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

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.

What's inside

Learning objectives

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
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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
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Syllabus

Introduction

Understanding characteristics of fraud

Understanding fraud

Framework to assess fraud maturity

In addition to using for ageing analysis, this dataset can be used for practicing Pareto chart, Benford law and box plot as well.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers Benford's Law, box plots, and correlation, which are foundational techniques in fraud detection and risk management

Explores the fraud triangle, which is a widely recognized model for understanding the motivations behind fraudulent behavior

Includes a framework to assess an organization's maturity in fraud risk management, which is useful for benchmarking and improvement

Teaches how to use Excel for fraud detection, which is a practical skill for professionals who need to analyze data quickly

Explores the use of AI and AutoML in fraud detection, which can help learners stay current with technological advancements

Requires learners to use PowerBI, which may require a subscription or license that some learners may not have

Reviews summary

Fraud analytics with excel and ai

According to learners, this course provides a strong foundation in fraud detection and prevention, blending traditional Excel techniques with modern AI and machine learning tools. Students appreciate the practical approach and the inclusion of topics like Benford Law, AutoML, and Explainer AI. The instructor's industry experience is frequently highlighted as a major strength, bringing real-world relevance to the concepts. While some sections might feel introductory depending on one's background, the course is generally seen as valuable for professionals looking to enhance their fraud analytics skills using a mix of tools.

Focuses on prevention, not just detection.

"Valuable insights into preventing fraud, not just finding it."

"The section on fraud prevention actions was particularly useful."

"Liked the framework for assessing fraud maturity."

Provides a solid overview of key concepts.

"Provides a great foundation on the topic."

"Helpful overview of various fraud detection methods."

"Covers important topics like Benford Law and XAI well."

Learners value insights from an industry expert.

"The instructor's vast experience really shines through."

"Gained valuable perspective from the industry veteran facilitating the program."

"His real-world anecdotes made the learning engaging."

Blends traditional and modern analytics tools.

"Appreciated the coverage of both Excel-based methods and AI."

"Good introduction to using AI alongside classic techniques."

"Loved how it showed both manual checks and automated methods."

Focuses on real-world application of techniques.

"The practical examples provided were very useful..."

"I learned how to apply the techniques directly to my work..."

"Very relevant content for fraud professionals."

"The hands-on approach helped solidify my understanding."

Pace and depth may vary based on background.

"Some parts felt a bit basic for someone with prior analytics knowledge."

"Could use more in-depth coverage on the advanced AI topics."

"If you're completely new, some AI sections might require extra study."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Fraud Risk Analytics (Excel & AI based tools) and Prevention with these activities:

Review Excel Fundamentals

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Reinforce your understanding of Excel, including formulas, functions, and data manipulation techniques, as the course heavily relies on Excel for fraud detection.

Browse courses on Excel

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Review basic Excel functions like SUM, AVERAGE, and COUNT.
Practice creating charts and graphs for data visualization.
Familiarize yourself with pivot tables for data summarization.

Review 'Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques'

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Gain a deeper understanding of fraud analytics techniques by reviewing a comprehensive book on the subject.

View Principles of Database Management: The... on Amazon

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Read the chapters related to descriptive and predictive analytics.
Summarize the key concepts and techniques discussed in the book.
Relate the book's content to the course's Excel and AI-based tools.

Practice Benford's Law Calculations

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Solidify your understanding of Benford's Law by working through practice problems and applying it to different datasets.

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Find datasets online or create your own datasets.
Calculate the expected digit distribution using Benford's Law.
Compare the actual digit distribution to the expected distribution.
Identify potential anomalies based on deviations from Benford's Law.

Four other activities

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Build a Fraud Detection Dashboard in Power BI

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Develop a practical skill by building a fraud detection dashboard using Power BI, incorporating the techniques learned in the course.

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Connect Power BI to a relevant dataset.
Create visualizations to highlight potential fraud indicators.
Implement interactive filters and drill-down capabilities.
Design the dashboard for easy interpretation and action.

Create a Fraud Detection Case Study

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Apply the concepts learned in the course by creating a case study that demonstrates how to detect fraud using Excel and AI-based tools.

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Choose a real-world fraud scenario or create a hypothetical one.
Gather relevant data and prepare it for analysis.
Apply Excel and AI techniques to detect fraudulent activities.
Document your findings and present them in a clear and concise report.

