Credit Ratings play a central role in Credit Risk Management for Financial Institutions. Credit Risk Managers are interested in calculating Risk Spreads, in order to balance the expected losses on a portfolio of customers or credit operations, what is much easier to do using Credit Ratings. In this paper, some alternative approaches are proposed to obtain Credit Ratings, derived from Statistical Theory and Neural Networks Architectures, instead of the usual Linear Logistic Regression Model: Additive Logistic Regression, Multi Layer Perceptron Neural Networks and Bayesian Networks. A comparison of the methods is presented and some recommendations are indicated.
Nowadays, time series data underlies countless research activities. Despite the wide range of techniques to capture and process all this information, issues such as analyzing large amounts of data and detecting unusual behaviors on them still pose a great challenge. In this context, this paper suggests SHESD+, a statistical technique that combines the Extreme Studentized Deviate (ESD) test and a decomposition procedure based on Loess to detect anomalies on time series data. The proposed technique employs robust metrics to identify anomalies in a more proper and accurate manner, even in the presence of trend and seasonal spikes. Simulation studies are carried out to evaluate the effectiveness of the SH-ESD+ using the published Numenta Anomaly Benchmark (NAB) collection. Computational results show that the SH-ESD+ performs consistently when compared against state-of-the-art and classic detection techniques.
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