Explainable machine learning in fintech

Machine learning applications balance interpretability and performance. Linear models provide formulas to directly compare the influence of the input variables, while nonlinear algorithms produce more accurate models. Eitan Anzenberg explores a solution that utilizes what-if scenarios to calculate the marginal influence of features per prediction and compare with standardized methods such as LIME.

As machine learning applications become more specialized, the models become increasingly opaque and harder to interpret. The ability to interpret black box nonlinear models is critical in certain fields such as finance, healthcare, and self-driving technology. Flowcast partners with several banks to leverage their proprietary data to build credit-risk models using machine learning, which helps unlock capital for small to medium businesses (SMB). A credit decision requires both an accurate assessment of risk and plain English explanations. For example, it’s not enough to reject a potential client but to give a reason. Eitan Anzenberg explores a solution that utilizes what-if scenarios to calculate the marginal influence of features per prediction and compare with standardized methods such as locally interpretable model estimation (LIME). At each prediction, Flowcast calculates the marginal impact of each feature independently to the response variable. The solution compares this approach to standardized methods such as LIME and reports the computational efficiency and accuracy of explanations. Flowcast then develops an accompanying plain English explanation, such as, “Client A is rejected because their months since most recent diluted payment is 2 (1.8 above median), and the USD amount requested is $72K ($57K above median).” These explanations are required for compliance and help build trust with Flowcast’s banking partners’ credit risk officers.