Steve Carroll

The through-line

Across every appearance, Steve Carroll returns to a single problem: the rental application process is broken in two directions at once, allowing too much fraud through while also being too slow and too subjective for the people going through it honestly. His answer, expressed consistently whether he is describing Findigs' origin story or its current product philosophy, is that screening needs to become more holistic (looking at identity, income and behavior together), more automated and standardized (so rules apply the same way to everyone), and faster, without any one of those goals being sacrificed for another [5][6][8]. What has shifted over time is scope: he started by validating a very narrow rent-payments idea in New York bars and ended up building infrastructure that operators, screening vendors and even competitors now discuss together as an industry-wide fraud and bad-debt problem [1][2][7].

On fraud driving bad debt

Carroll treats rental application fraud not as a marginal nuisance but as a structural driver of landlord losses. He cites that roughly 25% of all evictions are attributable to rental application fraud, describing a pattern in which fraudsters use stolen or fabricated information to secure a lease and then deliberately wait out the eviction process before being removed [5][6]. This is the throughline he brings into the broader industry conversation about fraud and bad debt: the two are connected but not the same thing, and understanding how fraud converts into bad debt is necessary for operators trying to fix either problem [1].

On building a more holistic underwriting process

His proposed fix is comprehensive identity verification paired with underwriting that weighs income and behavior rather than a single score or document check [5][6]. He argues screening should look at behavioral analytics as a genuine signal for identifying fraud, not just a supplementary tool, and that operators should be analyzing delinquencies early so screening criteria can actually be evaluated and improved against real outcomes rather than assumptions [8]. Getting reliable income data, he notes, isn't a problem one integration can solve: it requires relationships with multiple verification providers, not reliance on a single source like Plaid [8].

On the limits of credit scoring

Carroll sees a specific mismatch in how credit is used in tenant screening: current screening approaches lean on credit scores built to predict debt repayment, when what actually matters for a landlord is the likelihood someone pays rent. He frames this as an open opportunity rather than a solved problem, arguing credit screening could and should be re-oriented to predict rent-payment behavior specifically [8].

On fairness and removing bias

Alongside catching fraud, Carroll frames fairness as a co-equal design goal. He describes Findigs' purpose as giving property managers tools to make more informed and equitable leasing decisions, standardizing qualification and underwriting so the same rules apply consistently to every applicant rather than being interpreted case by case [5][6]. He connects this to where he expects regulation to go, anticipating that tenant screening rules will keep evolving with a continued focus on fairness and consumer permission over how applicant data is used [5][6].

On balancing security with a simple application

Carroll identifies the tension multifamily operators face between building strong fraud protection and keeping the application process low-friction for legitimate renters as one of the live problems in the space, engaging with it directly alongside other operators and vendors [2]. This mirrors a point he makes about product design at Findigs: an easy application process is treated as the end goal of screening, not an afterthought bolted on once fraud checks are satisfied [8]. In his account, speeding up landlord application processing and reducing fraudulent submissions are the same product problem, not two separate ones [4].

On building Findigs as a vertical SaaS company

The company's origin, in his telling, came from deliberately cheap, direct validation: sending his co-founder into New York bars to ask strangers how they paid rent, gathering roughly 1,000 survey responses through a Typeform, and using that to justify building a niche product for the rental market [7]. The real pivot, though, came from listening past the intended end users. Findigs began as a consumer rent-payments app, but brand partners who wanted access to renters at the point of move-in revealed a bigger, adjacent pain point in leasing itself, and Carroll shifted the business to a B2B vertical SaaS leasing platform combined with an ML-powered tenant screening engine [7]. He treats this as a general lesson: the strongest niche SaaS ideas often come from adjacent stakeholders raising problems, not from the original core user base [7]. On go-to-market, he differentiated Findigs in a crowded vertical software market by integrating alongside operators' existing tools rather than trying to replace them, a strategy that took the company to $2M ARR serving 60,000 units with a 12-person team [7].

On technology's role versus human connection

Even as he builds automation into leasing and screening, Carroll resists the idea that technology should remove people from the process entirely. He argues that while technology plays a crucial role in real estate, face-to-face interactions and personal connections should not be eliminated from it [5][6], positioning Findigs' automation as a way to standardize and speed up decisions rather than as a replacement for human relationships in leasing.

Takeaways

• Roughly 25% of evictions trace back to rental application fraud, with fraudsters using stolen information to lease and then waiting out eviction [5][6].
• Screening should combine identity verification with a holistic read on income and behavior, not rely on a single check [5][6].
• Credit scores built to predict debt repayment are the wrong proxy for rent-payment likelihood, and screening should be re-oriented toward the latter [8].
• Reliable income verification requires multiple provider relationships, not dependence on one integration like Plaid [8].
• An easy application process should be designed as the end goal of screening, not traded off against fraud protection [8][2].
• Findigs' pivot from a consumer rent-payments app to vertical SaaS leasing platform came from listening to brand partners' adjacent needs, not just core users [7].
• Findigs differentiated in a crowded market by integrating alongside operators' existing tools instead of replacing them [7].
• Standardizing underwriting so rules apply consistently to every applicant is framed as central to fairness, alongside an expectation that regulation will keep pushing toward fairness and consumer data permission [5][6].