This interview is with Daniel Kroytor, CEO at TailoredPay.

Daniel Kroytor, CEO, TailoredPay

Can you introduce yourself and share what makes you uniquely qualified to work in the high-risk lending or payment processing space? What expertise do you bring to helping businesses or borrowers who don’t fit traditional approval criteria?

I’m Daniel Kroytor, and I’ve spent my career in the payments world, mostly working with merchants who sit outside the usual approval box. My background spans merchant acquiring, underwriting, and payment security, and I’ve supported companies in travel, SaaS, and e-commerce as they grew from early traction to tens of millions in processing volume. What sets me apart is that I understand the real friction high-risk businesses face. I’ve managed payment setups where strong risk controls, smarter routing, and tighter compliance were the difference between scaling and getting shut out. My work often involves keeping chargebacks in check, building payment flows that actually hold up under pressure, and making sure merchants can operate without constant interruptions.

I try to strike a balance between growth and compliance, and I spend a lot of time helping founders choose processing structures that protect their revenue while keeping regulators and banks comfortable. I’m also pretty deep in the weeds on payment orchestration and fraud prevention, which tends to matter even more for merchants who don’t fit a standard profile. At the end of the day, I enjoy helping overlooked businesses get the payment support they need so they can run and scale safely. If a merchant has a complex model or a higher risk profile, that’s usually where I do my best work.

What was the pivotal moment or experience in your career that led you to specialize in high-risk, quick-approval financial solutions? Walk us through how you arrived at this niche.

The moment that pushed me toward high-risk and quick approval work came earlier in my career, when I was underwriting for a portfolio that included travel and subscription-based merchants. These were good operators with solid business models, but they kept running into long approval cycles, sudden account holds, and processors who didn’t understand their risk profile. I saw how much revenue they were losing simply because no one wanted to take the time to build the right structure for them.

One case in particular stayed with me. A fast-growing travel merchant had clean books, strong demand, and a solid plan, yet every traditional provider treated them as if they were one step away from a shutdown. I stepped in to reorganize their payment setup, rebuild their risk controls, and create a processing path that banks could actually support. Their volume unlocked almost immediately. Seeing the difference that made for them made something click for me.

From that point on, I realized there was a real gap in the market for merchants who operate in higher-risk models but still run responsible businesses. They needed someone who could speak the language of banks and processors, understand the pressure these merchants face, and move quickly without cutting corners. That experience is what set me on the path I’m on today.

Most lenders avoid high-risk clients because of potential losses, yet you’ve built a business around serving them. Can you share a specific example of a client who seemed too risky on paper but became a success story? What did you see that others missed?

One example that stands out is a subscription-based wellness brand that looked unworkable to most lenders and processors. On paper, they checked every box that usually gets an instant decline. Recurring billing, high chargeback exposure, fast customer acquisition cycles, and a young team with limited history. By the time they reached me, they had been turned away so many times they were ready to give up on scaling.

What I noticed was something that doesn’t show up in a standard file. Their customer retention was stronger than average for the category. Their refund patterns were predictable rather than chaotic. Their support team kept excellent records, and their onboarding flow had fewer friction points than most early-stage subscription companies. In other words, they had real signals of stability even though their industry flagged them as risky.

I worked with them to reshape their billing approach, tighten their dispute controls, and present their data in a way that gave acquiring partners actual confidence instead of assumptions. Once their processing was set up correctly, their performance validated everything we had seen beneath the surface.

They ended up growing into one of the most reliable merchants in that vertical. What others missed was that risk is rarely about the label. It’s about the story behind the data, how the business behaves day to day, and whether the team is disciplined enough to manage payments the right way.

Quick approvals and thorough risk management often seem at odds with each other. What’s one system or process you’ve implemented that allows you to evaluate applications rapidly without compromising on due diligence?

One system that has made a major difference for me is a structured intake framework that pulls the right signals from a merchant within the first few minutes instead of after days of back and forth. It is not a shortcut. It is a way of collecting the pieces that actually matter for high-risk profiles so the deeper review can start immediately.

I focus on three buckets. Operational behavior, historical patterns, and technical setup. In practice, that means getting a quick look at how they bill customers, how they handle disputes, what their refund rhythm looks like, and how their checkout flow works. These items say more about true risk than long questionnaires or generic checklists.

Once this information is captured, the rest moves fast because the analysis is already grounded in the real mechanics of the business. The compliance review still happens, the underwriting still happens, and the bank still gets everything it needs. The difference is that the first assessment is rooted in signals that predict performance, which removes a lot of the unnecessary friction.

This approach keeps approvals quick while making sure nothing important gets overlooked. It lets me move at the pace merchants want without giving up the depth banks expect.

You’ve mentioned working with clients who have non-traditional credit histories. Can you walk us through a real situation where you approved someone based on compensating factors rather than credit score alone? What specific factors tipped the scale?

