During the recent Private Funds Management CFO roundtable on valuation, the highlighted topic was how machine learning and robotic process automation can improve accuracy and efficiency.

As I highlighted, and the panel agreed, machine learning continues to help build more efficient models producing fewer errors, while necessary human judgment will occur later in the valuation process. 

A&M has already built a credit rating estimator (SCRE) on the firm's machine learning platform (AMMP), using historic corporate financial data which can predict what a rating agency would leverage to rate a company more accurately than any model we have seen (>90% accuracy).  For more details on SCRE and AMMP, Click here.

I also pointed out that increased automation will enable both internal and external analysts, including valuation professionals, to focus on issues that require valuable expertise and judgment. Other panelists agreed with this observation.

For the full Private Funds Management CFO Roundtable article, click the link below.

Panelists included: April Evans, Monitor Clipper Partners; Jon Schwartz, New Spring Capital; Tom Angell, Withum, Smith & Brown; Craig Ter Boss, EisnerAmper; Frederico Jost, BRG; Aryeh Sheinbein, Alvarez & Marsal