After navigating over $100K in personal debt, Anishek Kamal founded DebtZero to help others do the same—with empathy, AI, and smart solutions.
When Anishek Kamal moved from a small town in India to the United States, equipped with a computer science degree and a job offer in tech, he thought he was about to live the American dream, until reality set in. Without the kind of financial safety net that some immigrants from more privileged backgrounds have, he quickly found himself struggling to afford life in America. He still had to pay off his student loans, cover rent and bills, and manage a car loan, all while sending money home to support his family. Soon, he maxed out his credit cards, and by 2022, he had racked up over $100,000 in debt.
Despite the shame that often comes with debt, and money being a taboo topic, Kamal is open about his past struggles. Through extensive research and data analysis, he found a way to become debt-free.
“Debt is not just a money problem—it’s also a behavioral problem,” Kamal says.
It’s his vulnerability in speaking from personal experience, combined with technical knowledge, that makes his story unique.
That technical knowledge came from years in the data and cloud space, where Kamal built his career at companies like Lululemon, Press Ganey, and Neal Analytics before eventually landing at Microsoft. There, he has led AI and big data initiatives, helping Fortune 500 clients optimize systems and migrate to the cloud. But behind the scenes, he was still grappling with personal debt and beginning to ask deeper questions about why smart, capable people like himself were falling through the cracks of traditional financial services.
He tried everything—Excel spreadsheets, financial advisors, loan and refinancing advice, and countless apps that offered generic tips. It was overwhelming, and he found no clear, data-driven way to understand his options and make better decisions.
That’s what led to DebtZero, the fintech platform Kamal founded to help people get out of debt faster and with less mental strain. By analyzing spending patterns, income fluctuations, and behavioral habits, the platform builds personalized repayment plans and allows users to automate payments in one place. No more juggling multiple apps, due dates, or interest calculations. Kamal interviewed over 350 people and found that 73 percent had experienced the same kind of cognitive overload he had. He recognized there was a clear need for what he was building. DebtZero promises not only to help users save money but also to offer the peace of mind that comes from finally feeling in control.
Outside of his full-time job at Microsoft and building his app, Kamal also makes it a priority to give back. He has mentored over 400 aspiring data and cloud professionals, taking countless calls for free to help people break into the field. Many of those he’s advised are now working at top-tier tech companies.
The Urban Watch sat down with Kamal for an exclusive interview about how his personal experience with debt led him to found DebtZero, how AI can be built with empathy, and what it takes to design a system that truly works for people, not against them.
Tell me a little bit about your background and where you grew up. How has that shaped who you are today, personally and professionally?
I grew up in a small town, Darbhanga, in the state of Bihar, India, where resources were limited, but the people worked hard to create a better life. My father worked incredibly hard to provide for our family, and that work ethic definitely rubbed off on me. We didn’t have much exposure to technology growing up. I remember the first time I saw a computer; I was in elementary school, which was pretty late compared to kids in more developed areas. My uncle had a PC, and the entire family was using it. I was so fascinated by computers that by the time I was in ninth grade, I was assembling computers for friends or acquaintances for a couple of hundred Indian rupees each.
This background shaped me in a few important ways. First, I developed a deep appreciation for education as a path to opportunity. Second, I learned to be resourceful and solve problems with whatever tools were available. And third, I gained this perspective that technology isn’t just cool – it’s transformative. When I finally got my hands on a computer and started coding, it felt like discovering a superpower.
Professionally, this journey has made me particularly drawn to projects that democratize access to resources and knowledge. I think that’s part of why I’m so passionate about fintech now – it’s about using technology to solve real problems and create more equitable access to financial tools that were previously available only to the privileged few.
How did you become interested in working in tech?
My journey into tech started with pure fascination. I remember writing my first lines of code and being amazed that I could create something functional from nothing but logical instructions. It felt like a perfect blend of creativity and problem-solving.
I started as a big data developer working with Hadoop, which exposed me to the power of handling massive datasets. What kept me in tech was seeing how these skills could be applied to solve increasingly complex problems. As I moved into cloud technologies and solution architecture, I loved the challenge of designing systems that could handle enormous scale while remaining resilient and efficient.
