This course covers the role data analytics has in the identification of uncommercial transactions for later review and, if justified, investigation. It is one of the key ways in which uncommercial transactions can be identified.
Many businesses have a sense of the value that data analytics can deliver and do invest time and resources in this area. However, while data analytics targeting profitability, cash flow, business efficiency and so on are generally done well, that targeting the identification of uncommercial transactions is often less effective.
Key topics:
- ‘Data analytics’ – what is it?
- What data does an organisation need in order to perform data analytics?
- What tools are used?
- What frauds, corruption and uncommercial transactions can be detected?
- Post-transactional data analytics: a step-by-step guide
- Step 1: Analyse the business
- Step 2: Design data tests
- Step 3: Data acquisition
- Step 4: Data preparation and normalisation
- Steps 5, 6 and 7: Analyse, report and investigate
- Case study
- Challenges
- Analysis of data in real-time or near real-time
Learning objectives
- Explain the emerging discipline of data analytics
- Explain the data requirements and the tools used to enable data analytics
- Apply data analytics to effectively identify fraudulent, corrupt and other uncommercial transactions
Audience
This course is suitable for accountants working in financial investigations and forensic accounting in the corporate, SME, public practice, not for profit and public sectors.
