The smart dashboard using AI to transform SME lending

Tech development designed to tackle 60% rejection rate in SME loans applications

The smart dashboard using AI to transform SME lending



There is now a new way for rejected SME loan applicants to source the capital they need for their business, following the launch of a new dashboard that transforms undesirable loan candidates into viable applicants.

The LendingScore dashboard uses Artificial Intelligence (AI) and big data to pinpoint how a loan application should be modified to increase the odds of receiving funding.

LendingScore works by reducing risk miscalculations and creating a new market share, addressing the leading reason why SMEs are rejected for loans: application errors or improvable shortcomings on financial profiles.

“Across the UK, US, and Australia, more than 50% of SMEs are denied a loan, and, per lack of industry transparency, never receive feedback about why their request was turned down,” said CEO and co-founder of Lending Express, Eden Amirav.

“This means essentially that 20.4 million businesses are inaccurately assessed for funding viability, leaving an enormous amount of potentially viable businesses out in the cold by both traditional and alternative lenders,” he added.

LendingScore comprises four components: the SME personal dashboard; Match Score, to pair borrowers with lenders; a personal step-by-step plan for each SME; and a fundability prediction function.  

“With the launch of LendingScore we are leveraging AI-driven tech to turn undesirable loan candidates into viable applicants, transforming the alternative lending space by decreasing ‘risk misconceptions’ and creating a new market share,” Amirav continued.

Since launching in Australia in 2016, Lending Express has facilitated more than $40m in loans. With the LendingScore dashboard, the firm can now support rejected applicants to source the finance they need by addressing the common reasons for rejection. These often include an incompatible choice of lender; low credit score; tender business age; insufficient revenues; existing debt; prohibited industries; and technical application errors such as incorrect details or input errors.

“What is problematic is that business don’t know or understand the reasons for being rejected, and therefore don’t know what to fix. Instead of abandoning these customers, we are using AI to pinpoint trouble areas and personalise the funding process. Each business is judged on individual, empirical data, allowing it to address its unique shortcomings to get access to the funding it deserves,” Amirav explained.



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