Abstract – This study describes the loan products offered by the commercial banks and credit scoring techniques used for classifying risks and granting credit to the applicants in India. The loan products offered by commercial banks are: Housing loans, Personal loans, Business loan, Education loans, Vehicle loans etc. All the loan products are categorized as secures and unsecured loans. Credit scoring techniques used for both secured as well as unsecured loans are broadly divided into two categories as Advanced Statistical Methods and Traditional Statistical Methods
Conclusion: In a new or emerging market, the operational, technical, business and cultural issues should be considered with the implementation of the credit scoring models for retail loan products. The operational issues relate to the use of the model and it is imperative that the staff and the management of the bank understand the purpose of the model. Application scoring models should be used for making credit decisions on new applications and behavioral models for retail loan products to supervise existing borrowers for limit expansion or for marketing of new products. The technical issues relate to the development of proper infrastructure, maintenance of historical data and software needed to build a credit scoring model for retail loan products within the bank. The business issues relate to whether the soundness and safety of the banks could be achieved through the adoption of the quantitative credit decision models, which would send a positive impact in the banking sector. The cultural issues relate to making credit irrespective of race, colour, sex, religion, marital status, age or ethnic origin. Further, the models have to be validated so as to ensure that the model performance is compatible in meeting the business as well as the regulatory requirements. Thus, the above issues have to be considered while developing and implementing credit scoring models for retail loan products within a new or emerging markets.
Em breve teremos alguns posts aqui no blog sobre o assunto, mas é um case de ML com engenharia de caras questão mandando bem com métodos bem avançados com arquiteturas escaláveis.
ENGINEERING EXTREME EVENT FORECASTING AT UBER WITH RECURRENT NEURAL NETWORKS J – BY NIKOLAY LAPTEV, SLAWEK SMYL, & SANTHOSH SHANMUGAM
We ultimately settled on conducting time series modeling based on the Long Short Term Memory (LSTM) architecture, a technique that features end-to-end modeling, ease of incorporating external variables, and automatic feature extraction abilities.4 By providing a large amount of data across numerous dimensions, an LSTM approach can model complex nonlinear feature interactions.
We decided to build a neural network architecture that provides single-model, heterogeneous forecasting through an automatic feature extraction module.6 As Figure 4 demonstrates, the model first primes the network by automatic, ensemble-based feature extraction. After feature vectors are extracted, they are averaged using a standard ensemble technique. The final vector is then concatenated with the input to produce the final forecast.
During testing, we were able to achieve a 14.09 percent symmetric mean absolute percentage error (SMAPE) improvement over the base LSTM architecture and over 25 percent improvement over the classical time series model used in Argos, Uber’s real-time monitoring and root cause-exploration tool.
Abstract: Regional banks as savings and cooperative banks are widespread in continental Europe. In the aftermath of the financial crisis, however, they had problems keeping their profitability which is an important quantitative indicator for the health of a bank and the banking sector overall. We use a large data set of bank-level balance sheet items and regional economic variables to forecast profitability for about 2,000 regional banks. Machine learning algorithms are able to beat traditional estimators as ordinary least squares as well as autoregressive models in forecasting performance.
Conclusion: In the aftermath of the financial crisis regional banks had problems keeping up their profitability. Banks’ profitability is an important indicator for the stability of the banking sector. We use a data set of bank-level balance sheet items and regional economic variables to forecast profitability. For the 2,000 savings and cooperative banks from eight European countries and the 2000-2015 time period, we found that machine learning algorithms are able to beat traditional estimators as ordinary least squares as well as autoregressive models in forecasting performance. Therefore, our paper is in line with the literature on machine learning models and their superior forecasting performance (Khandani et al., 2010; Butaru et al., 2016; Fitzpatrick & Mues, 2016). The performance of the machine learning algorithms was particularly well during the European debt crisis which points out the importance of our forecasting exercise as during this time policy makers’ interest in banks’ profitability was enhanced as further potential rescue packages for banks could deteriorate fiscal stability. Policy makers and, especially, regulators should therefore use these algorithms instead of traditional estimators in combination with their even larger regulatory data sets in regard to size and frequency to forecast banks’ profitability or other balance sheet items of interest.
Improving the Forecasts of European Regional Banks_ Profi tability with Machine Learning Algorithms