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

When to rebuild or when to adjust scorecards

Data-based scorecards, such as those used in credit scoring, age with time and need to be rebuilt or readjusted. Unlike the huge literature on modelling the replacement and maintenance of equipment there have been hardly any models that deal with this problem for scorecards. This paper identifies an effective way of describing the predictive ability of the scorecard and from this describes a simple model for how its predictive ability will develop. Using a dynamic programming approach one is then able to find when it is optimal to rebuild and when to readjust a scorecard. Failing to readjust or rebuild a scorecard when they aged was one of the defects in credit scoring identified in the investigations into the sub-prime mortgage crisis

Predicting Customer Lifetime Value in Multi-Service Industries

Customer lifetime value (CLV) is a key-metric within CRM. Although, a large number of marketing scientists and practitioners argue in favor of this metric, there are only a few studies that consider the predictive modeling of CLV. In this study we focus on the prediction of CLV in multi-service industries. In these industries customer behavior is rather complex, because customers can purchase more than one service, and these purchases are often not independent from each other. We compare the predictive performance of different models, which vary in complexity and realism. Our results show that for our application simple models assuming constant profits over time have the best predictive performance at the individual customer level. At the customer base level more complicated models have the best performance. At the aggregate level, forecasting errors are rather small, which emphasizes the usability of CLV predictions for customer base valuation purposes. This might especially be interesting for accountants and financial analysts.forecasting;value;customer relationship management;customer lifetime value;customer segmentation;database marketing;interactive marketing

Credit risk modelling using multi-state markov models

A Dissertation as a partial requirement to obtain the degree of Master in Statistics and Information Management, specialization in Risk Analysis and ManagementThis paper is devoted to credit risk modelling issues concerning mortgage commercial loans. Mortgage loans are one of the most popular type of loans provided by credit institutions. Like in the case of other loans, the main concern of institutions providing this type of product is a potential inability to recover the amount assigned to their clients (credit risk). In order to prevent possible losses for credit institutions resulting from clients entering in default, it is therefore crucial to study the behaviour of risky clients. This issue can be addressed through several models, namely through the multi-state Markov model, despite it constituting a more unusual approach in the context of dealing with credit risk modelling. The multi-state Markov model is a useful way of describing a process in which an individual moves through a series of states (finite number) in continuous time. By fitting this model to the loans of risky clients, it is possible to estimate the mean sojourn time in each state before a transition occurs, as well as the transition probabilities between the different states assumed by the contracts, therefore providing a relevant modelling framework for event history data. The present work relies upon 2008-13 databases from one of the biggest American companies that act in the secondary mortgage market, the Fannie Mae. Results show that with the application of the multi-state Markov model, contracts signed during 2013 are more propitious to a scenario of recovery when compared to those referring to the year 2008

Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning

Reinforcement learning has been explored for many problems, from video games with deterministic environments to portfolio and operations management in which scenarios are stochastic; however, there have been few attempts to test these methods in banking problems. In this study, we sought to find and automatize an optimal credit card limit adjustment policy by employing reinforcement learning techniques. In particular, because of the historical data available, we considered two possible actions per customer, namely increasing or maintaining an individual's current credit limit. To find this policy, we first formulated this decision-making question as an optimization problem in which the expected profit was maximized; therefore, we balanced two adversarial goals: maximizing the portfolio's revenue and minimizing the portfolio's provisions. Second, given the particularities of our problem, we used an offline learning strategy to simulate the impact of the action based on historical data from a super-app (i.e., a mobile application that offers various services from goods deliveries to financial products) in Latin America to train our reinforcement learning agent. Our results show that a Double Q-learning agent with optimized hyperparameters can outperform other strategies and generate a non-trivial optimal policy reflecting the complex nature of this decision. Our research not only establishes a conceptual structure for applying reinforcement learning framework to credit limit adjustment, presenting an objective technique to make these decisions primarily based on data-driven methods rather than relying only on expert-driven systems but also provides insights into the effect of alternative data usage for determining these modifications.Comment: 29 pages, 16 figure

Advanced Information Systems and Technologies

This book comprises the proceedings of the VI International Scientific Conference “Advanced Information Systems and Technologies, AIST-2018”. The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing, computer networking and telecomunications, modern methods and information technologies of sustainable development. They will be useful for students, graduate students, researchers who interested in computer science

Acceptance and profitability modelling for consumer loans

This thesis explores and models the relationships between offers of credit products, credit scores, consumers' acceptance decisions and expected profits generated using data that records actual choices made by customers and their monthly account status after being accepted. Based on Keeney and Oliver's theoretical work, this thesis esti¬ mates the expected profits for the lender at the time of application, draws the iso-profit curves and iso-preference curves, derives optimal policy decisions subject to various constraints and compares the economic benefits after the segmentation analysis.This thesis also addresses other research issues that have emerged during the explo¬ ration into profitability and acceptance. We use a Bivariate Sample Selection model to test the existence of sample selection bias and found that acceptance inference may not be necessary for our data. We compared the predictive performance of Support Vector Machines (SVMs) vs. Logistic Regression (LR) on default data as well as on accep¬ tance data, without finding that SVMs outperform LR. We applied different Survival Analysis models on two events of interest, default and paying back early. Our results favoured semi-parametric PH-Cox models separately estimated for each hazard