The Story of Stratyfy: Building the Future of Transparent AI in Financial Services

Sometimes the most impactful companies are born from personal frustration. For Laura Kornhauser, the spark that would eventually become Stratyfy came from an unexpected credit card rejection.

In a recent episode of Category Visionaries, Laura shared how this experience opened her eyes to a fundamental problem in financial services. “Being rejected for a credit card that I applied for, a credit card that was heavily marketed to me… really exposed me to the folks that are inaccurately measured by more traditional ways of risk evaluation,” she explains.

While that credit card rejection wouldn’t significantly impact her life, Laura recognized a broader issue. “For many other folks that received these rejection notices or turned down by one or multiple financial institutions, it has a deep impact on their opportunity set and their path going forward.” This realization, combined with her decade of experience at JP Morgan Chase in lending and risk roles, led her to spot a crucial gap in the market.

The mission became clear: help financial institutions better understand their customers’ true risk while expanding financial inclusion. As Laura puts it, “We see that as two sides to the same coin. We can’t do the first, that is move the needle on financial inclusion, if we don’t do the second, managing and mitigation of risk.”

Starting with credit risk assessment, Stratyfy took an unconventional approach to AI. Rather than creating another “black box” solution, they developed technology that keeps “the human in the driver’s seat.” This approach resonated particularly well in financial services, where decision-makers need to understand and trust the technology they’re using.

The company’s evolution has been guided by careful attention to customer needs. Their initial credit risk solution naturally expanded into bias detection when they noticed how customers used their fairness metrics. As Laura explains, “We always saw fairness as a key performance indicator that should be evaluated right alongside expected financial performance.”

This led to their second product, Unbiased, which helps lenders proactively test for unfair bias. The expansion continued into fraud detection, where their transparent approach to machine learning proved particularly valuable because “fraud experts can spot emerging trends or emerging threats faster than you have enough data for a machine to find it on its own.”

The results of this customer-centric approach have been remarkable. Over the past year, Stratyfy has seen a “400% increase in number of customers” and has nearly doubled their team size. But what’s perhaps most impressive is how they’ve maintained their mission focus while scaling.

Looking ahead, Stratyfy’s vision extends far beyond their current success in financial services. They’ve already successfully piloted their technology in healthcare and insurance, industries where, as Laura notes, “the decisions really matter. They really matter on people’s lives.”

The goal is ambitious but clear: expand access to fairly priced credit and then move on to other financial products. Laura shares, “If I step back and I look three to five years out, we will have made a significant impact on access within credit.” This includes initiatives like their partnership with Beneficial State Foundation, where they’re working with 20 lenders to expand access to loan products.

But perhaps most intriguingly, Laura sees Stratyfy’s approach to transparent, human-centric AI as a blueprint for other industries where critical decisions impact people’s lives. By maintaining their focus on making machine learning both powerful and understandable, they’re showing that it’s possible to harness AI’s capabilities while keeping human expertise at the forefront of important decisions.

For founders watching Stratyfy’s journey, it offers a powerful lesson: sometimes the most successful companies don’t just solve technical problems—they bridge the gap between technological innovation and human understanding, creating solutions that work with people rather than trying to replace them.