--- title: Inside Bolt: Fighting Fraud in the Gray Zone source_page: https://www.sardine.ai/media/the-saturday-fraud-strategist/episodes/gray-zone-fraud canonical: https://www.sardine.ai/media/the-saturday-fraud-strategist/episodes/gray-zone-fraud format: text/markdown date: 2026-10-24T00:01:00.000Z description: Gray zone fraud is where good customers and opportunists blur. Bolt’s Jaanus Uudmäe explains refund abuse, manufactured labels, and expanding into new markets… --- **Quick links:** [Human page](https://www.sardine.ai/media/the-saturday-fraud-strategist/episodes/gray-zone-fraud) · [Home](https://www.sardine.ai) · [Customers](https://www.sardine.ai/customers) · [Blog](https://www.sardine.ai/blog) · [Demo](https://www.sardine.ai/demo) --- # Inside Bolt: Fighting Fraud in the Gray Zone **Published:** 2026-10-24T00:01:00.000Z Gray zone fraud is where good customers and opportunists blur. Bolt’s Jaanus Uudmäe explains refund abuse, manufactured labels, and expanding into new markets… For a couple of years now, Jaanus Uudmäe’s LinkedIn posts have kept showing up in my feed, and they always carry a practical take on what fraud looks like at Bolt. Bolt is an on-demand marketplace headquartered in Estonia that operates in more than 50 countries, with ride hailing, food delivery, scooters, e-bikes, and car rentals. That means his team sees almost every kind of customer and almost every kind of abuse. Jaanus started as a software developer at Skype in 2007 and landed on the fraud team by accident. He joined Bolt in 2017 as a hands-on analyst when the fraud team had two people, and today he has 14 reporting to him. In this conversation, we spend most of our time in what I’d call the gray zone. Gray zone fraud is behavior where you can’t tell whether you’re looking at a fraudster, a frustrated customer, or an ordinary person who tried a trick a friend told them about. We talk about refund abuse and bonus abuse, why first time customers are the hardest to judge, and how to build labels when no one hands them to you. We also get into how to explain losses to the business when the numbers aren’t clean, and how Jaanus’s team uses AI in a way that surprised me. What you’ll hear in this episode: How Jaanus went from a software developer at Skype to leading a 14 person fraud team at Bolt Why marketplace fraud prevention gets harder in a three sided marketplace with riders, drivers, restaurants, and couriers What gray zone first party fraud looks like in practice, from refund abuse to bonus abuse Why first time customer fraud is the hardest call, and how Bolt decides when to act and when to fall back on standard policy The difference between opportunistic vs professional fraud, and why opportunistic customers make A/B testing unreliable How fraud label manufacturing works when nobody hands you the answers, and how to avoid recursive fraud labeling Why label maturity matters, and which proxy metrics keep a team honest How Bolt thinks about fraud risk appetite and fraud expansion cost when entering a new market How AI works as a context layer for analysts, and why the fraud analyst mindset still matters You should listen to this episode if you: Deal with refund abuse or bonus abuse and can’t prove intent Work at a marketplace or on-demand platform with more than two sides Are launching in a new market and need a way to talk about fraud losses with the business Create your own fraud labels and worry about fooling yourself Want to see how a technical fraud team uses AI for analysis without removing human judgment ### Episode notes & key takeaways Gray zone fraud doesn’t fit the chargeback playbook Chargebacks are the easier problem, because stolen cards leave a clear label and you can run an A/B test to see whether a rule was right. A marketplace adds more problems, since drivers can collude with riders and a food delivery order involves an eater, a restaurant, and a courier. The harder cases are bonus abuse and refund abuse, where someone lies about quality or a missing item and there is no hard evidence either way. Jaanus’s team looks at the history of the customer, the restaurant, and the courier, and links accounts through email, IP address, or device. When they can’t prove anything, they default to the standard policy for that market. Opportunistic customers make this harder, because they act like good customers until a friend shares a trick, and they go back to normal when a rule stops it. My takeaway is that in the gray zone, you don’t need to catch everyone. You need to act where the evidence is strong and let policy handle the rest. Manufacturing labels without fooling yourself Nobody tells you when someone abused a discount. The fraud team has to create the labels. One simple version is to call a new customer good if they later transact at full price, though stopping after the discount doesn’t prove fraud. I raised the risk of recursive logic, where the label and the rule share the same assumption. The results look right because they were built to. Jaanus’s guardrails are customer service ticket volume as a proxy, anecdotal feedback such as a CEO’s friend getting blocked, and a conservative bar, which means only blocking when he could explain the reason to anyone, even his family. He also monitors the system as a whole instead of rule by rule, and he accepts that labels mature slowly, sometimes over weeks. Measuring refund fraud is just as tricky, because denied refunds give you an estimate of savings while false positives stay hidden, since good customers rarely complain. His team works with a range and explains it when the number moves. If you manufacture the labels, you own their quality too. Treat fraud in a new market as part of the cost of expansion When Bolt enters a new market, it goes in light, with a reasonable set of controls like chargeback protection, and then watches what the market does. The losses become part of the expansion cost, and they also give Jaanus concrete accounts to show the business why more rules are needed. When Bolt Food started in Latvia, fraud reached about 5% of revenue even with decent controls, which made the conversation easy. His team warns strongly when a product has loopholes and rarely puts its foot down, but it asks the business to accept the loss if they go ahead anyway. There is no official budget line for this, only an unwritten agreement. I love that it swaps a performance target for a loss budget, and it reminds me that trying to predict every scheme in advance is a losing bet. Rules for what’s new, models for what’s known Bolt’s fraud team is technical and does little manual review, because transaction values are low, a few euros for a ride in some African markets and 20 to 30 euros in the UK. Humans step in when regulations require it, for example when a driver may be involved, or when a large payout is at stake. Machine learning covers the core fraud patterns accurately, and analysts write rules for new trends that the model hasn’t seen, sometimes deploying them within hours. Later the model is retrained and the manual rule can be retired. That is a healthy way to think about rules, as a bridge until something sturdier is ready. AI as the context layer for the analyst I expected Jaanus to tell me that AI writes his team’s analysis. He described something different. His team uses AI to store knowledge in context files, like the difference between a GPS point from the rider app and one from the driver app. That kind of detail used to live only in an analyst’s head. Writing SQL has become almost trivial. Jaanus now spends his time on the numbers and the decision. His team also adds context from other teams. Payment costs are one example. They show whether a change that reduces fraud simply raises processing fees. He still wants a human in the loop. AI can sound convincing while being wrong. Someone without SQL or fraud knowledge can’t validate the answer. What matters most is the analytical, skeptical mindset, along with the ability to convince other teams to change a flow. AI gives the team more reach. It doesn’t replace the judgment that makes someone a fraud analyst ### Final takeaway The most useful idea from this conversation is that uncertainty is something to plan for. You can’t prove every case in the gray zone. Instead, you set a high bar for action, agree on how much loss the business can accept, and check your own labels as carefully as your rules. That makes the fraud team an easier partner for the business, because everyone knows the trade being made. Whether you run a marketplace or a payments product, the same approach applies. Be clear about what you can prove, honest about what you can’t, and ready to explain both. Resources & links Not ready to stop the conversation about my, and hopefully your, favorite subject? Subscribe to The Saturday Fraud Strategist newsletter. Connect with Jaanus Uudmäe | LinkedIn Head of Fraud at Bolt Connect with Chen Zamir | LinkedIn Host of The Saturday Fraud Strategist Helping fintechs build smarter fraud defenses Co-author of “The Fraud Fighter’s AI Playbook”