Lending process for SMEs seen faster with AI
TECHNOLOGY that uses automated decisions on risks brings innovation to fintech and lending platforms, resulting in speedy services to small- and medium-sized enterprises (SMEs), an industry player said.
“Using alternative data sets which are not accessible to SMEs cuts down the time and requirements for the application process,” Bharath Kumar Vellore, Asia-Pacific general manager at risk decisioning platform provider Provenir, said in an interview with BusinessWorld.
Access to formal data remains a key challenge among business owners, Mr. Vellore said, but artificial intelligence (AI) can shorten the loan application process for SMEs to less than 24 hours from 10 or more weeks under traditional lending institutions.
Lenders are provided an automated decisioning platform and integrated data marketplace to build their credit policy as accurately and efficiently as possible today, according to Mr. Vellore.
Credit onboarding and integrations are purely data-driven, pooling from banks’ internal data, application data from the SME, third-party reference data, and alternative data.
Alternative data sources include web behavior on devices, participation in the e-commerce supply chain, and point of sale machines.
“In a traditional lending process, you would see that it is highly rules-driven,” Mr. Vellore said about small business owners having to secure inaccessible data themselves and lenders manually processing applications.
Mr. Vellore noted that financial inclusion amid the lack of access to working capital is a challenge faced by SMEs, adding to the informal parts of the lending process.
According to the World Bank, access to capital is the second most cited obstacle faced by SMEs in growing their businesses in emerging markets and developing countries.
“SMEs are less likely to be able to obtain bank loans than large firms,” the World Bank said. “Instead, they rely on internal funds, or cash from friends and family, to launch and initially run their enterprises.”
Mr. Vellore described the obstacle as a “vicious cycle” as SMEs “take lending from informal channels at very high interest rates.”
AI has powered predictive decisions based on a vast number of datasets to help SMEs where they do not need any prior credit data or fixed collateral, he said.
“We have now gotten very disruptive lenders like fintechs and digital banks, proactively addressing the credit needs of the SME market,” he said. “Over the next couple of years, we’re going to see much more prevalent use of technology with very, very fast loan approvals.”
“Lenders will be embracing digital technology, data, and advanced algorithms like machine learning to simplify and transform the application process,” he added.
Hallucinations and risks must still be noted as the technology grows, Mr. Vellore said. — Miguel Hanz L. Antivola
