An AI solution for finance professionals of small and medium-sized businesses is no longer just a dream! The Committee on Finance & IT SMB, a committee of Financial Executives International saw a fantastic presentation by Ashok Manthena of Tadaa.ai on predictive analytics. CFIT SMB has a mission to share helpful Finance and IT solutions with the entire membership of FEI. Tadaa.ai is a software company that created a data science & Machine Learning (ML) platform to help clients with their financial modeling, planning, and analysis (FP&A). Clients of any-sized business can utilize the platform to get the benefits of an AI-driven forecast and related economic models without hiring a team of data scientist and AI experts.
Ashok says, “Data science uses statistics and math to extract meaningful insights while ML will study patterns to build models with actual and forecasted data to predict the future.” CFIT has been investigating this significant financial trend over the past year.
Predictive techniques use both time series models and ML models. Some time series models are ARIMA, SARIMAX, Prophet, Exponential smoothing, and Bayesian structural, and examples of ML models are XGBoost, LightGBM, LSTM, and Lasso. How can we get insights from the data? The question is, now, what will happen? How can we correct our course based on what is coming, not just analyzing the past?
Ashok says, “We are in a data-driven era where data is considered the new oil for the enterprise world.” Companies will be divided into those that make data-driven decisions and those that do not. Solution providers are counting on companies to leverage the ever-increasing volume of company data that can be leveraged to help those companies become more profitable.
Why is this important now? Firstly, the COVID crisis has every CFO looking to optimize business performance by increasing efficiency and agility. Secondly, early adopters who learn AI for finance solutions have an advantage over those who do not. Thirdly, AI technology is affordable with rich content, and lastly, there are challenges of inflation, supply chain delays, and a skilled labor shortage.
The steps in a typical data science project are data extraction, data cleanup (a critical step for better modeling), visualizing the data, model building, and model monitoring. All these steps are usually manual, but Tadaa.ai automated these tasks making it easy for finance teams to apply A. I in their use cases. Manual. A data-driven and driver-based forecast can be done much faster with AI.
When implementing AI solutions for finance, there are the following options: 1) Build it yourself with data scientists with finance backgrounds and include extensive testing and model training. 2) Use a third-party vendor to consult and build a custom solution, and 3) Use AI-enabled software to allow teams to experiment with no data scientists.
Here are seven sample AI uses cases: 1) Revenue Prediction using internal and external business data drivers, 2) Predicting maintenance costs with a combination of historical cost with an inflation factor, 3) People cost modeling, 4) Measure marketing channel ROI 5) Part number or SKU level sales predictions 6) Project completion accounting based on historical data and forecasted progress and cost for hundreds of projects 7) Cash flow forecasting based on predictive revenue and sales
Ashok says, “Contrary to what many believe; it isn't necessary to go all in when applying A.I.in finance. The key to success is to identify, discover and accelerate the A. I deployment in finance use cases.”
Select a use case with the most potential for your company. In my experience, predicting company cash flow is the best AI use case in my CFO role. I had to pull various data inputs like sales forecasts and customer collection trends into Excel and rely on historical financial trends. We did a manual five-year estimate with much room for improved forecast accuracy.
Tadaa.ai has some observations on AI forecasting projects that are the following: An average of 40% in increased forecast accuracy using AI; plan to reduce the time it takes to generate a series of “what if” forecasts by about 50%. Allow twice the number of projections to be utilized.
Ashok reports, “AI transformation requires finance teams to think unconventionally and continue learning. Implementing AI successfully can reduce business risk through quicker, more effective decision-making.”
AI is a finance tool to back up your decisions. AI provides you with data and insights into what drives your business. You can speed up your data analysis tasks with an AI tool for finance.
Starting early by using AI for finance will help expand the possibilities. Try using a finance user-friendly AI-powered forecasting tool. Upskilling the staff through data science and machine learning training will help your finance team become more data driven. Also, set realistic goals and choose straightforward AI use cases to start your journey toward utilizing AI for financial solutions. If you want to know more, you can visit their website at www.tadaa.ai.