Review 'AI and Machine Learning for Coders'

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Expand your knowledge of AI and machine learning by reviewing a book that focuses on coding implementations.

View AI and Machine Learning for Coders on Amazon

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Read the chapters related to classification and regression algorithms.
Experiment with the code examples provided in the book.
Apply the concepts learned to fraud detection scenarios.

Contribute to an Open Source Fraud Detection Project

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Deepen your understanding and contribute to the community by participating in an open-source fraud detection project.

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Find an open-source project related to fraud detection.
Review the project's documentation and code.
Identify areas where you can contribute, such as bug fixes or new features.
Submit your contributions to the project.

Career center

Learners who complete Fraud Risk Analytics (Excel & AI based tools) and Prevention will develop knowledge and skills that may be useful to these careers:

Fraud Analyst

A Fraud Analyst is responsible for investigating and analyzing suspicious activities to detect and prevent fraud. This course directly helps an aspiring fraud analyst. The program delves into the characteristics and different types of fraud, providing a foundation for identifying fraudulent patterns. Techniques taught, such as applying Benford's Law, creating box plots to identify outliers, and using Excel to detect fraud, are all essential skills for a fraud analyst to use when examining various datasets. Furthermore, the course also covers how to identify drivers of fraud through Explainer AI and using AutoML, advancing the analytical capabilities of a fraud analyst. The program’s focus on both detection and prevention, including best practices in fraud management, makes it an ideal choice for this type of role.

See salaries and explore the career path for Fraud Analyst

Forensic Accountant

A Forensic Accountant investigates financial discrepancies and fraud, and this course will be relevant. The program covers foundational concepts like fraud characteristics and different types of schemes. The course's emphasis on fraud detection and prevention techniques, including the use of Excel, AI, and methods like Benford's Law and box plots, directly relates to the day-to-day tasks of a forensic accountant. The course's deep dive into the nuts and bolts of fraud detection will help a forensic accountant understand not only how fraud occurs but also how to find it.

See salaries and explore the career path for Forensic Accountant

Internal Auditor

The Internal Auditor evaluates an organization's internal controls, and this course directly helps them to assess the risks related to fraud. The course provides a strong foundation in understanding fraud characteristics and different types of fraud. The methods for detecting fraud, including the use of Benford's Law and box plots are important techniques for an internal auditor doing forensic accounting. Furthermore, the course's insights into using AI and machine learning for detecting fraud increases an auditor's ability to assess sophisticated schemes. It also covers how to assess an organization's maturity in fraud prevention, which will aid an internal auditor in auditing the systems used by an organization.

See salaries and explore the career path for Internal Auditor

Financial Investigator

A Financial Investigator examines financial records and transactions to detect fraud and financial crimes. This program helps to teach the type of analytical techniques that they use. The course covers fraud characteristics and various types of fraud, which helps a financial investigator understand the context in which different fraud schemes are perpetrated. The different techniques of finding fraud that are covered by the program, such as using Benford's law and box plots, provide practical methods to locate fraud. The program's use of AI tools for fraud detection is also a useful skill for those who wish to enter the field.

See salaries and explore the career path for Financial Investigator

Risk Analyst

The Risk Analyst examines potential threats and vulnerabilities within an organization, and this course can help. This program includes a module on assessing fraud risk management maturity. The program covers various fraud detection techniques using Excel and AI, which can translate to overall risk identification. The course's exploration of statistical techniques such as using the concept of standard deviation, sampling, hypothesis testing, and correlation will contribute to a Risk Analyst’s quantitative analysis skillset. Studying different types of algorithms for fraud detection can make a risk analyst more conversant in both the prevention and reaction to fraud.

See salaries and explore the career path for Risk Analyst

Financial Controller

A Financial Controller is responsible for the financial reporting and management of an organization, and this course helps build their fraud awareness and skills. The program covers characteristics of fraud and ways to detect and prevent it. Techniques like using Benford's Law and box plots for anomaly detection, as well as using Excel, can aid a financial controller in their analysis. Furthermore, the course presents best practices in fraud management, and a financial controller will apply these methods to their duties. While a financial controller is not necessarily investigating fraud, the knowledge of how to detect it is invaluable.