The evolution from writing code to architecting solutions for Fortune 500 companies was natural because I’m fundamentally driven by impact – how can we use these powerful tools to solve meaningful problems? That’s ultimately what drew me to AI as well. It’s not just about the technology itself, but about the problems we can solve with it that weren’t solvable before.
What has been the most significant personal challenge you’ve ever faced?
Moving to the United States was probably my biggest personal challenge. I came here with essentially maxed-out credit cards, student debt from my bachelor’s degree, tremendous uncertainty, and the feeling of having to start completely from scratch. There’s something humbling about leaving behind your support network and familiar surroundings.
The financial pressure was intense – I remember calculating exactly how many months of my new salary would go toward paying off those cards. That experience of carrying debt while trying to build a new life taught me a great deal about financial stress and how it affects every aspect of life and decision-making.
Looking back, this challenge provided the perspective that eventually led to DebtZero. I experienced firsthand how even someone with a good education and technical skills can struggle with debt and financial navigation. It made me realize that financial systems aren’t designed with accessibility or empathy in mind, and that’s something technology could help address.
What do you consider your biggest professional accomplishment so far?
While there are several technical achievements I’m proud of, I believe my most significant professional accomplishment has been successfully transitioning from a solutions architect at a major tech company to a founder building something from scratch.
The shift required completely different skills–moving from implementing technical solutions within established frameworks to identifying market opportunities, validating ideas, building from zero, and creating something that genuinely helps people.
What makes this particularly meaningful is that I’m not just applying technical skills, but actually addressing a problem I experienced personally. Creating a product that can help millions of people achieve financial freedom is infinitely more satisfying than any technical problem I’ve solved previously.
How does DebtZero compare to other debt management companies? What makes it stand out?
Unlike traditional debt management companies that focus primarily on debt settlement or consolidation, Debtzero takes a more holistic, technology-first approach to debt freedom. Other companies typically negotiate with creditors after accounts have gone delinquent; we’re focused on optimization strategies that work with current, good-standing accounts.
There are three key differentiators that make us stand out:
First, our approach is personalized based on behavioral patterns, not just numbers. Traditional services apply the same mathematical strategies to everyone, but financial success is behavioral. We analyze spending patterns, income volatility, and psychological factors to create strategies that users will actually stick with.
Second, we’re focused on education and empowerment rather than dependency. Many debt management companies create ongoing reliance on their services, while we’re building tools that increase financial literacy and independence over time.
Third, we use technology to optimize the entire financial picture, not just debt. Our system analyzes cash flow patterns, payment timing effects, and opportunities across all financial accounts to identify savings that traditional approaches may overlook.
What inspired you to launch DebtZero?
Launching DebtZero came from a deeply personal place. When I moved to the U.S., I was carrying student loans and quickly found myself in credit card debt. The financial system here was completely different from India’s more cash-based approach—everything revolved around credit. Despite having a tech background and a decent job, I struggled to make sense of it all. I was making minimum payments, juggling multiple loans, even sending money back home to support my family. By 2022, I was over $100,000 in debt.
I tried everything—Excel spreadsheets, financial advisors who weren’t interested unless you had assets, random advice on 401(k) loans and refinancing—it was overwhelming. What I really needed was a clear, data-driven way to understand my options and make better decisions. There was an app in India that helped track and pay credit card balances in one place, and I couldn’t find anything like that here. That’s when I got the idea for DebtZero.
The final catalyst came during my time working on a credit union project, where I analyzed millions of financial transactions. I observed patterns of people making costly mistakes not because they lacked discipline, but because they lacked visibility and optimization tools.
I recognized an opportunity to apply my technical expertise in data and AI to help people overcome the same challenges I had faced. The more research I did, the more I realized there wasn’t a solution that truly leveraged technology to optimize debt management in a personalized way. That gap in the market, combined with the potential for meaningful impact, made launching DebtZero feel like the most important work I could be doing.
What role does AI play in Debt Zero’s platform?
AI is core to Debt Zero’s platform, but in ways that might be different from what people typically think. We’re not using AI to make decisions for users–we’re using it to uncover insights and opportunities that would be impossible to find manually.
