management and the Financial Performance of Banks in Nigeria." International Journal of Financial Research 11, no. 5 (September 22, 2020): 115. http://dx.doi.org/10.5430/ijfr.v11n5p115.

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The study examined “risk management and financial performance of banks in Nigeria” with focus on commercial banks. The broad objective of the study was to ascertain the effect of risk asset management on the optimal financial performance of commercial banks in Nigeria. The study is a longitudinal survey, so the ex-post facto research design was applied. Research data were analysed using generalized method of moments (GMM) and vector Error Correction Model, after testing and adjusting the data for stationarity and Cointegration.The research findings were: Banks’ profitability is significantly influenced in the short run by liquidity risk and in the long-run by credit risk, capital adequacy risk, leverage risk and liquidity risk. Furthermore, profitability measured by ROaA was found to be positively related to liquidity risk but negatively related credit risk. Arising from the findings, there is the need for effective risk management, especially credit, capital adequacy, leverage and liquidity risks, to enhance the profitability of banks. By helping to enhance the going concern of banks, risk management will help to reduce retrenchment and unemployment and hence help to forestall the attendant social vices.

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35

Ljubic, Marijana, Vladan Pavlovic, and Srecko Milancic. "The impact of credit risk assessment on credit activity of commercial banks." Megatrend revija 12, no. 3 (2015): 141–52. http://dx.doi.org/10.5937/megrev1503141l.

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36

Li, Jia Jun, Li Ping Qin, and Jia Zhao. "To Construct an Individual Credit Risk Assessment Method." Advanced Materials Research 143-144 (October 2010): 116–19. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.116.

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To achieve low costs and better accuracy of individual risk assessments, we constructed a practical method based on multiple classifiers. The method includes many singal classifiers, such as decision trees and the cluster analysis. And we tested it empirically. The result shows that the application of the method can achieve better accuracy than any single classifier of it.

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37

Ribeiro, Bernardete, and Ning Chen. "Financial credit risk assessment via learning-based hashing." Intelligent Decision Technologies 11, no. 2 (June 22, 2017): 177–86. http://dx.doi.org/10.3233/idt-170286.

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38

Mohammadi, Nasser, and Maryam Zangeneh. "Customer Credit Risk Assessment using Artificial Neural Networks." International Journal of Information Technology and Computer Science 8, no. 3 (March 8, 2016): 58–66. http://dx.doi.org/10.5815/ijitcs.2016.03.07.

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39

Kalapodas, Evangelos, and Mary E. Thomson. "Credit risk assessment: a challenge for financial institutions." IMA Journal of Management Mathematics 17, no. 1 (January 1, 2006): 25–46. http://dx.doi.org/10.1093/imaman/dpi026.

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40

Bae, Deog Sang, and Ivan Damnjanovic. "Credit Risk Assessment and Monitoring of TIF Bonds." Journal of Structured Finance 23, no. 4 (January 8, 2018): 57–68. http://dx.doi.org/10.3905/jsf.2018.2018.1.062.

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41

Bae, Deog Sang, and Ivan Damnjanovic. "Credit Risk Assessment and Monitoring of TIF Bonds." Journal of Structured Finance 23, no. 4 (January 22, 2018): 57–68. http://dx.doi.org/10.3905/jsf.2018.23.4.057.

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42

Niu, Xuelei, and Yufu Zheng. "Credit Card Risk Assessment Based on Machine Learning." Journal of Physics: Conference Series 1213 (June 2019): 022015. http://dx.doi.org/10.1088/1742-6596/1213/2/022015.

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43

Lyra, Marianna, Akwum Onwunta, and Peter Winker. "Threshold accepting for credit risk assessment and validation." Journal of Banking Regulation 16, no. 2 (January 29, 2014): 130–45. http://dx.doi.org/10.1057/jbr.2013.26.

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44

Doumpos, Michael. "A Stacked generalization framework for credit risk assessment." Operational Research 2, no. 2 (May 2002): 261–78. http://dx.doi.org/10.1007/bf02936330.