See salaries and explore the career path for Financial Controller

Security Analyst

A Security Analyst aims to protect an organization's assets and information which involves the prevention of fraud. This course can help a security analyst to understand how fraud occurs and to take proper action. The program covers both fraud detection and prevention, and is useful to learn how to spot vulnerabilities. The course also discusses different types of fraud, and this can help a security analyst understand possible security issues. The course's focus on applying AI and machine learning to fraud detection can also help a security analyst identify vulnerabilities.

See salaries and explore the career path for Security Analyst

Compliance Officer

A Compliance Officer ensures that an organization adheres to regulatory guidelines and internal policies, which often includes preventing fraud. The concepts covered in this course, such as types of fraud and how to detect and prevent it, are directly relevant. Understanding fraud detection techniques using Excel and AI and best practices in fraud management will enable a compliance officer to implement and enforce effective anti-fraud measures. The course’s discussion of assessing an organization's maturity in fraud prevention can also provide a framework for compliance audits and internal controls, making the program useful to a compliance officer.

See salaries and explore the career path for Compliance Officer

Data Scientist

A Data Scientist works to analyze large datasets and derive insights, and this course helps them explore specific applications. This program introduces methods for detecting fraud using both Excel and AI, and the course's inclusion of topics like Benford’s Law and outlier detection will provide background to a data scientist working on detection algorithms. The program also covers how to programmatically detect fraud and how to understand the drivers of fraud through Explainer AI. Finally, the course offers insight into how to use AutoML for fraud detection, which can contribute to the modeling skills of a data scientist.

See salaries and explore the career path for Data Scientist

Data Analyst

A Data Analyst works with data to find insights, and this course may be beneficial in their career. This course has a strong focus on using Excel for data analysis and fraud detection, an important task for a data analyst to perform. The course also teaches how to detect fraud programmatically. The course's incorporation of machine learning, through AutoML and PowerBI, can help a data analyst improve their analytical skills. This course's practical and hands-on nature may help develop practical skills applicable to data analysis.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

A Business Intelligence Analyst analyzes business data and provides insights to improve decision making; this course may be useful towards this goal. The course covers how to use PowerBI to find anomalies in data for fraud detection and this skill can be transfered for business intelligence. The program’s focus on data analysis techniques through methods like Benford law, box plots and using Excel for fraud detection is generally helpful for data analysis. This course's use of AI and machine learning for fraud detection may also be applicable to broader business intelligence tasks.

See salaries and explore the career path for Business Intelligence Analyst

Business Consultant

A Business Consultant provides advice to organizations to improve their performance, and this course can help them offer anti-fraud specific guidance. The program covers different types of fraud, how to detect it, and how to prevent it. This course's discussion of assessing an organization's maturity on fraud prevention, along with best practices, may be useful for consulting in this specific domain. The course's coverage of both operations and technology perspectives is useful to a consultant who must understand different areas of business.

See salaries and explore the career path for Business Consultant

Operations Manager

An Operations Manager oversees the daily operations of an organization, and this course may be helpful in ensuring minimal exposure to fraud. The program covers different types of fraud as well as how to detect fraud. Using methods such as box plots and Benford's Law to detect fraud, which is taught in this course, may be used to detect anomalies in operations. The course also discusses best practices in fraud management. The study of fraud maturity can help to create more robust and ethical practices in operations, making this course a useful consideration.

See salaries and explore the career path for Operations Manager

Financial Advisor

A Financial Advisor helps clients manage their money, and this course offers them expertise in how to help safeguard accounts. The program covers different types of fraud, as well as how to detect and prevent it. The course introduces techniques such as Benford law and box plots which can be used to identify suspicious transactions. Moreover, the course discusses methods for preventing and managing fraud. This is all helpful background information for a financial advisor in better protecting their clients.

See salaries and explore the career path for Financial Advisor

Project Manager

A Project Manager oversees projects, and this course provides insight into how projects can suffer from fraud. This course covers different types of fraud and how to prevent it, which will bring an awareness that can be useful to a project manager. By covering best practices in fraud management, the program provides a framework that a project manager may find helpful. Learning how to conduct a maturity assessment for fraud prevention may help in ensuring that the projects are run with integrity. Although not directly dealing with fraud as a project manager, this course's content is still helpful.

See salaries and explore the career path for Project Manager

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

What's inside

Learning objectives

Syllabus

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Reviews summary

Fraud analytics with excel and ai

Activities

Career center

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