A.I. Financial Planning
Rebellion Research’s AI investing program actively monitors the front page news and we are surprised by how much it is filled with ambitious CEOs with their aspirations and wisdom, as investors are more intrigued by stories of charismatic leaders like Elon Musk and his grand plans of revolutionizing the auto industry. However, few analysts and even executives really pay attention to the execution phase of the business. More specifically, many overlook the financial planning and analysis of the operations, from budget control to working capital management. AI investing is different, however, in that we do not miss on important factors like this. In this volatile time following the pandemic, Rebellion Research would like to examine this backbone of the businesses as it is directly linked to the very survival of the companies.
What is Financial Planning and Analysis (FP&A)?
FP&A analysts are responsible of maintaining a healthy and effective cash flow. On one hand, they make sure of the liquidity metrics are within a healthy range for companies. On the other hand, they are actively evaluating working capitals and business units for optimal strategies. Additionally, no companies would be profitable forever, so it is the FP&A analysts’ role to make sure the company would thrive under all circumstances. This is particularly critical this year when thousands of companies filed for bankruptcy partly due to a failed cash flow chain.
At Rebellion Research, we believe that the use of artificial intelligence in FP&A will be very promising in the coming years given the following benefits from our AI investing analysis.
AI can create and monitor financial databases
Rebellion Research has found that more than 50% of the team’s time is spent on administrative tasks like budgeting and expensing. A minimum function for machines, inputting data into the excel spreadsheets is a hideous and repetitive task for humans. It is indeed an easy task, but the sheer size of the database is taunting when mistakes are made. It is every financial analysts’ nightmare to go over thousands of entries.
With a simple program that actively tracks financial data and monitors the inputs, managers are able to see the financial information clearly without problems. Leveraging the big data analysis, AIs can identify even the smallest anomalies within millions of entries, alerting the manager of potential frauds or business interruptions. Just like AI investing, AI financial planning is able to pick up these cues missed by human analysts.
AI can categorize financial data
There are two kinds of financial data: the statistical data and the unorganized data. AIs have already been utilized to compile standardized financial data for a long time and this trend is growing thanks to the work-from-home trend amid the pandemic. This streamlines the process for employees and enables the managers to see the financial status of the company clearly.
A more recent development is the use of AI to organize messy data that are largely underused in business. The use of neural networks and natural language processing can be performed to keep track of non-linear financial data and prepare them for further analysis either by the machines or by experts.
This is also used by Rebellion Research in our AI investing strategy where we utilize NLP to analyze 10ks and financial reports.
AI can improve financial accounting process
Traditional financial accounting requires the accountants to manually process thousands of settlements every month, match them with the invoices, and then cross-verify with the current accounts. It is hideous work necessary to provide a basis for subsequent collections and payments to different parties.
An AI can automate the entire process and work 24/7. For example, financial robot can automatically reconciliate bank accounts without manual intervention through cross referencing and verifying cash flows. The robot can also record payments and send monthly reminders to parties. Robot can also automatically extract information from several databases online to verify payments and accounts. Confirmation.com, for example, connects bankers, auditors and financial professionals to validate data and transactions. This would save thousands of meaningless emails on verifications and phone calls to international banks, granting efficiency to the entire organization.
AI can perform financial analysis
Thanks to recent developments in machine learning, it is possible for AIs to analyze and sort all aspects of the company’s financial and operation status. It can compile the collected data into an integrated data base to perform analytics. The machine would build a financial model on all aspects and alert the manager of the impacts of different business segments. In contrast to humans who often focus on one aspect of the problem, AI can notice even the smallest details in complicated models.
Through the advanced neural network program and Monte Carlo simulations, the machine can make educated predictions on the potential financial decisions made by the managers. They can even find some potential financial risks in this model, and then use effective early warning and prevention to prevent corporate interests from being affected.
AI can help financial planning
Business planning and predictions is an important part of the operation. Managers have to make informed decisions about the next quarter or year based on the results from the last quarter. Often this involves the management of working capital, from inventories to new hires. Traditional business operation forecasts are based on manually inputted information and performed arbitrarily by people based on their own visions and experience. While industry knowledge is important, this approach has a certain degree of instability and inaccuracy.
AI can offer a new perspective into the financial planning. It can comprehensively collect financial information from a variety of aspects and analyze key metrics like revenues, expenditures, contingencies, etc. Then it will offer insights based on the liability level, liquidity ratios, etc. It can even leverage the big data collected from customers and employees to perform a comprehensive analysis on the operation and make predictions on the next quarter from this quantitative model. This is something that the traditional approach cannot do and warrants the necessity of investing in AI.
AI can assist on project management
One of the most important tasks for a CFO is calculating the returns on current and potential projects. Currently, most CFOs would use a combination of qualitative research and financial calculations to justify and analyze the projects. AIs do assist in analyzing alternative data in the market research, but, more importantly, they help mitigate the inherent risks of using current financial calculations like IRR and NPV.
Both IRR and NPV have their own flaws with NPV disproportionally favoring big projects with lower returns and IRR having a bias on long-term profits. According to Rebellion Research’s proprietary research, AIs can look at these projects at a holistic level. While assisting the day-to-day administration of these projects, the neural network collects and analyzes data on budgets, expenses, revenues, etc. that would otherwise be overlooked by humans given their hideous nature and large quantity. Overtime, it can learn to identify potential impacts of different factors and even predict the returns on these projects to offer the best risk and reward analysis for managers.
As a result, the use of AI in financial planning would offer great benefits to the companies and replace many manual tasks for the employees to focus on revenue-generating work. With the work-from-home trend, Rebellion Research is confident to see more automation and machine learning technologies being used in this aspect of the business. Our AI investing strategy will pick the companies that utilize this technology the best.
The chart above shows the Value Added Money Index (VAMI) and the monthly returns of the portfolio. VAMI is calculated in USD, showing how much an initial investment of $1000 would grow based on the returns of the strategy. The return is calculated in percentages, showing the monthly percentage growth of the portfolio.