One of the big advantages of Business Process Automation (BPA) software is that it automatically collects data related to financial transactions. As the software processes invoices, purchase orders, expense reports, and other financial documents, it automatically creates an audit trail and logs important data.
Accurate, up-to-date, accessible data is vital for forecasting and predictive analytics. Ten years ago, a lack of good data was one of the biggest challenges facing Financial Planning & Analysis (FP&A) teams. Now, BPA is solving that problem. Predictive insights based on quality data help CFOs make proactive, data-backed decisions instead of reacting to insights that are already outdated by the time the data rolls in.
AI, BPA, and Predictive Analytics
With the rise of artificial intelligence (AI), companies are starting to see both advantages and risks to incorporating AI into financial processes. One global survey of executives showed that 73% of those who have already extensively integrated AI into their business models “believe they are gaining a strategic advantage” by using AI.
Certain AI tools can help your company gain a strategic advantage. Automation software can now feed predictive models that forecast cash flow, budget variance, and revenue risk. Machine learning (a type of AI) helps the predictive models “learn” as you use them to use past and current data for forecasting trends. Over time, your finance team works with the software to develop more accurate models.
Cautions To Keep In Mind
AI tools have made predictive analytics more accessible and less costly than previously. Using tools like machine learning and data mining, companies can implement predictive analytics without having a huge IT budget or a large FP&A staff. As with all types of AI, though, there are risks as well as rewards.
Integrating BPA software helps ensure analytics are built on consistent, high-quality data. One of the big risks with AI is that it can hallucinate inaccurate or fabricated data, so you need to ensure that your predictive analytics begins with quality data. Human fact-checking is also needed. AI can be a powerful tool, but your team should be able to explain how AI was used and where the data came from to improve transparency and trust.
Where To Go From Here
If your company is one of many that’s new to predictive analytics, you can expect the process of developing good predictive models to take time. You’ll need to start by defining your objectives; having a goal for the predictive analysis lets you benchmark the results. Then, it’s time to gather and improve data (here’s where BPA software is a huge help). Next, you develop predictive models and algorithms that will make predictions based on the data (here’s where machine learning and AI come into play). Then, it’s time to integrate the model into your financial systems. The process doesn’t stop there, though. It’s important to try multiple models, cross-validate predictions, and adjust the models as new information comes in.
Here at NextProcess, our process automation software incorporates technology tools, including AI machine learning, to deliver a true end-to-end solution that automates and integrates capital project management, procurement & purchase orders, accounts payable, travel & expense, and payment disbursements. Our software integrates seamlessly with traditional ERPs (including ones like NetSuite that don’t usually support integrations) to supply your company with accurate, timely, and easily accessible data. Contact us today to learn more and schedule a free demo.
