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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.
Course Syllabus
Banking and Credit Fraud Analytics with ML
Fraud Detection in Credit Payments with ML
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