AI and the Future of Finance

AI and the Future of Finance

02 August 2018

Artificial Intelligence (AI) has become one of the hottest topics. So how does AI fit into Finance? As a field that is focused on executing strategies with wide-reaching impacts, the addition of AI to current functions can vastly lighten the load for finance teams. In this article, we’re going to take a closer look at what an AI is, how it works and the positive impacts it can have on the office of finance.

From Sci-Fi Character to Business Application

The most popular portrayals of AI are found in science fiction – such as a “Terminator” or “Blade Runner” type of humanoid robot. It looks and behaves like a human, but is made of metal, wiring and plastic. While building robots that emulate human abilities are definitely in the works, there are other more immediately practical uses for AI technology.

AI is a software or hardware that emulates human intelligence in some shape or form. In this context, AI does not need to look or sound like a human being – rather, it is able to imitate or perform functions that previously needed to be done by a person. This means that AI can be anything from a chatbot on a website to an self-checkout device at a grocery store – either way, both aim to emulate human intelligence in a way that’s useful to people.

Teaching a Machine How to Think – Machine Learning

The methods by which AI can be achieved are just as diverse as the types of AI researchers want to create. One such method that’s been picking up a lot of steam is called Machine Learning (ML). Machine Learning aims to teach machines how to make judgements through algorithmic pattern recognition. By learning how to make the right decision through multiple iterations of massive amounts of data, ML-based AI’s will not need to be programmed for every conceivable scenario, making it an adaptive application that’s easier to deploy.

The complexity of the decisions that a ML-based AI can make really depends on how it’s trained. Currently, there are three key methods by which ML-based AIs can learn how to make decisions – Supervised ML, Unsupervised ML and Reinforcement Learning. Here’s a brief description of all three:

  1. Supervised ML – these AIs learn by referring to “tags” given to them by people. They make a decision by cross-referencing these tags against the data they receive. While the tags need to made by people, these AIs can churn out high quality data as a result.
  2. Unsupervised ML – with this method, the AI is given an algorithm that allows it to draw intrinsic information on the data it receives. By drawing comparisons, it’s able to make predictions on the data it’s given.
  3. Reinforcement Learning – by giving AIs information on a final result of a process and a plethora of processes to analyze, these AIs are able to see patterns in how a result is achieved and better predict the outcomes of future processes.

While not entirely autonomous, ML-based AIs have the potential to take over repetitive yet nuanced finance functions, as well as provide useful insights to support higher-level business development in the office of finance.

The Potential of AI to Transform Finance

Based on how it learns to process data, the skills offered by ML-based AI can benefit the Office of Finance in multiple ways. Here is our vision on what each type of ML-based AI can do for the office of finance.

Supervised-ML AIs – The Helpful Accountant

Basic accounting functions that are nuanced but predictably consistent are a great match for this type of AI. Basic Accounts Payable, Receivable, Payroll tasks can be completed accurately and efficiently, tailored to the needs of team members. Report-building will also become easier as this AI can gather data and pre-fill templates on a regular basis. Finance departments will have a very reliable 24/7 accountant that can perform simple tasks and find data on their behalf.

Unsupervised-ML AIs – The Predictive Data Analyst

As an AI that predicts rather than decides, Unsupervised-ML AIs can be a powerful ally in building realistic budgets and forecasts. Drawing past data out of an ERP, it can analyze existing numbers and provide finance teams with possibilities on what the future can look like. Moreover, it can offer the probability of whether specific fiscal scenarios will play out and how likely their numbers will change due to industry trends. This will give finance teams a stronger foundation on which to budget and forecast, allowing them to make better business decisions for the future.

Reinforcement-ML AIs – Advisor to the CFO

This is the AI that can hyper-focus on big picture objectives such as what steps the CFO would need to take to maximize revenue. Working in tandem with multiple data sources including the finance team and the CFO themselves, the Reinforcement-ML AI can analyze a company’s financial footsteps to determine which decisions made the biggest differences in revenue. These insights in turn can be sorted into negative and positive changes and mapped out to recommend the order and timing for these changes. All-in-all, the CFO will gain a very useful advisor who can provide timely and relevant information on important long-term decisions.

While AI technology is still in its infancy, it has the potential to greatly enhance and benefit finance & accounting teams. Here at Limelight, we have already begun exploring the potential possibilities of embedding AI in our own platform. Click here to book your demo and see Limelight in action.

About the Author

Jade Cole Jade Cole
Jade is the Chief Technology Officer (CTO) of Limelight Software.

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