Análise do risco de inadimplência na utilização de cartões de crédito

ABSTRACT. This paper analyzes the risk of default in the use of credit cards generating probabilities of delay in payment with different variables such as age, gender, credit limit and annual income. The behavior of debtors who use credit cards is studied identifying changes in states of delay of risk levels. A multi-state model of Markov was used to perform the analysis. The study was applied to credit card usage records of individuals in 121 commercial and financial institutions. This research identifies the patterns of use by credit card customers and provides valuable inputs to help financial institutions understand the phenomenon of default risk.RESUMO. Este trabalho analisa o risco de inadimplência na utilização de cartões de crédito gerando probabilidades de atraso no pagamento com diferentes variáveis tais como idade, sexo, limite de crédito e rendimento anual. O comportamento dos devedores que utilizam cartões de crédito é estudado identificando alterações nos estados de atraso dos níveis de risco. Foi utilizado um modelo multiestado de Markov para realizar a análise. O estudo foi aplicado aos registos de utilização de cartões de crédito de indivíduos em 121 instituições comerciais e financeiras. Este estudo identifica os padrões de utilização pelos clientes de cartões de crédito e fornece dados valiosos para ajudar as instituições financeiras a compreender o fenómeno do risco de inadimplência

A big data analytics method for assessing creditworthiness of SMEs:Fuzzy equifinality relationships analysis

Nowadays, many financial institutions are beginning to use Big Data Analytics (BDA) to help them make better credit underwriting decisions, especially for small and medium-sized enterprises (SMEs) with limited financial histories and other information. The various complexities and the equifinality problem of Big Data make it difficult to apply traditionalstatistical techniques to creditworthiness evaluation, or credit scoring. In this study, we extend the existing research in the field of creditworthiness assessment and propose a novel approach based on neighborhood rough sets (NRSs), to evaluate and investigate the complexities and fuzzy equifinality relationships in the presence of Big Data. We utilize a real SME loan dataset from a Chinese commercial bank to generate interval number rules that provide insight into the fuzzy equifinality relationships between borrowers’ demographic information, company financial ratios, loan characteristics, other non-financial information, local macroeconomic indicators and rated creditworthiness level. In addition, the interval number rules are used to predict creditworthiness levels based on test data and the accuracy of the prediction is found to be 75.44%. One of the major advantages of using the proposed BDA approach is that it helps us to reduce complexity and identify equivalence relationships when using Big Data to assess the creditworthiness of SMEs. This study also provides important implications for practices in financial institutions and SMEs

Time matters: How default resolution times impact final loss rates

Using access to a unique bank loss database, we find positive dependencies of default resolution times (DRTs) of defaulted bank loan contracts and final loan loss rates (losses given default, LGDs). Due to this interconnection, LGD predictions made at the time of default and during resolution are subject to censoring. Pure (standard) LGD models are not able to capture effects of censoring. Accordingly, their LGD predictions may be biased and underestimate loss rates of defaulted loans. In this paper, we develop a Bayesian hierarchical modelling framework for DRTs and LGDs. In comparison to previous approaches, we derive final DRT estimates for loans in default which enables consistent LGD predictions conditional on the time in default. Furthermore, adequate unconditional LGD predictions can be derived. The proposed method is applicable to duration processes in general where the final outcomes depend on the duration of the process and are affected by censoring. By this means, we avoid bias of parameter estimates to ensure adequate predictions

Bank loans recovery rate in commercial banks: A case study of non-financial corporations

Empirijska literatura o kreditnom riziku uglavnom se temelji na modeliranju vjerojatnosti neispunjavanja obveza, izostavljajući modeliranje gubitka uz zadani rizik. Ovaj rad ima za cilj predvidjeti stope povrata bankovnih kredita uz primjenu rijetko korištene ne-parametarske metode Bayesovog modela usrednjavanja i kvantilne regresije, razvijene na temelju individualnih bonitetnih mjesečnih panel podataka u razdoblju 2007.-2018. Modeli su kreirani na temelju financijskih i bihevioralnih podataka koji prikazuju povijest kreditnog odnosa poduzeća s financijskim institucijama. U radu su prikazana dva pristupa: Točka u vremenu (Point in Time- PIT) i Promatranje cijelog ciklusa (Through-the-Cycle – TTC).Usporedba kvantilne regresije koja daje sveobuhvatan pogled na cjelokupnu razdiobu gubitaka s alternativama otkriva prednosti pri procjeni pada i očekivanih kreditnih gubitaka. Ispravna procjena LGD parametra utječe na odgovarajuće iznose zadržanih rezervi, što je ključno za ispravno funkcioniranje banke da se ne izlaže riziku insolventnosti ukoliko dođe do takvih gubitaka.The empirical literature on credit risk is mainly based on modelling the probability of default, omitting the modelling of the loss given default. This paper is aimed to predict recovery rates on the rarely applied nonparametric method of Bayesian Model Averaging and Quantile Regression, developed on the basis of individual prudential monthly panel data in the 2007–2018. The models were created on financial and behavioural data that present the history of the credit relationship of the enterprise with financial institutions. Two approaches are presented in the paper: Point in Time (PIT) and Through-the-Cycle (TTC). A comparison of the Quantile Regression which get a comprehensive view on the entire probability distribution of losses with alternatives reveals advantages when evaluating downturn and expected credit losses. A correct estimation of LGD parameter affects the appropriate amounts of held reserves, which is crucial for the proper functioning of the bank and not exposing itself to the risk of insolvency if such losses occur