First, our AI analyzes transaction data to understand unique spending patterns and financial behaviors. It identifies patterns invisible to the human eye, like how spending in certain categories triggers cascading effects across someone’s finances.
Second, we use machine learning models to predict cash flow volatility and potential financial stress points before they happen. This allows us to proactively recommend adjustments rather than reacting to problems after they occur.
Third, we apply optimization algorithms that consider hundreds of variables simultaneously–payment dates, interest calculation methodologies, cash flow patterns, and psychological factors–to create debt payoff strategies that are both mathematically optimal and behaviorally sustainable.
Fourth, we use natural language processing to translate complex financial concepts into accessible guidance tailored to each user’s financial literacy level.
The AI doesn’t replace human judgment–it enhances it by revealing options and insights that wouldn’t be apparent otherwise, then empowers users to make better-informed decisions about their financial future.
What kinds of data are you analyzing to offer clients the most effective repayment strategies?
We analyze multiple categories of data to create truly effective repayment strategies, all while adhering to strict privacy protections and obtaining user consent.
Transaction data is our foundation—we look at spending patterns, income timing, volatility, and cash flow dynamics. This helps us understand not just how much money someone has, but when it’s available and how predictable their financial situation is.
We also assess debt-specific details, such as interest rates, payment due dates, minimum payment requirements, and how interest is calculated (e.g., daily average balance vs. previous statement balance). These overlooked nuances are critical for optimization.
Behavioral data plays a key role as well—how consistently users stick to plans, where they’ve struggled or succeeded, and the kinds of financial decisions that tend to trip them up.
What makes our approach unique is that we don’t just look at each of these data types in isolation. We examine the relationships between them. For example, how income timing interacts with payment due dates, or how certain spending patterns affect debt payoff consistency.
Importantly, we maintain bank-level security and transparency. Users always know what data we use and why, ensuring trust is built into every step.
Who is your ideal user, and what does a typical customer journey look like on the platform?
You don’t have to be in debt to use DebtZero; it can help you optimize your payments and stay debt-free. However, our current target user is someone who has more than $10,000 in debt and is actively seeking to eliminate it, but feels overwhelmed by the complexity of the process. They usually have a mixture of credit cards, student loans, car loans, and possibly a mortgage. They’re making payments but not seeing the progress they want, and they’re seeking a more effective approach rather than simply budgeting harder.
That said, debt is not just a money problem; it’s also a behavioral problem. That’s why we provide users with educational content specifically tailored to their situation, helping them build financial skills and confidence alongside their progress toward debt freedom.
The typical customer journey begins with what we call a ‘Financial X-Ray’–connecting their financial accounts to provide us with a comprehensive view of their financial picture. This is a low-friction process that takes about 5 minutes but provides immediate value.
Next, we generate their personalized Debt Freedom Plan, showing them exactly how much faster they could be debt-free using our optimization strategies. This is a key moment because most users discover they can cut years off their debt payoff timeline without increasing their monthly payments.
Once they have their plan, we help implement it through a combination of guided actions and automation. This includes optimizing payment dates, restructuring how payments are applied to different debts, and setting up the right automatic transfers.
As they progress, we provide ongoing monitoring and adjustments. If we detect changes in their financial situation, the system recommends modifications to keep them on the fastest path to debt freedom.
Debt is deeply personal and often stigmatized. How do you design AI systems that are both effective and empathetic?
I’m passionate about designing AI systems that balance effectiveness with empathy. In the debt management space, shame and vulnerability are common experiences.
We start by acknowledging that debt isn’t just a financial issue–it’s an emotional one. Our design philosophy centers on judgment-free interaction. The system never uses language that implies moral failing or personal shortcoming when discussing financial situations.
We’ve also built what we call ‘context awareness‘ into our AI. It recognizes emotional cues in how users interact with the platform and adjusts its tone and recommendations accordingly. For example, if someone is showing signs of financial anxiety, the system focuses more on small, achievable wins rather than long-term projections.
Another key aspect is appropriate personalization. The system remembers individual circumstances and preferences, avoiding the need for users to repeatedly explain their situation, which can reinforce feelings of shame.
