Assessment of model risk due to the use of an inappropriate parameter estimator

The purpose of this study is to assess model risk with respect to parameter estimation for a simple binary logistic regression model applied as a predictive model. The assessment is done by comparing the effectiveness of eleven different parameter estimation methods. The results from the historical credit dataset of a certain financial institution confirmed that using several optimization methods to address parameter estimation risk for predictive models is substantial. This is the case, especially when there exists a numerical optimization method that estimates the optimum parameters and minimizes the cost function among alternative methods. Our study only considers a univariate predictor with a static sample size of cases. This research work contributes to the literature by presenting different parameter estimation methods for predicting the probability of default through binary logistic regression model and determining optimum parameters that minimize the objective model's cost function. The Mini-Batch Gradient Descent method is revealed to be the better parameter estimator

Credit risk prediction with and without weights of evidence using quantitative learning models

The credit risk assessment process is necessary for maintaining financial stability, cost and time efficiency, model performance accuracy, comparability analysis and future business implications in the commercial banking sector. By accurately predicting credit risk, highly regulated banks can make informed lending decisions and minimize potential financial losses. The purpose of this paper is to assess the power of conventional predictive statistical models with and without transforming the features to gain better insights into customer’s creditworthiness. The findings of the predicted performance of the logistics regression model are compared to the performance results of machine learning models for credit risk assessment using commercial banking credit registry data. Each model has its strengths and weaknesses, and where one model lacks, another performs better. The article reveals that simpler credit risk assessment techniques delivered outstanding performance while consuming less processing power and have given insights into the most contributing feature categories. Improving a conventional predictive statistical model using some of the feature transformations reduces the overall model performance, specifically for credit registry data. The logistics regression model outperformed all models with the highest F1, accuracy, Jaccard Index and AUC values, respectively. Financial institutions, specifically banks have questioned whether transformations using Weights of Evidence (WoE) have been significant in quantifying the relationship between categorical independent variables for various types of credit data. This study provides insights when considering the usage of feature transformation for credit risk modelling in commercial banking. The transformation technique is particularly useful in situations where statistical predictive modelling techniques are employed. The results revealed that not only can the logistic regression models perform similarly to the machine learning models but can also outperform them. The best performance is attributed to the simplicity, interpretability, and access to understanding features of individual clients within a portfolio of credit products. The logistic regression model without transformation turned out to perform the best out of the five machine learning models. Considering the business impact, enhancing the logistic regression model by using a WoE transformation did not improve the model's performance for commercial banking data considered. However, the transformation did provide insights regarding each binned categorical independent variable. Therefore, our findings in this article contribute towards assisting banks in managing the impact and interpretability of each binned feature category on the discriminatory power of credit scoring.</p

A review on residential exposure to electromagnetic fields from overhead power lines : electrification as a health burden in rural communities

Abstract: Electrification has improved millions of lives over the years. With the benefit of electricity comes the emission of electromagnetic fields (EMFs) from power lines, substations, electrical home appliances and railways. There have been studies done to associate exposure to EMFs with the development of health effects such as cancer and transient biological effects. The aim of this paper is to provide discourse on the association between EMFs and cancer, not excluding other severe health effects such as cognitive impairment and preterm labor in pregnant women. In this paper, google scholar, science direct and PubMed were used to search for literature. Out of thirty articles, fifteen were selected and used to compile this paper. These articles revealed that studies in the past have found conflicting results from research conducted globally. Eight articles out of the fifteen found a link between exposure to EMFs and leukemia as well as impaired neurobehavioral function in children. Six articles found a statistically insignificant association, with one article being inconclusive. World Health Organization (WHO) found a link between childhood Leukemia and EMFs, bringing it into the attention for more research to be done to confirm this association. Based on the evidence, epidemiological studies need to be done and address the data scarcity relating to EMFs from power lines; especially in the South African context