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.

CPA Australia

Who Should Attend

  • Junior managers / managers / accountants
  • Executives in the accounting / auditing / finance-related job functions
Prerequisites
  • Basic accounting or finance knowledge
Avatar Female
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
Avatar Female
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
Chu Mui Kim

Senior Lecturer, Business, Communication and Design, Singapore Institute of Technology

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Janet Tan
Janet Tan

Lead Professional Officer, Singapore Institute of Technology

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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.

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.

Course Runs

March 2024
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13 Mar 2024 - 14 Mar 2024
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2 days
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In-person
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CPA Australia, 1 Raffles Place, #31-01 One Raffles Place, Singapore 048616
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SGD $1,962.00
Up to 90% SF Funding
Apply By:
20 Feb 2024 23:59

Frequently Asked Questions

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    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.