Johnnie Ball is chief data scientist at Fluidly, a company that applies machine learning to transaction data from cloud accounting packages and bank accounts to automate the forecasting, optimization, and protection of cashflow. Johnnie spent his early career as an interest rates trader at an major investment bank before deciding to return to academia to study computational statistics and machine learning at UCL. Over his subsequent career, Johnnie has been a data scientist at a hedge fund and an energy company and a cohort member at deep tech startup accelerator Entrepreneur First.
According to the Office of National Statistics, 80–90% of UK businesses that fail do so due to poor cashflow, and over half of SME owners describe late payments as a severe pain point. SMEs spend over 1,300 hours per year chasing payments, and the business activity finance teams spend most of their time on is cashflow management.
One way to solve the cashflow problem is to tap into the vast amount of financial data that is now available through bank and accounting APIs. Fluidly is applying data science and machine learning techniques to this data to define a new approach to financial analysis. Fluidly can now accurately forecast the cash position of any small business within seconds.
Johnnie Ball explains how Fluidly’s data scientists have rebuilt financial forecasting from the ground up and shares the challenges faced and the lessons learned along the way. Johnnie concludes by exploring the opportunities that are opened up by automating highly manual modeling approaches, both in cashflow and other commercial contexts.