Porównanie wykorzystania sieci neuronowych i analizy dyskryminacyjnej w ocenie niewypłacalności

The paper investigates the use of different structure of NN and DA in the process of establishing the possibility of default. The results of those different methods are juxtaposed and their performance is compared.W artykule opisano wykorzystanie i użyteczność różnych typów sieci neuronowych i modeli analizy dyskryminacyjnej w procesie określania potencjalnej niewypłacalności dłużnika. Następnie wyniki poszczególnych metod, uzyskane na podstawie danych finansowych przedsiębiorstw pochodzących z różnych sektorów gospodarki, zostały porównane i na tej podstawie określono przydatność badanych metod w procesie oceny ryzyka kredytowego

Dinamikus pénzügyi mutatószámok alkalmazása a csődelőrejelzésben = Application of dynamic financial variables in bankruptcy prediction

A csődelőrejelző modellek a vállalkozások jövőbeli fizetőképességét próbálják előrejelezni objektív információk alapján statisztikai (adatbányászati) módszerek felhasználásával. E modellek jellemzően a vállalatok pénzügyi kimutatásaiból (mérleg, eredménykimutatás) számítható hányados típusú pénzügyi mutatószámokat használják magyarázó változóként. A tudományterület kutatása közel 50 éves múltra tekint vissza. Ennek ellenére számos nyitott kutatási kérdés található a szakirodalomban, melynek köszönhetően folyamatos az érdeklődés a témakör iránt. E kérdések közül az egyik legrégebben ismert probléma a csődelőrejelző modellek statikus jellegéből adódik. Ennek lényege, hogy a modellek magyarázó változói közt csak legaktuálisabb adatokat használják fel és figyelmen kívül hagyják a pénzügyi mutatószámok időbeli trendjéből kinyerhető információkat. A probléma megoldására főként bonyolultabb módszertani megoldások születtek, melyek – vélhetően komplexitásuk miatt – nem terjedtek el általánosan az ígéretes eredmények ellenére sem. Értekezésemben arra törekedtem, hogy a modellek dinamizálását bonyolultabb módszertani megoldások nélkül valósítsam meg. Erre a célra egy olyan mutatószámot javasoltam, amely lehetővé teszi a pénzügyi mutatók időbeli tendenciájának figyelembe vételét a szakirodalomban és a gyakorlati modellezésben általánosan elterjedt „hagyományos” módszerek keretei közt. E mutatószám azt tükrözi, hogyan viszonyul egy érintett vállalkozás legaktuálisabb pénzügyi mutatója az adott vállalkozás azonos mutatójának korábbi években megfigyelt értékeihez

Two Stage Comparison of Classifier Performances for Highly Imbalanced Datasets

During the process of knowledge discovery in data, imbalanced learning data often emerges and presents a significant challenge for data mining methods. In this paper, we investigate the influence of class imbalanced data on the classification results of artificial intelligence methods, i.e. neural networks and support vector machine, and on the classification results of classical classification methods represented by RIPPER and the Naïve Bayes classifier. All experiments are conducted on 30 different imbalanced datasets obtained from KEEL (Knowledge Extraction based on Evolutionary Learning) repository. With the purpose of measuring the quality of classification, the accuracy and the area under ROC curve (AUC) measures are used. The results of the research indicate that the neural network and support vector machine show improvement of the AUC measure when applied to balanced data, but at the same time, they show the deterioration of results from the aspect of classification accuracy. RIPPER results are also similar, but the changes are of a smaller magnitude, while the results of the Naïve Bayes classifier show overall deterioration of results on balanced distributions. The number of instances in the presented highly imbalanced datasets has significant additional impact on the classification performances of the SVM classifier. The results have shown the potential of the SVM classifier for the ensemble creation on imbalanced datasets

Credit Risk Evaluation as a Service (CREaaS) based on ANN and Machine Learning

Credit risk evaluation is the major concern of the banks and financial institutions since there is a huge competition between them to find the minimum risk and maximum amount of credits supplied. Comparing with the other services of the banks like credit cards, value added financial services, account management and money transfers, the majority of their capitals has been used for various types of credits. Even there is a competition among them for finding and serving the low risk customers, these institution shares limited information about the risk and risk related information for the common usage. The purpose of this paper is to explain the service oriented architecture and the decision model for those banks which shares the information about their customers and makes potential customer analysis. Credit Risk Evaluation as a Service system, provides a novel service based information retrieval system submitted by the banks and institutions. The system itself has a sustainable, supervised learning with continuous improvement with the new data submitted. As a main concern of conflict of interest between the institutions trade and privacy information secured for internal usage and full encrypted data gathering and as well as storing architecture with encryption. Proposed system architecture and model is designed mainly for the commercial credits for SME’s due to the complexity and variety of other credits

Comparison of data mining techniques to predict and map the Atterberg limits in central plateau of Iran

