Data Markets Take Guesswork Out of Credit Decisions

In today’s challenging macroclimate, many companies are losing resources.

Fortunately, new digital tools and modern lending and financing solutions can help those firms to close gaps and firm up their operations.

But what lies behind the impact of today’s modern tools, and what empowers the underwriting of the ever-more critical lending solutions? Increasingly, it is the ability to access and leverage best-in-class data.

“It is not about having a better UX [user experience] or UI [user interface], those times are over,” Maik Taro Wehmeyer, co-founder and CEO at Taktile, tells PYMNTS. “[Success today] is about having a strong hypothesis on the market and launching a financial product that works to activate that hypothesis in a way that others can’t.”

That’s because at the of the day, the technology that serves the needs of people best — as well as actually makes money — is going to be the one that excels.

“The whole magic around winning as a FinTech at the moment is testing and experimenting on your go-to-market hypothesis with new data sources combined in a way [competitors] aren’t able to do,” Wehmeyer says.

He emphasizes, “We are just at the start of understanding what the vast majority of data sources can do for credit underwriting. … A lot of new, cool things are coming in.”

Particularly as the world runs more and more on automated decisions, financial services businesses including banks, insurance companies, FinTechs, and lenders need to integrate automated decision engines that can help drive profitable growth.

These firms don’t just need their own data to run these engines, Wehmeyer explains, but “they need access to the vast amount of data” being accumulated everywhere.

See Also: Can Non-Traditional Data Sources Close the Funding Gap for Main Street?

Simplifying Data Integrations

Making informed decisions based on real evidence, not speculation, rests on the effective integration of fresh and relevant data.

“There’s not much in the market where new data sources are widely available in order to drive sophisticated credit decisions,” says Wehmeyer.

He adds that while there do exist valuable assets and marketplaces, they “exist in a very vertical way” across areas like know-your-customer (KYC) and fraud.

“Having access to data doesn’t mean you can use it,” Wehmeyer notes. “And once you have that access, the question becomes: What will you use it for?”

Fortunately, the ongoing modernization of payments systems and capabilities, as businesses move away from legacy, often manual back-end processes, is creating more data around operations and providing a rich informational background for lenders to draw upon when underwriting and extending credit to businesses.

“Sophisticated teams use data to define their policies and models, and you need data at the point where you think about the decisions, experiment with the decisions, and try out different data sources,” Wehmeyer says.

Read also: Companies Tap Their Own Data to Drive Efficiencies With AI

In the end, Wehmeyer emphasizes, firms need to try out their solutions in the market to validate their hypothesis.

“You need to try things out and integrate various data sets to understand what works well for your use case — and access to data is changing the game for companies that can now experiment and test in a much faster way to increase the development cycles of their products,” he adds.

Accelerating the speed of that development cycle is the sudden market prominence of generative artificial intelligence (AI) tools, and their ability to create synthetic data sets based on real information.

See Also: Generative vs Predictive AI’s Role Across the Future of Payments

Role of Synthetic Data

As for where synthetic data can play?

Wehmeyer says that it’s during the sandbox phase, when companies are testing their most innovative, future-fit products.

“A lot of things are happening on the testing site before products go to market in the real world, and the ability to create synthetic populations with generative AI is so powerful — you just task the model with creating similar sets based on custom variables, and it allows you to launch much safer in a more robust fashion than you could in the past,” he explains.

Still, Wehmeyer notes that “it doesn’t replace real-time, real-world data,” just helps firms avoid “stupid mistakes.”

It’s about thinking about the product strategy, which needs to be done by a human, but that human needs “to be unblocked and enabled,” which is where decision engines and AI can play an impactful role, he says.