The author suggests that predictive analytics can revolutionize accounts receivable management by providing unprecedented insights into strategy and finances. It can help businesses tap into new data sources, optimize customer risk assessment and credit control, prevent late payments, and improve collection strategies. The article also provides actionable steps for implementing predictive analytics in accounting, including saving and integrating data, building the right team, and continuously monitoring and refining the models.
Key takeaways:
- Predictive analytics can revolutionize accounts receivable management by providing unprecedented insights into strategy and finances.
- Artificial intelligence can help businesses process large volumes of data to predict future phenomena like customer payment behaviors, credit risk or cash flow forecasts.
- Implementing predictive analytics requires a team with the proper skill set, including data analysts, data scientists and domain experts in accounts receivable and credit management.
- Making financial services more efficient through predictive analytics can give businesses a competitive edge, helping to mitigate late payment risks, forecast accurate cash flows and improve decision-making related to credit.