The Atterberg limits display soil mechanical behavior and, therefore, can be so important for topics related to soil management. The aim of the research was to investigate the spatial variability of the Atterberg limits using three most common digital soil-mapping techniques, the pool of easy-to-obtain environmental variables and 85 soil samples in central Iran. The results showed that the maximum amount of liquid limit (LL) and plastic limit (PL) were obtained in the central, eastern and southeastern parts of the study area where the soil textural classes were loam and clay loam. The minimum amount of LL and PL were related to the northwestern parts of the study area, adjacent to the mountain regions, where the samples had high levels of sand content (>80%). The ranges of plasticity index (PI) in the study area were obtained between 0.01 to 4%. According to the leave-in-out cross-validation method, it should be highlighted the combination of artifiial bee colony algorithm (ABC) and artifiial neural network (ANN) techniques were the best model to predict the Atterberg limits in the study area, compared to the support vector machine and regression tree model. For instance, ABC-ANN could predict PI with RMSE, R2 and ME of 0.23, 0.91 and -0.03, respectively. Our fiding generally indicated that the proposed method can explain the most of variations of the Atterberg limits in the study area, and it could berecommended, therefore, as an indirect approach to assess soil mechanical properties in the arid regions, where the soil survey/sampling is difficult to undertake

Magyar vállalkozások felszámolásának előrejelzése pénzügyi mutatóik idősorai alapján

A vállalatok felszámolásának előrejelzésében általános gyakorlat a számviteli adatokból kapott hányados típusú pénzügyi mutatók használata. E mutatókat általában csak az utolsó lezárt üzleti év adatai alapján kalkulálják. Az így felépített modellek azonban statikus jellegűek, s nem veszik figyelembe a vállalati gazdálkodás folyamatjellegét. E hiányosság kiküszöbölésére korábban Nyitrai [2014a] tett kísérletet a statikus pénzügyi mutatószámok idősoraiból képzett, úgynevezett dinamikus pénzügyi mutatók használatával – azonban számos, önkényesnek tűnő feltételezéssel élt, amelyek közül tanulmányunkban kettőt feloldunk. Az idézett cikk csak döntési fák segítségével vizsgálta a pénzügyi mutatók időbeli trendjét kifejező változó hatékonyságát. Most e megközelítés hatását a modellek előrejelző képességére a – szakirodalomban általánosan elterjedt – logisztikus regresszió keretei között vizsgáljuk meg. Nyitrai [2014a] a pénzügyi mutatók teljes idősorait felhasználta, ennek szükségessége kérdéses lehet, ezért megnézzük a csődmodellek előrejelző képességét annak függvényében, hogy hány évre visszamenően vesszük figyelembe a pénzügyi mutatók értékeit. Journal of Economic Literature (JEL) kód: C52, C53, G33

Designing an Expert System for Credit Rating of Real Customers of Banks Using Fuzzy Neural Networks

Currently, in Iran's banking system, non-repayment of facilities has become one of the biggest issues, and due to the lack of a proper system for proper allocation of facilities, they face a number of problems, including the problem of allocation of loans, the problem of failure to repay loans Of the central bank, or the amount of facilities increased from the amount of reimbursement. The solution of this problem is the credit rating of the customers, which is based on a model based on the theory of fuzzy sets for validation of real customers of the Maskan bank of the East Azer-baijan in Iran in 2016. In this research a structured model was obtained for deter-mination and categorization of input variables for application in the system by factorial analysis then a expert fuzzy system was modelled that consist of six steps. In the first step a fuzzy system is designed that its inputs are financial capacity, support, reliability, repayment record and its outputs is customer credit. In the second step input and outputs are partitioned, in the third step thee partitioned inputs and outputs are converted into fuzzy numbers. The fuzzy inference is compiled in step four. In step five the defuzzifier is conducted. Finally the designed model is tested in step six. These results indicate research model efficiency compared to bank credit measuring experts that they predicate applicants performance according their judgment and intuition

Collection Score e as Oportunidades no Mercado de Non-Performing Loans

In the academic literature, credit scoring models are widely studied, while collection scoring models are less explored; likewise, there are few articles dealing with the Brazilian non-performing-loans market. This work has as main contributions: the use of scoring models in the area of collection and working with non-performing-loans data. The objective of this paper is to develop a collection scoring model through Logistic Regression to identify, in a portfolio of clients with non-performing-loans, to verify if it is possible to adjust a good model and to indicate which clients are more likely to pay the debts nonperforming credits The results show that the model worked well for the database, obtaining an excellent fit (accuracy of classification greater than 83% for the two samples and KS=68), pointing the viability of this methodology.Na literatura acadêmica, modelos aplicados à área de crédito (chamados de credit scoring) são largamente explorados, ao passo que modelos aplicados à cobrança (chamados de collection scoring) são pouco abordados; da mesma maneira existem poucos artigos que tratam o mercado brasileiro de empréstimos bancários não pagos ou mais comumente chamados de non-performing-loans. Este trabalho traz como principais contribuições: a utilização de modelos de scoring na área de Cobrança, e trabalhar com dados non-performing-loans. O objetivo deste trabalho é, desenvolver um modelo de collection scoring por intermédio de Regressão Logística para identificar, em uma carteira de clientes com “créditos podres”, para verificar a possibilidade ajustar um bom modelo com altas taxas de acerto e apontar quais clientes têm maior propensão de pagar os créditos não performados. Os resultados mostram que o modelo funcionou bem para o público testado, obtendo um excelente ajuste (taxa de acerto superior a 83% nas amostras de desenvolvimento e de validação; KS de 68), apontando a viabilidade de sua aplicação