If you are working in the finance industry, then you naturally have an interest in helping to keep your company on a financially stable footing. You shouldn’t underestimate the extent to which financial risk modelling could help you to do this; in fact, many financial firms already heavily seek it.
It has been estimated that the predictive analytics market will surpass $10.95 billion in value by 2025. However, before you act to take advantage of financial risk modelling, you should educate yourself on the following points that can be too easy to overlook.
Risk should not be confused with volatility
Before you start to put together a financial risk model, you should be aware of what exactly you are modelling. This question warrants emphasis because, though assessing risk is a common responsibility in the finance world, risk itself is not always reliably defined.
The U.S. News website observes: “Academia has defined risk as volatility because it can be measured.” As a foundational concept, volatility underlies most financial models. When investors assess stocks, they do so using the stocks’ fluctuations to the backdrop of the market.
However, the terms “risk” and “volatility” should not actually be used interchangeably. Doing so would be risky, as a particular sector’s beta can be significantly different to that of its stock market index. Therefore, a stock’s beta can be a poor indicator of that stock’s risk exposure.
Financial risk can be divided into four broad categories
Through taking account of these categories, you can garner an even more accurate perception of what constitutes risk. The four categories are defined by Investopedia, which lists them as market risk, credit risk, liquidity risk and operational risk.
Market risk refers to dangers of condition changes in the company’s specific marketplace. Customers increasingly choosing to buy finance products online rather than on bricks-and-mortar premises would be an instance of market risk; it would suggest a need for firms to revise their strategy.
As it extends credit to customers, a business can incur credit risk. Liquidity risk concerns a firm’s ability to easily change its assets into cash, while operational risks are varied and would come into play due to the company’s usual business activities.
A financial model’s findings must be well-communicated
Financial risk modelling can help your business in making risk-aware decisions. Fortunately, by using technology and algorithms in the right way for your financial risk modelling efforts, you can more easily predict customer outcomes.
Still, even accurate insights from a financial risk model could count for surprisingly little if, as a risk manager, you fail to impart the findings to people who can act on them. Here, we are referring to the board and CEO who have the ultimate responsibility of devising corporate strategy.
There remains the unfortunate possibility of you failing to explain the model’s findings with sufficient clarity. Harvard Business Review points out that information could even reach senior management overly late or, due to intermediaries, become distorted on its way there.
Risks should be managed in real time
In the fast-changing financial world, it wouldn’t suffice for you to simply put together one financial risk model and then long leave it as the continually unaltered basis for future predictions. This is because the model would be capturing only the risk profile as it stood at a particular point in time.
In sectors other than finance, risks tend to see a more gradual rate of change. Furthermore, the changes tend to arise due to a policy shift on the company’s own part; for example, moving into a fresh market that uses a foreign currency.
However, due to the many derivatives positions and positions with embedded derivatives that financial firms have, these companies’ risks can keep changing regardless of the actual firms’ positions. Therefore, these companies should conduct financial risk modelling regularly.
Financial risk modelling can be even more effective in the cloud
Fresh advances in technology can open up fresh opportunities in financial risk modelling. The UK-based cloud consultancy RedPixie has noted how one of its clients was blighted by legacy Linux operating systems and a weak disaster recovery position that restricted its financial risk modelling.
This client believed that the on-premises estate it used for this modelling was nearing its maximum capacity. RedPixie responded by re-platforming the firm’s architecture in Azure to boost efficiency.
All of this sheds light on how your company, too, could improve the effectiveness of its financial forecasting by utilising financial risk modelling in the cloud. A cloud partner like RedPixie might be able to assist.