Transformation of the Forecast Assessment of Expected Credit Losses in Monitoring and Assessment of Credit Risk in Commercial Banks

The article presents the results of the systematization of issues arising in connection with the transformation of the banks forecast assessment of expected credit losses during the monitoring and evaluation of credit risk in commercial banks. Based on the data obtained on the introduction of IFRS 9 "Financial instruments" into the banking sector, it is concluded that in banking practice there is uncertainty regarding the long-term impact of credit risk, and there are significant difficulties with the use of a large amount of additional information, which creates certain difficulties in calculating future credit losses of banks. It is noted that the current use of the model of predictive assessment of expected credit losses of customers in the monitoring and evaluation of credit risk in the bank should take into account the selected collective or individual basis of assessment. The article presents a comprehensive approach to the use of the impairment model of expected losses in banking as a basic tool for modeling expected credit losses in order to form provisions for impairment with the allocation. The modification of this model will depend on the specifics of the bank's credit activities and portfolio, the types of its financial instruments, the sources of available information, as well as the IT systems used. Validation of this model will reduce the expected credit losses, reduce the amount of estimated reserves, as well as improve the efficiency of the Bank as a whole

A Framework for Credit Risk Prediction Using the Optimized-FKSVR Machine Learning Classifier

Transparency is influenced by several crucial factors, such as credit risk (CR) predictions, model reliability, efficient loan processing, etc. The emergence of machine learning (ML) techniques provides a promising solution to address these challenges. However, it is the responsibility of banking or nonbanking organizations to control their approach to incorporate this innovative methodology to mitigate human preferences in loan decision-making. The research article presents the Optimized-Feature based Kernel Support Vector Regression (O-FKSVR) model which is an ML-based CR analysis model in the digital banking. This proposal aims to compare several ML methods to identify a precise model for CR assessment using real credit database information. The goal is to introduce a classification model that uses a hybrid of Stochastic Gradient Descent (SGD) and firefly optimization (FFO) methods with Support Vector Regression (SVR) to predict credit risks in the form of probability, loss given, and exposure at defaults. The proposed  O-FKSVR model extracts features and predicts outcomes based on data gathered from online credit analysis. The proposed O-FKSVR model has increased the accuracy rate and resolved the existing problems. The experimental study is conducted in Python, and the results demonstrate improvements in accuracy, precision, and reduced error rates compared to previous ML methods. The proposed O-FKSVR model has achieved a maximum accuracy rate value of 0.955%, precision value of 0.96%, and recall value of 0.952%, error rate value of 4.4 when compared with the existing models such as SVR, DT, RF, and AdaBoost.&nbsp

On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility

[Abstract]: In classical survival analysis, it is assumed that all the individuals will experience the event of interest. However, if there is a proportion of subjects who will never experience the event, then a standard survival approach is not appropriate, and cure models should be considered instead. This paper deals with the problem of adapting a machine learning approach for classical survival analysis to a situation when cure (i.e., not suffering the event) is a possibility. Specifically, a brief review of cure models and recent machine learning methodologies is presented, and an adaptation of machine learning approaches to account for cured individuals is introduced. In order to validate the proposed methods, we present an extensive simulation study in which we compare the performance of the adapted machine learning algorithms with existing cure models. The results show the good behavior of the semiparametric or the nonparametric approaches, depending on the simulated scenario. The practical utility of the methodology is showcased through two real-world dataset illustrations. In the first one, the results show the gain of using the nonparametric mixture cure model approach. In the second example, the results show the poor performance of some machine learning methods for small sample sizes.This project was funded by the Xunta de Galicia (Axencia Galega de Innovación) Research projects COVID-19 presented in ISCIII IN845D 2020/26, Operational Program FEDER Galicia 2014–2020; by the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union European Regional Development Fund (ERDF)-Galicia 2014–2020 Program, by grant ED431G 2019/01; and by the Spanish Ministerio de Economía y Competitividad (research projects PID2019-109238GB-C22 and PID2021-128045OA-I00). ALC was sponsored by the BEATRIZ GALINDO JUNIOR Spanish Grant from MICINN (Ministerio de Ciencia e Innovación) with code BGP18/00154. ALC was partially supported by the MICINN Grant PID2020-113578RB-I00 and partial support of Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C-2020-14Xunta de Galicia; IN845D 2020/2

Diseño de una estrategia para la gestión de cobranza, a través de Big Data Analytics en empresas de venta por catálogo

Este trabajo del MBA presenta un diseño de la estrategia de gestión de cobranza a través de Big Data Analytics, en empresas de Venta por Catálogo. La metodología empleada para lograr el objetivo del trabajo consiste en una revisión sistemática de literatura y un análisis cualitativo, con el fin de identificar, describir, profundizar y finalmente divulgar la estrategia. Es así como el método de investigación se empleó de la siguiente forma: (1) Planear el protocolo de revisión; (2) Identificación y clasificación de literatura orientada al objeto de estudio; (3) Descripción de literatura de la evolución del objeto de estudio, y (4) finalmente, la entrega de resultados. Como resultado se diseña la estrategia de gestión de cobranza a través de Big Data Analytics en empresas de Venta por Catálogo, en tres categorías enmarcadas en el ciclo de vigencia del crédito: (1) Otorgamiento del crédito, (2) Seguimiento al Comportamiento del uso del crédito y (3) Recuperación del crédito. Asimismo, el trabajo es un buen ejemplo de cómo emplear estas estrategias en empresas orientadas al desarrollo del canal comercial, lo cual asegura crecimiento, pero al mismo tiempo protege la estructura financiera de la empresa, pues permite segmentar los perfiles de los clientes, y genera estrategias customizadas de acuerdo con el riesgo, de tal forma que minimice la probabilidad de pérdida de la empresa. (la probabilidad de pérdidas que podría tener la empresa).The present study of MBA seeks to design a collection management strategy using Big Data Analytics implemented in Direct Selling Companies. The methodology used to achieve the main objective is based on a systematic literature review and a qualitative analysis, in order to identify, describe, deepen and communicate the final strategy. The research method was applied in four core activities: (1) Planning the review protocol ; (2) Identification and classification of literature approach to the object of study; (3) Literature description of the evolution of the study and finally (4) Results. As a result, the strategy design model applied in Collections management through Big Data Analytics in Direct Selling companies, the model design was divided into three categories focused on the credit cycle (1) Credit Granting, (2) Credit Use Behavior and (3) Credit Recovery. Likewise, this study is a good example of how to use these strategies in companies oriented to the development of the commercial channel, which ensure growth, but at the same time protect the financial structure of the company, by segmenting customer profiles, generating personalized strategies according to the risk, thus minimizing the possibility of loss of the company.Magíster en Administración MBAMaestrí