Clair

Does adding a financial benefit make your app stickier? The data says yes. When a partner embeds Clair into their app, users trust and like those brands more. We tracked the sentiment, and the results are clear: Over 80% of users say having On-Demand Pay makes their Payroll or WFM app better. It drives engagement (users check the app more often) and loyalty (they feel supported during stressful moments). In this video, I dive into the concept of the "halo effect" of On-Demand Pay - which not only provides a valuable financial tool for people who need it, ODP also enables a better, stickier product for our partners.

At Clair, our product philosophy is built on "fairness." But that principle is truly tested not just when things go right, but also when they go wrong. So we built two features: - For when things go right, we launched a feature that automatically refunds instant transfer fees if an advance is repaid within 12 hours. If you don't get the value, you shouldn't pay the fee. It's that simple. - For when things don't, we built a new Repayment Portal for users who miss a payday. Instead of just locking them out, we give them a flexible, clear path back to financial health. In my new video, I break down both features and how they're key to building a financial system that's on the user's side.

Our product had a flaw: New users would log in on their first day of work, often their most financially stressful moment, and see a $0 available balance. This failed them when they needed us most. We became obsessed with solving this. Our data science team dug in and found a powerful insight: employees enrolled at Clair in their first 20 days of tenure are 30% more likely to get a paycheck than those with a longer tenure. This insight allowed us to build "Day One Dollars," a responsible way to underwrite new employees. We've now helped thousands of workers bridge the gap to their first paycheck. In this video, I explain how this data-driven approach is key to building a truly responsive financial system.

US consumer debt has increased 32% since 2019, rising 6% faster than inflation! Today, there's over $18 trillion dollars in debt held by US consumers. While credit is a necessary tool for people to buy cars, houses and pay for education, we're seeing a fundamental shift from long-term debt (like student loans) to everyday, revolving debt. For a typical hourly worker, short-term debt (credit cards, BNPL) is up 270% since 2019. Why? Because for most Americans, income arrives after expenses. They are forced to use debt as a liquidity bridge for everyday items. This timing problem is solvable with a non-interest and defaults-to-free credit solution connected to the employer. On-demand pay solves this gap by giving workers access to their earned wages as soon as they finish the hours they work. In my new video, I break down this trend and how we're building a responsible alternative at Clair that helps workers use their own money earned instead of piling on new debt.

How will GenAI impact fintechs? Credit decisions must be transparent and explainable - for our users and for our bank partners' regulators. So, does Clair Atlas use GenAI? The answer is yes. We don't use it for the core scoring decision. We do use it for what it's great at: data enrichment. In this clip from our fireside chat, our lead data scientist Veronika Shmagina explains our philosophy: use the right tool for the right job and never sacrifice transparency.

How does Clair’s AI engine decide whether the data we get from payroll and workforce software partners is reliable enough to make pay available on-demand, assuming we want to take near-zero repayment risk? Answer: Think of it like a jury. One juror is good, but thousands are better. That's the simple but powerful analogy our lead data scientist, Veronika Shmagina, uses to explain our proprietary AI model, Clair Atlas. In this clip from our recent fireside chat, Veronika breaks down how Atlas works: a series of "decision trees" that learn from each other's mistakes to get progressively smarter on the rare edge cases of underwriting based on payroll and workforce data. Most importantly, she explains why we chose a model that is highly explainable. For us, trust and transparency aren't features; they're essential. Take a look to see how we're building the future of underwriting, responsibly.