Provision for Bad Debts

Lenders such as banks and credit card companies while reviewing a client's request for loan use credit scores. Credit scores help measure the creditworthiness of the client using a numerical score. Now it has been found out that the problem can be optimized by using various statistical models. In this study a wide range of statistical methods in machine learning have been applied, though the datasets available to the public is limited due to confidentiality concerns. Problems particular to the context of credit scoring are examined and the statistical methods are reviewed.

Credit scoring attracts special attention of financial institutions. In recent years, deep learning methods have been particularly interesting. In this paper, we compare the performance of ensemble deep learning methods based on decision trees with the best traditional method, logistic regression, and the machine learning method benchmark, support vector machines. Each method tests several different algorithms. We use different performance indicators. The research focuses on standard datasets relevant for this type of classification, the Australian and German datasets. The best method, according to the MCC indicator, proves to be the ensemble method with boosted decision trees. Also, on average, ensemble methods prove to be more successful than SVM.

Developing an efficient credit scoring model to reduce the risk of personal-loan defaulters involves the selection of manageable reliable predictor variables in order to avoid the potential clients from providing too much information and to reduce the burden of a bank from keeping huge historical data, which can be burdensome and costly. The objective of this paper is therefore to illustrate how compromised-AHP can be used as one the methods to select such relevant reliable predictor variables before the final credit scoring model is constructed. A case study involving four experts from a bank was conducted. A set of sub-predictor variables under four main predictor variables namely financial indicators, demographic Indicators, employment indicators, and behavioural indicators was rated based on the perception of the four experts. The results reveal that, based on the experts' perception, the number of payments per year and payment interval, the loan or credit history, total income, total debt, the checking accounts, and age are the six most influential predictor variables while race, gender, and social status are the three least influential predictor variables.

Polish bank law defines credit ability as the ability to repay a credit and interest according to terms that have been set in the credit agreement. Credit scoring is a crucial element for any bank with a fundamental impact on its future financial condition. Credit scoring can be calculated with the application of statistical methods. The main aim of this paper is to present the possibility of an ensemble of symbolic decision stumps in credit scoring where two real data sets are used. Results show that symbolic decision stumps can be applied in individual credit scoring.

A discriminant model for evaluation of loan •ppficants by bank m•nngers should be capable of handling a wide variety of data in • parsimonious manner. Frequently the data will be discrete, and not eaily converted to continuous form. Furthermore, missing data may be a problem. This paper examines the use of a discrete discriminant analytie technique in credit screening in • rural setting. The el~/cocy of the model is evaluated with regard to both rate and costs of misclossification.

The main aim of this paper is to investigate how far applying suitably conceived and designed credit scoring models can properly account for the incidence of default and help improve the decision-making process. Four statistical modelling techniques, namely, discriminant analysis, logistic regression, multilayer feed-forward neural network and probabilistic neural network are used in building credit scoring models for the Indian banking sector. Notably actual misclassification costs are analysed in preference to estimated misclassification costs. Our first-stage scoring models show that sophisticated credit scoring models, in particular probabilistic neural networks, can help to strengthen the decision-making processes by reducing default rates by over 14%. The second-stage of our analysis focuses upon the default cases and substantiates the significance of the timing of default. Moreover, our results reveal that State of residence, equated monthly instalment, net annual income, marital status and loan amount, are the most important predictive variables. The practical implications of this study are that our scoring models could help banks avoid high default rates, rising bad debts, shrinking cash flows and punitive cost-cutting measures.

The current system in the bank depends only on the client's failure to pay monthly installments for three consecutive months to start moving and take the necessary actions towards the client. This routine system is the basic reason of happening the problem of loans default. In this paper the researcher presents a comparative mathematical model to predict the default of clients, as well as to devise a modern parallel model to measure the degree of credit risk criteria that guides the bank in the following-up of the client. Altman model is one of the famous methods for default prediction, formula is used to predict the probability of loan default by using Z-scores. The Z-score is a linear combination of five coefficient-weighted common financial criteria. The researcher applies the Neutrosophic Analytical Hierarchy Process (NAHP) model on the same five common financial criteria which the bank can using them to provide constant following-up of the uses of the granted loan to guaran...

