Why black swan events push, and not limit the use of AI in Finance

When thinking of financial forecasting and the utilization of AI, the concept of time series is most often used. Time series forecasting is a statistical method to predict future values based on previously observed values. These kinds of models often make use of the one-way ordering of time so that values for a given future period are expressed as a derivation of past values.

It is true that time series based financial forecasting works best in a stable environment when the dynamic between variables can be reliably captured across time. In such conditions, some of the more skillful Financial Controllers can produce good enough estimations without AI, using Excel models. But in a black swan event, the unpredictable nature of the situation renders both time series and smart Excel models useless. It drives the need for agile and fast scenario planning, and this is where AI very much outperforms any other method, including Excel.

• Achieve fast and reliable scenarios by letting AI to do the heavy lifting

Preparing for more than one “official plan” has historically helped companies to manage their risks and asses their options in normal times. But it’s especially when facing high uncertainty, scenario planning become crucial in navigating a crisis effectively.  It’s important that reliable scenarios can be generated fast, given the operational lead time there often is for production- or project planning. Having a good scenario planning model in place that rapidly provides insights into best case / worst case type of scenarios helps a company understand and prepare for the possible financial implications, including the need for funding, reorganizations, or the need to delay certain investments.

For many companies, creating fast and reliable scenarios has proven to be a difficult goal, but now it can be much easier achieved with the use of AI. Like in other planning models, manual estimations are often biased and slow, since they require coordinated input from multiple parties. Embedding AI in this process helps automate the fine calculations while dramatically improving precision, requiring only to enter the key assumptions of the scenario at hand. While companies still have to invest time to train the AI to understand logical correlations between (mostly) non-financial inputs and their financial outcomes, once this is done the AI provides for a very powerful ally in the creation of different scenario models in a fraction of the time previously required Think about expectations related to covid infections, weather situations, or financial market indicators – it’s becoming increasingly easy to input these expectations into a well-trained AI model to see how they would impact the company’s financial forecast.

Today, one thing is certain: there will always be uncertainty. Companies can leverage AI to help deal with that uncertainty and prepare for the most probable outcomes.

^[1]www.investopedia.com