Data Forensics in Accounting

Course Overview

Domain

Business, Communication and Design

Infocomm Technology

Format

Short Course

Duration

2 days

Fee Subsidy

Up to 90% SF Funding

Professional Development Units

16 CPD hours (CPA)

You will learn how to plan, design, and apply numerous data analytics tests, including examining and interpreting the results of those tests, to identify the red flags of fraud.

The first day of the course aims to build a strong foundation of data analytical skills by reviewing the types of fraud schemes and examples of data analytics tests that can be performed to detect the warning signs of such schemes. The various data analysis techniques that include both supervised and unsupervised learning for fraud or anomaly detection will be covered in detail.

On day two, you will go through experiential learning where you will apply your knowledge of analytical tests, data sources, and fraud schemes to real data sets and scenarios to uncover trends and patterns in the data.

As we are moving towards safeguarding against financial crime, employees equipped with the skills of data analysis and forensics investigation are at the forefront of fighting against financial crime.

Programme Partner

This course is supported and endorsed by CPA Australia.

Who Should Attend

Junior managers / managers / accountants
Executives in the accounting / auditing / finance-related job functions

Prerequisites

Basic accounting or finance knowledge

I highly recommend this course to others as there were useful points applicable across different industries.

Ow Wai Ching Nicole

Financial Analyst, Prospec Surfaces Pte Ltd

A good start to Power BI.

Prabhash Nadesan

Vice President, Group Internal Audit, Frasers Property Limited

A very informative session over two days, and very knowledgeable trainers.

Michelle Lee

Data Analytics Associate, BDO LLP

Very insightful into forensic accounting and the data analytics tool (Power BI).

Daryl Pang

Assistant Manager, Group Internal Audit, Frasers Property Limited

What You Will Learn

Acquire overall concepts of data analysis in detecting fraud
Gain knowledge on common detection techniques of fraud
Be better equipped to identify irregularities in data trends and data profile
Be able to perform quantitative data analysis

Teaching Team

Chu Mui Kim

Associate Professor, Business, Communication and Design, Singapore Institute of Technology

Janet Tan

Lead Professional Officer, Professional Officer Division, Singapore Institute of Technology

Schedule

Time	Day	Topics
9:00 am - 6:00 pm	Day 1	Anti-fraud Data Analytics Tests When reviewing a company’s financial statements, certain undesirable characteristics may stand out as fraud red flags. Participants will learn how to integrate data analytics tests into fraud risk assessment or investigative work plans through a library of test examples that is organised by categories of occupational fraud risks, such as corruption, bribery, asset misappropriation and financial statement fraud. Data Fundamentals This session will introduce participants to the uses, benefits and challenges of data analytics techniques, as well as the types of data that can be analysed using available software options to perform data analysis tests. Data Analysis Techniques Participants will discuss many of the most common data analysis techniques — such as duplicate testing, matching, gap testing and rule-based analytics — that can be used to comb through the data and identify anomalies and red flags of fraud. They will then progress to more sophisticated analysis techniques such as Benford's Law analysis, regression analysis, risk scoring and machine learning to bring fraud detection efforts to the next level. Modern technology also allows for the handling of Big Data with analytics techniques on unstructured data such as text analytics as well as visual, timeline analytics, and transactional risk-ranking techniques that incorporate time intelligence and drill-down capability.
Day 2	Case Study 1: Financial Statement Fraud This session explores specific data tests that participants can use to spot red flags of fraud in the customer sales cycle in their organisations. They will apply the data analytics techniques by working on a hands-on case study using spreadsheets. Case Study 2: Detecting Corruption & Misappropriation of Cash Discussion scenarios will be used to walk through data analytics techniques to analyse the structured and unstructured data in which warning signs of corruption schemes and misappropriation of cash are often found. Case Study 3: Expense Reimbursement Fraud This session focuses on data analysis techniques to uncover fraud schemes within expense reimbursements. Participants will apply the data analytics by working on a hands-on case study involving expense reimbursement fraud using a business intelligence tool. They will learn how to navigate dashboards and perform effective dashboard actions to uncover anomalies in data.

Time

Day

Topics

9:00 am - 6:00 pm

Day 1

Anti-fraud Data Analytics Tests
When reviewing a company’s financial statements, certain undesirable characteristics may stand out as fraud red flags. Participants will learn how to integrate data analytics tests into fraud risk assessment or investigative work plans through a library of test examples that is organised by categories of occupational fraud risks, such as corruption, bribery, asset misappropriation and financial statement fraud.

Data Fundamentals
This session will introduce participants to the uses, benefits and challenges of data analytics techniques, as well as the types of data that can be analysed using available software options to perform data analysis tests.

Data Analysis Techniques
Participants will discuss many of the most common data analysis techniques — such as duplicate testing, matching, gap testing and rule-based analytics — that can be used to comb through the data and identify anomalies and red flags of fraud. They will then progress to more sophisticated analysis techniques such as Benford's Law analysis, regression analysis, risk scoring and machine learning to bring fraud detection efforts to the next level. Modern technology also allows for the handling of Big Data with analytics techniques on unstructured data such as text analytics as well as visual, timeline analytics, and transactional risk-ranking techniques that incorporate time intelligence and drill-down capability.

Day 2

Case Study 1: Financial Statement Fraud
This session explores specific data tests that participants can use to spot red flags of fraud in the customer sales cycle in their organisations. They will apply the data analytics techniques by working on a hands-on case study using spreadsheets.

Case Study 2: Detecting Corruption & Misappropriation of Cash
Discussion scenarios will be used to walk through data analytics techniques to analyse the structured and unstructured data in which warning signs of corruption schemes and misappropriation of cash are often found.

Case Study 3: Expense Reimbursement Fraud
This session focuses on data analysis techniques to uncover fraud schemes within expense reimbursements. Participants will apply the data analytics by working on a hands-on case study involving expense reimbursement fraud using a business intelligence tool. They will learn how to navigate dashboards and perform effective dashboard actions to uncover anomalies in data.

Certificate and Assessment

A Certificate of Participation will be issued to participants who

Attend at least 75% of the course
Undertake and pass non-credit bearing assessment during the course

Fee Structure

The full fee for this course is S$1,962.00.

Category	After SF Funding
Singapore Citizen (Below 40)	S$588.60
Singapore Citizen (40 & Above)	S$228.60
Singapore PR / LTVP+ Holder	S$588.60
Non-Singapore Citizen	S$1,962.00 (No Funding)

Note: All fees above include GST. GST applies to individuals and Singapore-registered companies.

Funding

Learn more about funding schemes available

Course Runs

April 2025

16 Apr 2025 - 17 Apr 2025

2 days

In-person

CPA Australia, 1 Raffles Place, #31-01 One Raffles Place, Singapore 048616

SGD $1,962.00

Up to 90% SF Funding

Apply By:

25 Mar 2025 23:59

Apply Now

Frequently Asked Questions

What coding skills do I need for this programme?
No programming skills are required for this programme as we are using spreadsheet application and business intelligence tool for practical application.
You will be required to bring along your own laptop and ensure that Microsoft Power BI has been installed prior to the workshop.

Upcoming Events

CM-OP-202205000016