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45

Marqués, A. I., V. García, and J. S. Sánchez. "Two-level classifier ensembles for credit risk assessment." Expert Systems with Applications 39, no. 12 (September 2012): 10916–22. http://dx.doi.org/10.1016/j.eswa.2012.03.033.

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46

Olaoye, F. O., and T. C. Ojuolape. "Credit Risk Disclosure Compliance And Bank Performance In Nigeria: A Case Study Of Zenith Bank PLC." Archives of Business Research 7, no. 8 (August 13, 2019): 109–13. http://dx.doi.org/10.14738/abr.78.4055.

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The study examined the influence of credit risk disclosure compliance on bank performance in Nigeria. IFRS 7 state that entity should disclose credit risk in their financial report. Credit risk may affect going concern concept and audit report is silent about it. The specific objective of the study is to ascertain the influence of credit risk disclosure on bank profitability. Linear regression analysis was used to analyse the data collected from financial reports, with the help of SPSS 20.0 version. The study discovered that there is positive relationship between credit risk disclosure and bank profitability. The study therefore recommends that the financial institutions regulators should enforce credit risk disclosure in their financial reports as this will help the stakeholders to make informed investment decision.

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47

Peškauskaitė, Dovilė, and Daiva Jurevičienė. "Companies Credit Risk Assessment Methods for Investment Decision Making." Mokslas - Lietuvos ateitis 9, no. 2 (June 2, 2017): 220–29. http://dx.doi.org/10.3846/mla.2017.1014.

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As the banks have tightened lending requirements, companies look for alternative sources of external funding. One of such is bonds issue. Unfortunately, corporate bonds issue as a source of funding is rare in Lithuania. This occurs because companies face with a lack of information, investors fear to take on credit risk. Credit risk is defined as a borrower’s failure to meet its obligation. Investors, in order to avoid credit risk, have to assess the state of the companies. The goal of the article is to determine the most informative methods of credit risk assessment. The article summarizes corporate lending sources, analyzes corporate default causes and credit risk assessment methods. The study based on the SWOT analysis shows that investors before making an investment decision should evaluate both the business risk,using qualitative method CAMPARI, and the financial risk, using financial ratio analysis.

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Yusuf, Iliyasu, Orifah Martins, and Ahungwa Gabriel. "Assessment of Rural Farmers’ Access to Credit in Jigawa State, Nigeria." Asian Journal of Agricultural Extension, Economics & Sociology 21, no. 4 (December 29, 2017): 1–12. http://dx.doi.org/10.9734/ajaees/2017/32309.

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49

Oliyide, Olusesan. "Law, credit risk management and bank lending to SMEs in Nigeria." Commonwealth Law Bulletin 38, no. 4 (October 2012): 673–95. http://dx.doi.org/10.1080/03050718.2012.707350.

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50

HAORU, WANG, Yi Zhixuan, WEI YUJIA, Tianpeng Yao, Zhao Shuoheng, and Xuzhi qiang. "Risk Assessment of Internet Credit Based on Big Data Analysis." E3S Web of Conferences 214 (2020): 01012. http://dx.doi.org/10.1051/e3sconf/202021401012.

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In recent years, network technology has continued to develop, and Internet finance has rapidly developed into a new business area. Internet credit is one of the important ways for banks to conduct business, and the scale of online credit has continued to expand. Due to the existence of various unpredictable factors, frequent emergencies, and online financial fraud, the overall market risk in the field of online credit has increased, and the rate of non-performing loans has continued to increase. Online financial fraud cases show that online credit risk has become one of the most prominent risks in the operation of commercial banks, which has a direct impact on the stability and development of commercial banks. We can build a bank database system based on big data, introduce professional big data analysis technical personnel, and constantly improve the big data sharing analysis platform, so that commercial banks can use system data more fully and effectively, and facilitate relevant business personnel to use big data technology for analysis and calculation. Big data is constantly produced, which provides basic materials for online credit risk assessment. Big data analysis technology is gradually mature, and it has the necessary conditions for online credit risk assessment. Based on the theories and technologies related to big data analysis, this paper comprehensively evaluates the online credit risk in the form of example data analysis, thereby effectively reducing the online credit risk coefficient.

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