Hazai vállalkozások csődjének előrejelzése a csődeseményt megelőző egy, két, illetve három évvel korábbi pénzügyi beszámolók adatai alapján

A cikk azt vizsgálja, hogy milyen besorolási pontossággal jelezhető előre a hazai vállalkozások csődje az azt megelőző egy, két, illetve három évvel korábbi éves beszámolók adatai alapján. A kutatási kérdés megválaszolásához egy, a hazai szakirodalomban még kevésbé elterjedt nem paraméteres módszer: a k legközelebbi szomszéd eljárást alkalmazza a szerző. A tanulmány külön figyelmet szentel a legjobb előrejelző teljesítmény elérését biztosító paraméterek (szomszédok száma, távolságmérték) optimális megválasztására is. A számításokat egy hazai vállalkozásokból álló, ezerelemű véletlen minta adatain végezték el. Nemzetközi kutatási eredmények szerint nagyobb találati arány érhető el, ha a csődmodellek input változói között nemcsak a csőd előtti év adatait használják fel, hanem az azt megelőző 2-3 év pénzügyi mutatóit is. E kérdés vizsgálatát is célul tűzi ki a tanulmány

Efficient Multi-Classifier Wrapper Feature-Selection Model: Application for Dimension Reduction in Credit Scoring

The task of identifying most relevant features for a credit scoring application is a challenging task. Reducing the number of redundant and unwanted features is an inevitable task to improve the performance of the credit scoring model. The wrappers approach is usually used in credit scoring applications to identify the most relevant features. However, this approach suffers from the issue of subsets generation and the use of a single classifier as an evaluation function. The problem here is that each classifier may give different results which can be interpreted differently. Hence, we propose in this study an ensemble wrapper feature selection model which is based on a multi-classifiers combination. In a first stage, we address the problem of subsets generation by minimizing the search space through a customized heuristic. Then, a multi-classifier wrapper evaluation is applied using two classifier arrangement approaches in order to select a set of mutually approved set of relevant features. The proposed method is evaluated on four credit datasets and has shown a good performance compared to individual classifiers results

Optimization of the system of allocation of overdue loans in a sub-saharan Africa microfinance institution

In microfinance, with more loans, there is a high risk of increasing overdue loans by overloading the resources available to take actions on the repayment. So, three experiments were conducted to search for a distribution of the loans through the officers available to maximize the probability of recovery. Firstly, the relation between the loan and some characteristics of the officers was analyzed. The results were not that strong with F1 scores between 0 and 0.74, with a lot of variation in the scores of the good predictions. Secondly, the loan is classified as paid/unpaid based on what prediction could result of the analysis of the characteristics of the loan. The Support Vector Machine had potential to be a solution with a F1 score average of 0.625; however, when predicting the unpaid loans, it showed to be random with a score of 0.55. Finally, the experiment focused on segmentation of the overdue loans in different groups, from where it would be possible to know their prioritization. The visualization of three clusters in the data was clear through Principal Component Analysis. To reinforce this good visualization, the final silhouette score was 0.194, which reflects that is a model that can be trusted. This way, an implementation of clustering loans into three groups, and a respective prioritization scale would be the best strategy to organize and assign the loans to maximize recovery

Loan products and credit scoring methods by commercial banks

This study describes the loan products offered by the commercial banks and credit scoring techniques used for classifying risks and granting credit to the applicants in India. The loan products offered by commercial banks are: Housing loans, Personal loans, Business loan, Education loans, Vehicle loans etc. All the loan products are categorized as secures and unsecured loans. Credit scoring techniques used for both secured as well as unsecured loans are broadly divided into two categories as Advanced Statistical Methods and Traditional Statistical Methods.peer-reviewe

Using Ensembles of Machine Learning Techniques to Predict Reference Evapotranspiration (ET0) Using Limited Meteorological Data

To maximize crop production, reference evapotranspiration (ET0) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET0 and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological parameters, needed for ET0 estimation, are difficult to obtain in developing countries. Therefore, alternative ways to estimate ET0 using fewer climatic data are of critical importance. To estimate ET0 with alternative methods, difference climatic parameters of temperatures, relative humidity (maximum and minimum), sunshine hours, and wind speed for a period of 20 years from 1996 to 2015 were used in the study. The data were recorded by 11 meteorological observatories situated in various climatic regions of Pakistan. The significance of the climatic parameters used was evaluated using sensitivity analysis. The machine learning techniques of single decision tree (SDT), tree boost (TB) and decision tree forest (DTF) were used to perform sensitivity analysis. The outcomes indicated that DTF-based models estimated ET0 with higher accuracy and fewer climatic variables as compared to other ML techniques used in the study. The DTF technique, with Model 15 as input, outperformed other techniques for the most part of the performance metrics (i.e., NSE = 0.93, R-2 = 0.96 and RMSE = 0.48 mm/month). The results indicated that the DTF with fewer climatic variables of mean relative humidity, wind speed and minimum temperature could estimate ET0 accurately and outperformed other ML techniques. Additionally, a non-linear ensemble (NLE) of ML techniques was further used to estimate ET0 using the best input combination (i.e., Model 15). It was seen that the applied non-linear ensemble (NLE) approach enhanced modelling accuracy as compared to a stand-alone application of ML techniques (R-2 Multan = 0.97, R2 Skardu = 0.99, R-2 ISB = 0.98, R2 Bahawalpur = 0.98 etc.). The study results affirmed the use of an ensemble model for ET0 estimation and suggest applying it in other parts of the world to validate model performance

Prediction of personal default risks based on a sparrow search algorithm with support vector machine model

Aiming at the personal credit evaluation of commercial banks, this paper constructs a classified prediction model based on machine learning methods to predict the default risk. At the same time, this paper proposes to combine the sparrow search algorithm (SSA) with the support vector machine (SVM) to explore the application of the SSA-SVM model in personal default risk prediction. Therefore, this paper takes the personal credit data as the original data, carries out statistical analysis, normalization and principal factor analysis, and substitutes the obtained variables as independent variables into the SSA-SVM model. Under the premise of the same model, the experimental results show that the evaluation indexes of the experimental data are better than the original data, which shows that it is effective for the data processing operation of the original data in this paper. On the premise of the same data, each evaluation index of the SSA-SVM model is better than the SVM model, which shows that the hybridized model established in this paper is better than the latter one in predicting personal default risk, and has certain practical value