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所在平台: Udemy |
课程主页: https://www.udemy.com/course/credit-risk-modeling-using-r-programming/
课程评论：没有评论
课程名称：使用R编程进行信用风险建模 课程概述：每当金融机构发放贷款时，都会面临信用风险。这是指经济损失的风险，当借款人未能履行合同条款时，金融机构可能面临这种风险。评估和管理信用风险，以及制定和实施降低借款人违约风险的策略，成为任何风险管理活动的核心。金融机构利用大量关于借款人和贷款的数据，应用预测和统计模型，帮助银行量化、汇总和管理跨地域和产品线的信用风险。本课程的目标是利用真实数据集逐步学习如何从头开始构建这些信用风险模型。 课程分为两个部分：1）开发信用风险评分卡；2）开发违约概率（PD）模型。我们将构建一个预测模型，该模型以贷款申请人的各个方面作为输入，并输出该贷款申请人违约的概率。根据巴塞尔指南中采用的内部评级基础方法，PD也是计算信用风险的主要参数。在本课程中，我们将执行模型构建所涉及的所有步骤，并在此过程中了解整个预测建模的全景。
Every time an institution extends a loan, it faces credit risk. It is the risk of economic loss that every financial institution faces when an obligor does not fulfill the terms and conditions of his contracts. Measuring and managing the credit risk and developing, implementing strategies to help lowering the risk of defaults by borrowers becomes the core of any risk management activities.Financial institutions make use of vast amounts of data on borrowers and loans and apply these predictive and statistical models to aid banks in quantifying, aggregating and managing credit risk across geographies and product lines. In this course, our objective is to learn how to build these credit risk models step by step from scratch using a real life dataset.The course comprises of two sections: 1) Developing a credit risk scorecard and 2) Developing a Probability of Default (PD) model. We will build a predictive model that takes as input the various aspects of the loan applicant and outputs the probability of default of the loan applicant. PD is also the primary parameter used in calculating credit risk as per the internal ratings-based approach (under Basel guidelines) used by banks.In this course, we will perform all the steps involved in model building and along the way, we will also understand the entire spectrum of the predictive modeling landscape.
