Bureau companies often have sparse data on small businesses, and SME Lenders struggle to make sense of incomplete and disparate data. To make things more difficult, bureaus often cannot provide the proper level of confidence that the data is accurate; or assure that the data belongs to the business being inquired upon. This makes underwriting and account reviews manually intensive and challenges the ability to create cost efficiencies as an SME Lender attempts to scale. Another reality is that many SME business units are aligned within their company’s consumer vertical, making it a challenge to secure the technical resources needed to support agility. SME leaders struggle to tell an ROI story that can equal that of a consumer ROI business case given the difference in volumes, making it easy for companies to deprioritize SME initiatives in favor of consumer projects. Without a proper technology commitment, SME business units cannot efficiently launch new products, test new data, partners and strategies. SME Lenders are often forced to “make their needs work” within infrastructures and capabilities designed for consumer processes that don’t allow for the complexity of, or the inherent differences between consumer and SME transactions. This includes common issues such as: differing bureau results; more complex identity resolution processes; managing multiple borrowers; evaluating entities with multiple names and locations; personal versus business guarantors etc. SME Lenders are falling behind in their ability to use AI and Machine Learning as they lack easy access to the available universe of data necessary to feed AI driven model development. Even though bureaus have a deep data set of elements and attributes, legacy systems and consumer infrastructures often
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