Top 9 Online Credit Risk Modelling Courses One Must Learn In 2020
Credit risk modelling is the analysis of the credit risk that helps in understanding the uncertainty that a lender runs before lending money to borrowers. In the present scenario, advanced analytics techniques enable organisations to analyse the level of risk for those clients with little to no credit account based on data points. Organisations have started developing robust credit modelling tools with the help of machine learning and deep learning techniques. We list down the top 9 online credit risk modelling courses one must learn in 2020. (Please note that this list is not a ranking and is in no particular order) 1| Credit Risk Modelling With Machine Learning Source: DexLab Analytics About: This course by DexLab Analytics will offer you an opportunity to understand the measure of central tendency theorem, measures of dispersion, probability theory and probability distribution, sampling techniques, estimation theory, types of statistical tests, linear regression, logistic regression. Besides, you will learn the application of machine learning algorithms such as Decision tree, Random Forest, XGBoost, Support Vector Machine, banking products and processes, uses of the scorecard, scorecard model development, use of scorecard for designing business strategies of a bank, LGD, PD, EAD, and much more. Click here to know more. 2| Credit Risk Modelling In Python 2020 Source: Udemy About: In this course, you will learn how to improve modelling skills, credit risk modelling theory, how to evaluate the effectiveness of a model, how to pre-process real-world data in Python, etc. Throughout the course, you will also cover several important data science techniques, including the weight of evidence, information value, area under the curve, Gini coefficient, and other such. Click here to know more. 3| Credit Risk Modelling In Python Source: DataCamp About: In this course, you will learn how to prepare credit application data, how to apply machine learning and business rules to reduce risk and ensure profitability. The course includes exploring and preparing loan data, logistic regression for defaults, gradient boosted trees using XGBoost and model evaluation and implementation. Here, you will learn how to use two datasets that emulate real credit applications while focusing on obtaining business value. Click here to know more. 4| Credit Risk Modelling In Python Source: 365 DataScience About: In this comprehensive credit risk modelling course in Python, you will learn a complete credit risk modelling right from pre-processing, through the probability of default (PD), loss given default (LGD) and exposure at default (EAD) modelling, and finally finishing off with calculating expected loss (EL). Click here to know more. 5| Credit Risk Modelling In Python (U365) Source: Moneyweb CPD HUB About: In this course, you will understand how banks use data science modelling in Python to improve their performance and comply with regulatory requirements. You will get a complete picture in credit risk using state-of-the-art techniques to model all three aspects of the expected loss equation – PD, LGD, and EAD, including creating a scorecard from scratch. Click here to know more. 6| Credit Risk Analysis And Modelling Source: Udemy About: In this course, you will learn different measures of credit risk, the probability density function of credit losses (discussion on Value at Risk), traditional credit models – credit rating and credit scoring such as strengths and weaknesses, parameter specifications such as loss given default, probability of default, etc. This course is ideal for financial analysts, credit rating analysts, private equity analysts, credit analysts, investment bankers, corporate bankers, and business analysts. Click here to know more. 7| Credit Risk Modelling Course Source: EDUCBA About: This credit modelling course is designed for students and professionals who want to master credit modelling skills. You will understand the credit risk and how it is being measured, learn traditional credit models and their examples, structural model of credit risk, Altman Z-score, credit analysis, UFCE and WC modelling and internal ratings in credit modelling. Click here to know more. 8| Credit Risk Modelling Source: Redcliffe Training About: In this course, you will learn the key features of credit risk models, their utilisation in financial institutions and the inherent risks. This course entails proven methods and processes employed by top tier institutions to deploy best in class models to measure, manage and control credit risks. At the end of this course, you will have gained a better understanding of the credit risk models, their utilisation in financial institutions and most importantly, the inherent model risks. Click here to know more. 9| Credit Risk Modelling Source: SAS About: In this course, you will learn how to develop credit risk models in the context of the Basel guidelines. It also provides a sound mix of both theoretical and technical insights, as well as practical implementation details. Here, you will learn how to develop a probability of default (PD), loss given default (LGD), and exposure at default (EAD) models, validate, backtest, and benchmark credit risk models, among others. Click here to know more.
Credit risk modelling is the analysis of the credit risk that helps in understanding the uncertainty that a lender runs before lending money to borrowers. In the present scenario, advanced analytics techniques enable organisations to analyse the level of risk for those clients with little to no credit account based on data points. Organisations have […]
Organisations have started developing robust credit modelling tools with the help of machine learning and deep learning techniques.
["credit risk machine learning"]
["data science", "Go", "machine learning", "AI", "credit risk machine learning", "Python", "deep learning", "XGBoost", "analytics", "GAN", "R"]
["AI", "machine learning", "deep learning", "data science", "analytics", "XGBoost", "Python", "R", "Go", "GAN"]
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.