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

Viterbi optimization for crime detection and identification

In this paper, we introduce two types of hybridization. The first contribution is the hybridization between the Viterbi algorithm and Baum Welch in order to predict crime locations. While the second contribution considers the optimization based on decision tree (DT) in combination with the Viterbi algorithm for criminal identification using Iraq and India crime dataset. This work is based on our previous work [1]. The main goal is to enhance the results of the model in both consuming times and to get a more accurate model. The obtained results proved the achievement of both goals in an efficient way

HMMs for Anomaly Detection in Autonomous Robots

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distribution. We also present a method for o!ine anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our o!ine method to discriminate anomalous behaviors in real-world applications are statistically proved

HMMs for Anomaly Detection in Autonomous Robots

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distri- bution. We also present a method for onine anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our online method to discriminate anomalous behaviors in real-world applications are statistically proved

An intelligent payment card fraud detection system

This is the author accepted manuscript. The final version is available from Springer via the DOI in this recordPayment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems

An Efficient Deep Learning Classification Model for Predicting Credit Card Fraud on Skewed Data

Due to fast-evolving technology, the world is moving to the use of credit cards rather than money in their daily lives, giving rise to many new opportunities for fraudsters to use credit cards maliciously. Based on the Nilson report, losses related to global cards were estimated to be over $35 billion by 2020. In order to maintain the security of users of these cards, the credit card company must develop a service to ensure that users are protected from any risks they may be exposed to. For this reason, we introduce a fraud detection model, denoted ST-BPNN, which is based on machine and deep learning approaches to identify fraudulent transactions. ST-BPNN was applied on real fraud detection data provided by the European bank. Comparing the obtained results from ST-BPNN with a recent state-of-the-art approach shows that our proposed model demonstrates high predictive performance for detecting fraudulent transactions

Reducing Payment-Card Fraud

Critical public data in the United States are vulnerable to theft, creating severe financial and legal implications for payment-card acceptors. When security analysts and managers who work for payment card processing organizations implement strategies to reduce or eliminate payment-card fraud, they protect their organizations, consumers, and the local and national economy. Grounded in Cressey’s fraud theory, the purpose of this qualitative single case study was to explore strategies business owners and card processors use to reduce or eliminate payment-card fraud. The participants were 3 data security analysts and 1 manager working for an international payment card processing organization with 10 years or more experience working with payment card fraud detection in the southeastern United States. The data collection process was face-to-face semistructured interviews and review of company documentation. Within-case analysis, pattern matching, and methodological triangulation were used to identify 4 themes. The key themes related to artificial intelligence, cardholder and acceptor education, enhanced security strategies, and Payment Card Industry Data Security Standard (PCI-DSS) rules and regulations to reduce or end card fraud. The key recommendations are enforcement of stricter PCI-DSS rules and regulations for accepting payment cards at the acceptor and processor levels to reduce the potential for fraud through the use of holograms and card reader clearance between customers. The implications for social change include the potential to reduce costs to consumers, reduce overhead costs for businesses, and provide price reductions for consumers. Additionally, consumers may gain a sense of security when using their payment-card for purchases