Jordanian banks traditionally use a set of indicators, based on their internal explicit knowledge to examine the credit risk caused by default loans of individual borrowers. The banks are reliant on the personal and financial information of the borrowers, obtained by knowing them, often referred as internal explicit knowledge. Internal explicit knowledge characterizes both financial and non-financial indicators of individual borrowers, such as; loan amount, educational level, occupation, income, marital status, age, and gender. The authors studied 2755 default or non-performing personal loan profiles obtained from Jordanian Banks over a period of 1999 to 2014. The results show that low earning unemployed borrowers are very likely to default and contribute to non-performing loans by increasing the chances of credit risk. In addition, it is found that the unmarried, younger borrowers and moderate loan amount increase the probability of non-performing loans. On the contrary, borrowers ...

The main aim of this paper is to investigate how far applying suitably conceived and designed credit scoring models can properly account for the incidence of default and help improve the decision-making process. Four statistical modelling techniques, namely, discriminant analysis, logistic regression, multilayer feed-forward neural network and probabilistic neural network are used in building credit scoring models for the Indian banking sector. Notably actual misclassification costs are analysed in preference to estimated misclassification costs. Our first-stage scoring models show that sophisticated credit scoring models, in particular probabilistic neural networks, can help to strengthen the decision-making processes by reducing default rates by over 14%. The second-stage of our analysis focuses upon the default cases and substantiates the significance of the timing of default. Moreover, our results reveal that State of residence, equated monthly instalment, net annual income, marital status and loan amount, are the most important predictive variables. The practical implications of this study are that our scoring models could help banks avoid high default rates, rising bad debts, shrinking cash flows and punitive cost-cutting measures.

There are quite complicated rules and constraints that can b e imposed by the bank when the loan issued. Bank branches, which play a direct role in the credit, must accurately deter mine the customer’s credit request to eliminate these diffic ulties and create an effective payment system according to the customer. In th e study, 100 random loan applications made in 2016 of a bank br anch operating in the Black Sea Region were examined. These custo mer demands are affecting customer characteristics. The ”L ogistic Regression (LR) Model” was created to predict creditworthi ness according to the identified fugitives. In the model, cus tomer age, education, marital status, debt grade, credit card debt, ot her debts, cross product are the variables. These are statis tically significant in terms of marital status, gender, cross product, or creditwo rthiness. However, various variables such as debt income ra tio, credit card debt, and other debts are statistically significant and affe ct c...

Credit risk estimation is a key determinant for the success of financial institutions. The aim of this paper is presenting a new hybrid model for estimating the probability of default of corporate customers in a commercial bank. This hybrid model is developed as a combination of Logit model and Neural Network to benefit from the advantages of both linear and non-linear models. For model verification, this study uses an experimental dataset collected from the companies listed in Tehran Stock Exchange for the period of 2008–2014. The estimation sample included 175 companies, 50 of which were considered for model testing. Stepwise and Swapwise least square methods were used for variable selection. Experimental results demonstrate that the proposed hybrid model for credit rating classification outperform the Logit model and Neural Network. Considering the available literature review, the significant variables were gross profit to sale, retained earnings to total asset, fixed asset to to...

Credit risk estimation is a key determinant for the success of financial institutions. The aim of this paper is presenting a new hybrid model for estimating the probability of default of corporate customers in a commercial bank. This hybrid model is developed as a combination of Logit model and Neural Network to benefit from the advantages of both linear and non-linear models. For model verification, this study uses an experimental dataset collected from the companies listed in Tehran Stock Exchange for the period of 2008–2014. The estimation sample included 175 companies, 50 of which were considered for model testing. Stepwise and Swapwise least square methods were used for variable selection. Experimental results demonstrate that the proposed hybrid model for credit rating classification outperform the Logit model and Neural Network. Considering the available literature review, the significant variables were gross profit to sale, retained earnings to total asset, fixed asset to to...

Credit risk estimation is a key determinant for the success of financial institutions. The aim of this paper is presenting a new hybrid model for estimating the probability of default of corporate customers in a commercial bank. This hybrid model is developed as a combination of Logit model and Neural Network to benefit from the advantages of both linear and non-linear models. For model verification, this study uses an experimental dataset collected from the companies listed in Tehran Stock Exchange for the period of 2008–2014. The estimation sample included 175 companies, 50 of which were considered for model testing. Stepwise and Swapwise least square methods were used for variable selection. Experimental results demonstrate that the proposed hybrid model for credit rating classification outperform the Logit model and Neural Network. Considering the available literature review, the significant variables were gross profit to sale, retained earnings to total asset, fixed asset to to...

