Many small businesses need a little financial help; some need a lot. Whether a business is just getting started, wants to expand, or simply needs help getting through a slow period, access to funding can make the difference between a successful small business and a business that has to close its doors. And yet the process of underwriting a small business loan is often not cost effective for traditional lenders. As a result, many small businesses struggle to get financing.
Intuit is a steward of its customers’ data, and its goal is to use that data to help its customers. One of the ways in which Intuit does so is by using the financial information its customers enter into QuickBooks, Intuit’s small business accounting software product, to help them get financing. Diane Chang explains how a small team used big data to turn a 70% loan rejection rate into a 70% acceptance rate and solve a critical problem for small businesses, sharing how the team used lean experimentation, innovative team structures, and leading-edge data science techniques to overcome obstacles that had thwarted previous attempts to help small businesses secure the funding that enables them to thrive.
Diane is a distinguished data scientist at Intuit, where she powers the prosperity of consumers and small businesses with machine learning, behavioral analysis, and risk prediction. Previously, Diane was at TurboTax, looking at the effectiveness of its digital marketing campaigns, understanding user behavior in the product, and analyzing how customers get help when they need it; helped launch QuickBooks Capital, predicting outcomes for loan applicants; worked for a small mathematical consulting firm and a startup in the online advertising space; and was a stay-at-home mom for six years. She applies AI and ML techniques to security, risk, and fraud. Diane earned her PhD in operations research from Stanford.
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