02. Risk

We have seen it before…will fraud be different this time?

The past 12 months have given rise to unprecedented levels of personal anxiety and business stress.

As we saw following the Global Financial Crisis back in 2008, stress can cause ‘good people to do irrational things’. We anticipate that more instances of fraud and financial misstatement will emerge over the next two years, and even longer, as Government stimulus measures begin to wind back.

Companies face challenges in getting back to pre-COVID activity, and investors and stakeholders will expect strong financial performance. Recent instances, where executives have manipulated financial statements to make a company look ‘healthier’ and receive performance-based remuneration, serve as a warning to other business leaders. Unfortunately, one thing that never changes is human nature, greed and the tendency for some to go astray.

Employee fraud is also quite common. When someone has a need, and the opportunity presents itself, that person will often justify their actions and commit fraud by convincing themselves they will eventually pay the money back. It’s only later that they realise it is far more difficult to return money back into the system without raising alarm.

We predict there will be more fraud instances emerging in 2021, as we are already seeing increased demand for asset and fund tracing analyses, as well as expert reports. Together, fraud and insolvency assignments will require expert analysis and regular consultation to assist with any asset recovery action.

To respond to the increased risk of fraud and financial misstatement, audit committees and management this year will need to review controls and test for warning signs. Greater use of data analytics techniques that incorporate both predictive and continuous monitoring will be a powerful tool in interpreting the financial wellbeing of an organisation, recognising changing and uncharacteristic financial ratios, and detecting anomalous transactions or unexpected relationships in financial data.