This research study summarizes the loan evaluation method known as credit scoring.Credit scoring is a technique that helps banks decides whether to grant credit to applicants who apply to them or not.The main objective of the research was to evaluate credit risk in commercial banks of Pakistan using credit scoring models.The requirement of credit scoring models by commercial banks of Pakistan to assess the creditworthiness of individuals was described. A credit scoring modelwas developed called as Credit Scoring Model for Individuals (CSMI), which can be used by commercial banks to determine the creditworthiness of individual borrowers requesting for personal loans. The CSMI was explained along with a detailed look at different credit scoring models. The results of the developed credit scoring model were compared with the other statistical credit scoring techniques known as logistics regression and discriminant analysis. Type I and type II errors had been calculated for all the credit scoring models used. The results shows that the proposed model "CSMI"has more accuracy rate with no errors as compared to LR and DA.Also, several suggestions for further research were presented.

Pelanggan merupakan aset terpenting pada sebuah perusahaan, termasuk pada perusahaan pada bidang telekomunikasi. Banyaknya data pelanggan dapat dimanfaatkan untuk mencari informasi lebih detail mengenai pelanggan sehingga dapat memberikan pelayanan terbaik bagi pelanggan. Profil pelanggan dapat membantu perusahaan untuk lebih memahami basis pelanggan yang dimiliki. Profil pelanggan diperlukan perusahaan untuk mengetahui karakteristik pelanggan sehingga mempermudah perusahaan melakukan pemasaran terhadap para pelanggan tersebut. Profil pelanggan dapat dilakukan dengan intelijen bisnis. Salah satu komponen intelijen bisnis adalah data warehouse. Data warehouse digunakan untuk menyimpan data secara historis yang berasal dari data transaksional. Penelitian ini menghasilkan skema data warehouse sesuai dengan kebutuhan bisnis perusahaan.

This research paper summarizes the development of a credit scoring model known as Credit Scoring Model for Corporations (CSMC), which can be used to evaluate the creditworthiness of corporate borrowers before granting loan. Altman Z-Score model was part of the CSMC. The dataset consists of 30 corporate borrowers of rejected and accepted corporations from the textile and chemical industry of Pakistan. The developed credit scoring model can be used by credit analysts, lending institutions, shareholders, financial institutions and auditors to predictcredit worthiness of the corporations. The credit scoring model was explained along with a detailed look at different credit scoring models. The results of the all developed credit scoring models were compared with the other statistical credit scoring techniques known as logistics regression and discriminant analysis. Type I and type II errors had been calculated for all the credit scoring models used. The results show that the proposed mod...

The main aim of this paper is to investigate how far applying suitably conceived and designed credit scoring models can properly account for the incidence of default and help improve the decision-making process. Four statistical modelling techniques, namely, discriminant analysis, logistic regression, multi-layer feed-forward neural network and probabilistic neural network are used in building credit scoring models for the Indian banking sector. Notably actual misclassification costs are analysed in preference to estimated misclassification costs. Our first-stage scoring models show that sophisticated credit scoring models, in particular probabilistic neural networks, can help to strengthen the decision-making processes by reducing default rates by over 14%. The second-stage of our analysis focuses upon the default cases and substantiates the significance of the timing of default. Moreover, our results reveal that State of residence, equated monthly instalment, net annual income, marital status and loan amount, are the most important predictive variables. The practical implications of this study are that our scoring models could help banks avoid high default rates, rising bad debts, shrinking cash flows and punitive cost-cutting measures.

Salah satu permasalahan dalam suatu perusahaan provider fixed broadband adalah tingginya bad debt pelanggan khususnya di hunian vertikal atau apartemen. Hal ini tentu saja sangat berpengaruh kepada perusahaan terkait dengan biaya modal dan operasional yang sudah dikeluarkan relative sangat besar, sehingga jaminan ketertagihan layanan diharapkan bisa maksimal. Metode yang dipergunakan dalam hal penanganan bad debt ini berdasarkan dari evaluasi secara internal dan eksternal diantaranya berdasarkan pada data sekunder dari beberapa bagian di perusahaan. Beberapa langkah penanganan untuk mengurangi bad debt dari sisi mitigasi permasalahan, beberapa cara yang sudah dipergunakan, serta kendala yang ditemukan sehingga didapatkan solusi terbaik sebagai antisipasinya. Hasil dari penanganan diantaranya adalah melakukan identifikasi pelanggan berdasarkan kepemilikan unit apartemen, pembuatan produk prabayar, kerjasama penagihan dengan pihak building management apartemen, dan perbaikan dari sisi aplikasi CRM perusahaan.

Kata Kunci: bad debt, apartemen, mitigasi, profil, pelanggan

One of the key requirements for a cooperative to be coordinated and to gain successful feedbacks and good governance is unity among its members. Lack of unity among members can give rise to larger problems and challenges that may be hard for an organization to cope up.