Beyond what is humanly possible
AI can aggregate and process data with far greater speed, accuracy, and scalability than what is humanly possible, allowing companies to streamline and improve their financial forecasting calculations. And whereas people can feel overwhelmed by large volumes of data, AI thrives better when it has more data to feed its algorithms.
In mainstream conversational AI applications, voice assistants, such as Siri and Alexa, can answer common questions and execute tasks because they can tap into internet search results and user data from emails and multiple apps. For AI in financial forecasting, if a machine can use non-traditional data (such as weather data or availability of stock) in addition to conventional financial data (like regional market information), then it can find new relations between metrics and get a more accurate picture of expected revenue and sales. Thanks to digital technologies, like natural language processing, forecasting teams can bring in previously unstructured data—the information in email texts, contracts, online transactions or financial statements—for even richer insights.
When organizations adopt machine learning, a system can identify patterns between multiple datasets and develop its own predictive models for future use cases. As more data becomes available and with continued use, these models become smarter and more accurate over time. They can even reveal the true drivers of business revenue. For example, using machine learning to analyze both financial and non-financial assets, a global technology discovered its traditional datasets (volume and price), had far less impact on its revenue than expected. In addition, the technology helped the company minimize its forecasting requirements from a three-week effort with a team of 100 to a two-day job for two people.
While machines play an important role in financial forecasting teams, people are still critical. After all, predictive models are only as good as the data that goes into them. Businesses need finance teams that have the process experience and industry knowledge, as well as data engineering skills, to review data for cleanliness, accuracy, and potential biases. They can become more like valued partners in the business, providing essential context to the models' outputs to the benefit of the enterprise.