Artificial intelligence (AI) and machine learning (ML) are no longer emerging technologies.
The innovations have officially arrived, and they are already having an impact around the globe – particularly within payments and finance.
“There’s a lot of data science magic that goes behind the simple act of giving credit to our customers in our markets,” Kelly Uphoff, chief technology and product officer at Tala, told PYMNTS.
“By coupling new and novel data sources with machine learning and AI, we provide the ability to detect creditworthiness where others can’t,” Uphoff explained.
Utilizing AI and ML to build proprietary financial identities for customers in emerging markets is revolutionizing the lending landscape, acting as a catalyst for financial inclusion for those ignored or stymied by traditional financial institutions that rely on existing credit scores and other legacy methods.
“People’s financial lives, and in particular the financial lives of our customers, are very personal and they’re very nuanced,” Uphoff said. “We cannot just use machine learning and AI to optimize for a transaction…the product has to [build a long-term relationship with them.]”
And when working with the “global majority” of people who want credit but can’t find it, Uphoff noted that the FinTechs and lending platforms that have the ability to innovate continuously will enjoy the commensurate competitive advantage.
“We are the first FinTech to adapt and tailor Metaflow, which is a machine learning platform that was developed and open sourced by Netflix,” explained Uphoff, who came to her current role after 10 years leading data and ML teams at Netflix.
“We are really pioneering the intersection of causal inference and machine learning in the FinTech space,” she added, noting that this approach represents a key “scientific unlock” for allowing both a truly personalized financial service and enabling relationship building over time.
“Metaflow, what it really allows us to do is to bring in all these very disparate structured and unstructured data sets and adapt, almost in real time, as we gain new insights about our customers and also adapt to a very rapidly changing data environment,” Uphoff said.
But while AI plays a crucial role, Uphoff stressed that success within emerging market lending lives and dies by a truly personalized understanding of the person on the other end of the credit relationship.
“We’re not just paying lip service to those things; our technological advancements are helping enable them — this idea of truly personalized relationship building financial services,” she said.
The focus on understanding customer pain points and building solutions that add real value to their lives is essential.
“The big data space has a lot of shiny objects…we don’t want to build something fancy and then look for a problem to solve. The key to a value-add technology is being mission driven and an emphasis on finding customer pain points first, identifying them and then building the right solution,” Uphoff said.
Uphoff referred to this approach of emphasizing the importance of aligning technology with the end goal of enhancing customers’ lives as “mitigating concept risk.”
“At the end of the day, customers don’t really care about the technology — they just want credit when they need it, how they need it, and they want to be treated with trust and fairness,” she said.
Still, AI in finance raises concerns about data privacy and security.
Uphoff acknowledged the challenges and emphasized the importance of committing to being excellent stewards of customer data, as well as supplementing compliance efforts with proactive engagement with regulators to co-create privacy frameworks that balance financial inclusion with consumer protection.
These efforts should also be grounded in customer consent and transparency around the use of data the value delivered in return.
Uphoff explained that she also believes in the symbiotic relationship between humans and AI. Instead of replacing humans, AI should unlock human ingenuity by handling repeatable tasks, allowing human experts to focus on more nuanced customer insights, hypothesis generation and strategic thinking, she said.
This collaborative approach harnesses the strengths of both AI and human capabilities and can establish an environment where AI’s potential to anticipate customers’ needs more quickly and adapt to their rapidly changing environments can be leveraged to foster financial inclusion.
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