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Fraud Analytics in Banking and Credit using Machine Learning

Learners: 6,779

Instructor: Exam Turf

Duration:

This course provides a comprehensive overview of fraud analytics in banking and credit using machine learning. It covers topics such as fraud detection, risk analysis, rank functions, RHS constraints, VRS, CRS efficiency, loan status grades, beta value, predict value, performance values, and logistic regression algorithms. Through case studies and hands-on projects, you will gain a deep understanding of the robust internal controls and risk management systems in organizations. With this course, you will be able to detect frauds and develop effective fraud detection solutions through data science.▼▲

Course Feature Course Overview Course Provider

Go to class

Course Feature

Cost:

Paid

Provider:

Udemy

Certificate:

Paid Certification

Language:

English

Start Date:

2021-08-24

Course Overview

❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [July 25th, 2023]

This course provides an overview of fraud analytics in banking and credit using machine learning. It covers the process of analyzing illegitimate transactions and developing effective fraud detection solutions through data science. Students and professionals will gain an understanding of the robust internal controls and risk management systems in organizations. The course will guide participants through the process of understanding the concept of fraud detection in credit payments using a case study. Algorithms such as Kmeans and hierarchical clustering will be used to understand the data, as well as other visualization techniques and methods to compare and understand the flow of data. Additionally, logistic regression algorithms will be implemented in a project. Topics such as loan status grades, beta value, predict value, performance values, cust ranking, risk analysis, rank functions, RHS constraints, VRS, and CRS efficiency will be discussed.