One situation that comes to mind involved an e-commerce founder who had a thin credit file and a past default from several years earlier. On paper, most lenders would have stopped right there. The business itself told a different story. Their sales were steady, their chargebacks were low for the category, and their cash flow showed clear month-over-month improvement.

The compensating factors became obvious once we looked past the score. They had consistent order volume with no major spikes that would raise flags. Their refund patterns were controlled, and their customer service logs showed quick response times and clean communication. They also had a reserve already built from previous processing, which signaled responsibility even without perfect personal credit.

What tipped the scales was the combination of discipline in their operations and the financial habits inside the business. The credit score alone didn’t reflect how well they were running things day to day. By grounding the approval in performance data rather than a single number, we were able to support them safely, and they went on to scale without any major risk events.

Different industries carry different risk profiles—what’s one industry that surprised you in terms of either being easier or harder to underwrite than you initially expected? What did that experience teach you about tailoring solutions to specific sectors?

One industry that surprised me was online education. At first glance, it seems straightforward. Digital delivery, predictable billing, and clear records. Once I got deeper into it, I realized it can be harder to underwrite than many people expect. The risk is not tied to fraud or shady behavior. It comes from student expectations, refund cycles, and chargeback patterns that move in waves rather than steady lines.

What that taught me is that every sector has its own rhythms. Two companies with the same processing volume can behave completely differently depending on how their buyers think and how the product is consumed. In online education, the key signals are engagement, completion rates, and support quality. When those items are strong, the merchant ends up being far more stable than the category suggests.

That experience pushed me to stop relying on broad industry labels and focus more on how a business operates within its niche. Tailoring the structure to that specific behavior is what keeps both the merchant and the bank comfortable.

Every high-risk lender eventually faces defaults or problem accounts. Can you share a time when an approval went wrong, and what changes you made to your risk assessment process as a result? How do you balance learning from mistakes without becoming overly conservative?

There was a case early on where I approved a fast-growing subscription merchant whose numbers looked solid on the surface. Their chargebacks were low, their revenue was climbing, and their credit file was clean. What I missed was how much of their growth came from aggressive promotions that pulled in a lot of short-term customers with very little intent to stay. The spike in cancellations arrived about sixty days later, followed by a wave of disputes that hit all at once.

It was a clear miss on my part. The lesson was that strong volume and clean metrics are not enough if the underlying customer behavior does not support long-term stability. I added two items to my process after that. First, a closer look at customer cohorts so I can see how buyers behave after the first billing cycle. Second, a deeper review of marketing practices to understand whether the growth is durable or just a temporary surge.

Learning from that mistake did not make me cautious to the point of saying no to everything. It just made my first pass more focused on signals that predict future behavior rather than only relying on the current snapshot. That balance is what helps me avoid repeating the same error while still giving good operators a fair chance.

Low rates in a high-risk environment seem counterintuitive. How do you structure your pricing and risk management to offer competitive rates while still protecting your business? What’s one creative approach you’ve used to make this work?

Competitive rates in a higher-risk setting only work when the structure supports them. One approach that has worked well for me is building pricing around actual performance signals instead of broad category labels. Instead of assuming a merchant needs a high rate because of their industry, I look at their cash flow rhythm, dispute patterns, billing setup, and customer retention. When those items trend in the right direction, the rate can stay lower without putting the portfolio at risk.

A creative method I have used is a stepped model tied to early performance. The merchant starts with a rate that is competitive, and a reserve that reflects their initial profile. As the data comes in and they show consistent dispute control and responsible billing behavior, the reserve shifts and the effective cost drops. This gives them room to grow while letting me keep protection in place during the first few cycles.

This setup works because it rewards good operators, encourages healthy habits, and keeps the downside contained. It lets me stay competitive without ignoring the realities of higher risk segments.

Looking at where the high-risk lending landscape is heading, what’s one piece of advice you’d give to other financial professionals who want to serve underbanked or high-risk clients? What mindset shift or operational change would you recommend based on your experience?

The biggest advice I would give is to stop treating high-risk or underbanked clients as a category and start treating them as operators with very different behaviors. Too many financial professionals rely on labels instead of learning how these businesses actually handle customers, cash flow, and disputes. Once you shift your thinking toward understanding the mechanics of each model, the decisions become clearer and far more accurate.

Operationally, the best change I ever made was building my process around real performance signals rather than default assumptions. Look closely at how a merchant bills, how they communicate with buyers, how predictable their refund rhythm is, and whether they keep clean records. These items tell you far more than a credit score or a broad risk tag.

If there is one mindset shift I would recommend, it is this. Approach each client with curiosity instead of caution. Not blind optimism, but a willingness to learn how the business works before making a judgment. That attitude opens the door to better approvals, safer structures, and stronger long-term partnerships.

Thanks for sharing your knowledge and expertise. Is there anything else you’d like to add?

Thank you for the opportunity and for creating this platform; it’s been a joy to use.