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

Fraud Risk Analytics (Excel & AI based tools) and Prevention, available at $64.99, has an average rating of 4.25, with 67 lectures, 1 quizzes, based on 1176 reviews, and has 5684 subscribers.

You will learn about 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 How to programmatically detect fraud How to apply unsupervised learning to detect fraud How to apply supervised learning to detect fraud How to use box plots to identify fraud What is image analytics and how it is used to detect fraud What is correlation and how it is useful in fraud risk management How to use PowerBI in finding anomalies for fraud detection What is AutoML and how it can be used in fraud detection This course is ideal for individuals who are Auditors or Risk consultants or Machine learning enthusiasts or Students or Managers and Senior Management involved in fraud risk management or Fraud Investigators It is particularly useful for Auditors or Risk consultants or Machine learning enthusiasts or Students or Managers and Senior Management involved in fraud risk management or Fraud Investigators.

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

Summary

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

Price: $64.99

Average Rating: 4.25

Number of Lectures: 67

Number of Quizzes: 1

Number of Published Lectures: 47

Number of Published Quizzes: 1

Number of Curriculum Items: 73

Number of Published Curriculum Objects: 53

Original Price: $34.99

Quality Status: approved

Status: Live

What You Will 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
How to programmatically detect fraud
How to apply unsupervised learning to detect fraud
How to apply supervised learning to detect fraud
How to use box plots to identify fraud
What is image analytics and how it is used to detect fraud
What is correlation and how it is useful in fraud risk management
How to use PowerBI in finding anomalies for fraud detection
What is AutoML and how it can be used in fraud detection

Who Should Attend

Auditors
Risk consultants
Machine learning enthusiasts
Students
Managers and Senior Management involved in fraud risk management
Fraud Investigators

Target Audiences

Auditors
Risk consultants
Machine learning enthusiasts
Students
Managers and Senior Management involved in fraud risk management
Fraud Investigators

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
Detect fraud programmatically
Understand the drivers of fraud through Explainer AI (XAI)
Detect fraud through AutoML (No Code Machine Learning using PowerBI)
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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Understanding characteristics of fraud

Lecture 1: Understanding fraud

Chapter 3: Framework to assess fraud maturity

Lecture 1: Framework to assess fraud maturity

Chapter 4: Understanding different types of Fraud

Lecture 1: Types of fraud

Lecture 2: Types of fraud in different processes and sectors

Chapter 5: Fraud Analytics: Core techniques to detect fraud easily

Lecture 1: Snapshot of fraud detection

Lecture 2: Fundamental Rules in fraud detection

Lecture 3: Benford Law

Lecture 4: Concept of Standard Deviation to detect fraud

Chapter 6: How to use Excel to detect Fraud

Lecture 1: How to do Ageing Analysis in Excel

Lecture 2: How to create Pareto Chart in Excel

Lecture 3: How to apply Benford Law Using Excel

Lecture 4: How to create box plot in excel and identify outliers

Lecture 5: Benford Law for First Two Digits

Lecture 6: Fixing the responsibility after fraud analysis

Chapter 7: Sampling and Hypothesis Testing | Advanced Data Analysis

Lecture 1: Principles of Sampling

Lecture 2: Correlation

Lecture 3: Chi Square

Chapter 8: Fraud Analytics: Power of AI

Lecture 1: Setting the context

Lecture 2: What is AI?

Lecture 3: What gets measured gets improved

Lecture 4: Different take on the types of algorithms

Chapter 9: Detect Fraud Progammatically

Lecture 1: Regression Algorithms for Fraud

Lecture 2: Classification Algorithms for Fraud

Lecture 3: Unsupervised Learning Algorithms for Fraud

Lecture 4: Explainer AI (XAI) Algorithm: Explore the Reasons Behind Anomaly Detection

Lecture 5: Time based Anomaly Detection Algorithm

Lecture 6: Image Anomaly Detection

Chapter 10: Detect Fraud using AutoML (No-Code Machine Learning): PowerBI

Lecture 1: What is AutoML

Lecture 2: Time Based Anomaly Detection

Lecture 3: Classification Algorithm

Lecture 4: Regression Algorithm

Chapter 11: Managing Fraud – Actions to prevent fraud

Lecture 1: Insights about fraud – How to plan better?

Lecture 2: Fraud Triangle

Lecture 3: Core Actions to Prevent Fraud

Lecture 4: Actions for Work From Home Challenges

Chapter 12: Process Approach to detect and prevent fraud

Lecture 1: Process Approach to detect and prevent fraud

Lecture 2: Cause Vs Root Cause and Mistake Proofing

Chapter 13: Cost recovery

Lecture 1: Cost recovery

Chapter 14: Master Python: For Programmatic Approaches to Fraud Detection

Lecture 1: Setting the context

Lecture 2: Introduction to Colab: Google Cloud Development Environment

Lecture 3: Python Basics

Lecture 4: Arrays

Lecture 5: Numpy

Lecture 6: Pandas

Chapter 15: Conclusion

Lecture 1: Conclusion

Chapter 16: Bonus Lecture

Lecture 1: Bonus Lecture

Instructors

SeaportAi .
Artificial Intelligence and Business Transformation Experts

Rating Distribution

1 stars: 13 votes
2 stars: 31 votes
3 stars: 182 votes
4 stars: 432 votes
5 stars: 519 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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