How can artificial intelligence (AI) and machine learning (ML) help finance professionals and CFOs make proper business decisions?
The role of a modern-day CFO or a finance professional is rapidly evolving and has become all the more demanding now. The role of a finance professional or a CFO in an organization has transformed from being merely a scorekeeper to a strategic advisor, and even a business partner or a technocrat who leads organization’s digital transformation. There are a lot of expectations from these executives who are the go-to people for many decision makers in an organization including the CEO. It is only inevitable that technology comes to the rescue of such professionals whose role is dynamic and complicated. In particular, how can enterprise applications in an organization support them in making proper business decisions?
It is a fact apparent that enterprise applications consume oodles of data. What are we doing with that data, though? Are we paying close attention to it? Yes, in some cases. But, mostly no. It is essential that someone pays attention to it and keep us all informed. We can make enterprise applications do this by infusing it with artificial intelligence (AI). The success of an artificial intelligence platform is gauged by the way it learns from the data patterns which it consumes over a period of time. The more it consumes, the more intelligent it becomes.
Using data patterns, a recommendation engine can be built to recommend the right choices. Eventually, this engine can start anticipating and pre-empting the future course of action to guide the users to make the right choices. When Google can autosuggest based on user patterns, and food apps can automatically recommend your favourite restaurants or dishes, why can’t an enterprise application do the same in the specific context? When a majority of the transactions are intelligently managed, then the senior management’s attention will be needed only for the select few tasks that require their expertise.
Focus on the right transactions
If you take the case of a remittance advice from a customer which is in an unstructured format, an accountant may have to waste his/her precious time working over it. Won’t it be nice if the enterprise application deciphers this and automatically clears for reconciliation based on the rules and the data patterns from the past? The accountants can then work only on exceptions or tasks that truly warrant their attention instead of performing mundane tasks. So, can the labour-intensive tasks be reduced and resources be optimized? The answer is a “Yes,” with a robust AI/ML Rule engine.
Can we prevent mistakes from happening?
Using AI and ML, we can detect anomalies and predict probable mistakes. Instead of an event occurring post which it is analyzed and dissected, we can make the AI/ML engines detect exceptions proactively while performing transactions and route approvals appropriately. With the help of intelligent solutions, we can train a system to provide insights for every transaction while performing an authorization to the supervisor. E.g., what happened the last time when you approved a similar order/request, whether it was successful, the ROI, etc. With this, the finance professionals can be better prepared for audits and ensure better governance in the organization, which otherwise gives sleepless nights to the finance function.
Focus on the future
Using AI and ML, we can estimate what the future has in store based on past patterns, seasonality and a bit of integration with big data, in terms of the trends in the market. There are other developments too. Can a CFO talk to a bot to get the information he/she needs and work on it? “Hey, Alexa! Will we meet our sales target? Which product’s sale will add the most to the bottom line? Which salesperson will meet the target? Which salesperson will need support to meet the target? What will be my cash position in 6 months?” The advent of smart conversation bots has started helping CFOs get the answers quickly, saving them time and effort.
Enabling smart solutions
Remedies to critical business problems lie in solutions that are driven by artificial intelligence. In order to get the benefits of AI and ML, a standard integrated platform is recommended, which should have the ability to learn and understand the data patterns along with the potential to consume various algorithms to provide relevant answers. The onus now lies with the organizations to think ahead, to develop and plan relevant use cases. They need to identify problem statements and what they feel is a solution from the data points they have, so that they can teach the system with respect to what it has to do. Popular author and speaker Stephen Covey’s “Begin with an End in Mind” mantra will work like magic in this scenario.