We’re careful about timing and delivery of information. Financial insights are delivered when users are most receptive, not as interruptions or judgments. And we frame everything in terms of opportunities rather than mistakes.
Perhaps most importantly, we maintain a clear line between what AI should handle and what requires human connection. For particularly sensitive situations, we make it easy to connect with a human financial coach who has the context from the AI system but brings the empathy and nuance that technology alone can’t provide.
The goal isn’t to make our AI seem human, but rather to ensure it respects and accommodates human emotions while providing effective financial guidance.
What steps are you taking to ensure transparency and fairness in automated decision-making, especially for users from financially vulnerable backgrounds?
Transparency and fairness are foundational to DebtZero’s approach, particularly because we work with many financially vulnerable users.
First, we practice radical transparency in how our system works. Users can see exactly what factors influence their debt freedom plan, what data points we’re using, and the reasoning behind recommendations. We avoid black-box solutions where users have to simply trust the AI without understanding it.
We’ve implemented fairness audits throughout our development process. This means regularly testing our recommendations across different demographic groups to ensure our system doesn’t perpetuate biases or deliver better results to certain populations. If we detect disparities, we address them at the algorithm level.
We’re also careful about assumption-free design. Traditional financial systems often make assumptions about income stability or access to banking services that don’t hold for many vulnerable populations.
Our systems are designed to work effectively for gig workers with irregular income, users with thin credit files, or those who’ve had negative banking experiences in the past.
We provide options, not mandates. Our AI presents multiple paths forward with clear explanations of the tradeoffs, empowering users to make informed choices rather than forcing them into a particular approach.
And finally, we maintain human oversight of our systems, particularly for edge cases. When our AI encounters situations outside its confident decision-making parameters, especially for vulnerable users, those cases are reviewed by humans to ensure appropriate recommendations.
The ultimate measure is outcomes–we track whether our recommendations are actually helping users across all demographic groups achieve financial progress, and continuously refine our approach based on those results.
Can people use both a traditional debt settlement company and your app?
Yes, they can. In our first version, users will be able to see all their debts in one place and make payments directly through the app. The focus is on visibility and control.
In future versions, we plan to partner with credit unions to help users access lower-interest loans, compare rates, and understand where they qualify. The goal is to offer smarter refinancing options that work in the user’s favor.
Traditional debt settlement companies focus heavily on negotiation, and while that can be helpful, it can also be inefficient and confusing. We aim to bring that same value, but in a more transparent, automated, and user-friendly way.
Ultimately, our app is designed to help people, not institutions. But to offer the best tools and rates, strategic partnerships will be necessary.
To end on a broader note, as someone who works with AI every day, what do you see as the most pressing challenge or ethical dilemma facing the field today—technically, socially, or economically?
I think the most pressing challenge facing AI today is the growing disconnect between who builds these systems, who benefits from them, and who bears the risks.
Technically, we’re creating increasingly powerful tools without sufficient guardrails or oversight. Many AI systems are deployed with inadequate testing for biases, edge cases, or potential harms. The pace of development has far outstripped our governance mechanisms.
Socially, we’re facing a potential deepening of existing inequalities. AI could either bridge or widen gaps in access to healthcare, education, financial services, and job opportunities. Without deliberate attention to inclusion and equity, these systems will naturally tend to benefit those who already have advantages.
Economically, the concentration of AI capabilities in a small number of large companies raises serious questions about power distribution.
When a handful of organizations control the most powerful AI models, they effectively become gatekeepers for innovation across many sectors.
The ethical dilemma that keeps me up at night is the risk of ‘automation bias’–our tendency to defer to algorithmic recommendations even when we should be skeptical. As AI becomes more embedded in consequential decisions, from medical diagnoses to lending approvals, this bias could lead to a progressive abdication of human judgment and responsibility.
Addressing these challenges requires technical solutions, like better testing methodologies and more transparent systems. But equally important are social and governance innovations–developing new frameworks for accountability, ensuring diverse perspectives in AI development, and creating mechanisms that distribute the benefits of AI more equitably.
Learn more:
🔗 Check out DebtZero
🔗 Connect with Anishek Kamal on LinkedIn🔗 Book a call with Anishek
🔗 Book a call with Anishek
