Credit Risk Modeling – Probability of Default

This project enables learners to apply various data visualization functions & feature engineering techniques to select appropriate features.

Credit Risk Modelling - Probability of Default- Data Treatment & Feature Selection
  • 1 bar graph
    Difficulty: Beginner

    No prior knowledge or experience with data required.

  • Asset 1
    Duration: Approximately 2 hours

Case Overview

  • Credit risk modeling is a useful model in the banking industry aimed at minimizing the credit risk defaulting cases through the use of various machine learning techniques.
  • The core objective of Banking institutions is to lend loans to individuals & companies that need capital. Despite the fact that it is hard to predict who would default, correctly evaluating and managing credit risk can decrease the severity of a loss.
  • Hence, banks can gain an advantage by using credit risk models by understanding the factors which causes such cases and creating a model to predict any future credit default case to prevent losses.
  • Learn to apply numerous machine learning techniques to create an accurate prediction model for credit defaults.

What’s included

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Lifetime Access

Access this project for life once completed

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Flexible Scheduling

Start learning online immediately, at your own pace

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Desktop Only

We recommend completing this project on a desktop

Skills You Will Learn

Data Management

Data Visualization

Business Analysis

Hypothesis Testing

A/B Testing

Data Mining Techniques

Feature Engineering

Machine Learning

Associated Learning Tasks

Case Context

  • To expand the businesses and increase customer attrition, banks need a continual risk management system for their existing customer base that can determine probability of default beforehand.
  • Machine learning and deep learning advancements have made it significantly easier for businesses and individuals to establish their own strong credit default risk prediction models.
  • The purpose is to identify consumers who are at risk of defaulting within the next six months, for which we built a Risk Score to identify high-risk clients and proactively target them with remedial efforts.

  • Firstly, the data visualization techniques are applied to summarize data and determine correlation among various indicators.
  • Duplicated, missing values and outliers are treated, and data sample is updated.
  • VIF was also used to determine the impact of various indicators on the outcome. It is used to determine variables’ contribution to error range in regression.

  • The case provides a step-by-step process of learning and comprehending the various stages and strategies involved with data exploration, data processing, and feature extraction as part of a model development exercise inside a risk framework.
  • The final data set is used to train several models on the Probability of Default.
  • The potential of identifying certain spending habits can be used to determine the persons’ creditworthiness which is extremely beneficial to banks and loan providers when evaluating applications.

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