Fraud Risk Analytics (Excel & AI based tools) and Prevention

Detect & Prevent Fraud Smartly

What you’ll learn:
What is fraud
How to detect fraud
How to prevent fraud
What is fraud triangle
What is Benford Law
How to use excel to detect fraud
How AI is helping in detecting fraud
How to assess an organization for its maturity on fraud prevention
How to find anomalies in a dataset

Requirements
None

Description
Fraud is everywhere! History is replete with many high profiles examples like Satyam, Enron and Lehman Brothers that caused billions of dollars of fraud. However, fraud can adversely affect the bottom line of any organization and hence it becomes important to detect and prevent fraud. Though fraud in banking and online transactions generate lot of visibility, fraud occurs in every industry and every process area.

Whatever be your industry or process, focus should always be on prevention since recovering costs from perpetrators of fraud is always a challenging affair.

This program focuses on both these aspects of fraud – detection and prevention and brings in both operations and technology perspectives.

The following are covered in the program:

What is fraud – Characteristics and different types of fraud

How to detect fraud and how to prevent fraud.

Benford law

Box plots to identify outliers

How AI can help detect fraud including a case study on AI in action

Best practices in fraud management

In fraud detection, the program covers tools and techniques that can be deployed both using excel and AI. A walk through of applying the techniques in excel has been provided for better understanding.

A cool framework to assess the maturity of the organization for fraud management is also provided.

This program is facilitated by an industry veteran who has managed fraud detection and prevention in his career. He brings his vast experiences and perspectives into this program.

Who this course is for
Auditors
Risk consultants
Machine learning enthusiasts
Students
Managers and Senior Management involved in fraud risk management