--- title: Fraud S-curve model for scaling fraud technology strategy source_page: https://www.sardine.ai/media/fraudology/episodes/fraud-s-curve-model canonical: https://www.sardine.ai/media/fraudology/episodes/fraud-s-curve-model format: text/markdown date: 2024-03-12T00:00:00Z description: Fraud S-curve model explains how fraud ops mature over time, exposing the controls, tradeoffs, and growth signals teams need to recognize much earlier --- **Quick links:** [Human page](https://www.sardine.ai/media/fraudology/episodes/fraud-s-curve-model) · [Home](https://www.sardine.ai) · [Customers](https://www.sardine.ai/customers) · [Blog](https://www.sardine.ai/blog) · [Demo](https://www.sardine.ai/demo) --- # Fraud S-curve model for scaling fraud technology strategy **Published:** 2024-03-12T00:00:00Z Fraud S-curve model explains how fraud ops mature over time, exposing the controls, tradeoffs, and growth signals teams need to recognize much earlier Guest: Sriram Bhattaru and Calvin Tewari Let’s break this down. In this episode of Fraudology, I’m joined by two fraud leaders from Booking.com, Sriram Bhattaru and Calvin Tewari. Both of them work directly in the trenches of fraud prevention for one of the largest travel platforms in the world, which means they’re constantly balancing customer experience, platform growth, and fraud risk. And in this first part of our two-part conversation, we start with something I always find fascinating. How people actually end up in fraud. Because very few of us plan a career in fraud prevention. Most of us fall into it somewhere along the way. And Sriram and Calvin each have a different story about how they found themselves solving fraud problems inside a global travel company. But from there, we move into a topic I think more fraud teams should be thinking about. Treating fraud as a technology problem. Because here’s what I’ve seen across hundreds of ecommerce and fintech companies. Fraud prevention can’t just live inside operations anymore. It has to be embedded into product strategy, engineering decisions, and the long-term technology roadmap. That’s where the fraud S-curve model comes in. In this episode, we talk about how fraud technology adoption tends to follow a predictable lifecycle. Early in the process, teams often see quick wins and rapid improvements. But as fraud programs mature, the pace of improvement slows and teams have to rethink how they scale their defenses. Understanding that curve is what helps fraud teams move from reactive firefighting to building sustainable, long-term fraud programs. Here are a few ways fraud teams begin approaching fraud as a technology problem: aligning fraud product management with platform engineering decisions designing fraud technology roadmaps that evolve with fraud adoption stages building operational teamwork between product, engineering, and risk teams identifying quick wins that help build stakeholder buy-in early in the process What you’ll hear in this episode How the fraud S-curve model explains the lifecycle of fraud prevention technology Why fraud product management is becoming essential for modern fraud teams How operational teamwork between product and fraud improves platform resilience Why quick wins are critical for gaining stakeholder support in fraud programs How travel platforms adapt fraud strategies as technology adoption evolves You should listen to this episode if you lead fraud prevention for an ecommerce or travel platform work in product management or fraud technology development are trying to scale fraud teams inside a growing company want to understand how fraud programs mature over time are building long-term fraud technology roadmaps If you liked this episode, be sure to subscribe and review the podcast on iTunes, Spotify, YouTube, or wherever you listen to podcasts. ### Episode notes & key takeaways Fraud prevention increasingly requires a technology-first mindset For a long time, fraud prevention was treated primarily as an operations function. Analysts reviewed transactions, responded to alerts, and adjusted rules as fraud patterns changed. But what Sriram and Calvin explain in this conversation is that fraud prevention increasingly requires a technology-first approach. Operational indicators may include: technology-first fraud approach embedding fraud prevention into platform design anti-fraud technology lifecycle shaping how fraud tools evolve over time operationalizing fraud controls within product infrastructure building sustainable fraud programs that scale with platform growth When fraud prevention becomes part of the product architecture, teams gain far more control over how fraud is detected and prevented. Fraud technology adoption follows an S-curve One of the most helpful frameworks we talk about in this episode is the fraud S-curve model. Early in a fraud program’s lifecycle, the improvements can be dramatic. A new model or new system can significantly reduce fraud losses. But over time, those improvements begin to slow down. Fraudsters adapt, attack patterns change, and the same tools stop producing the same gains. Operational indicators may include: fraud adoption stages moving from experimentation to maturity fraud prevention maturity requiring more advanced controls evolving fraud strategies as detection methods plateau fraud innovation in travel platforms responding to new threats Understanding where your organization sits on that curve is critical if you want your fraud program to keep evolving. Product and operations alignment strengthens fraud defenses Another theme throughout this conversation is the importance of cross-team collaboration. Fraud teams can’t operate in isolation if they want to build durable defenses. Operational indicators may include: product and operations alignment enabling stronger fraud controls cross-team fraud strategy integrating risk insights into product development scaling fraud teams alongside platform growth platform fraud resilience improving through shared ownership of fraud risk When fraud insights feed directly into product decisions, platforms can address fraud risks earlier and more effectively. Fraud programs improve when teams learn from past mistakes One of the things I appreciate about conversations like this is the honesty around how fraud programs actually evolve. No team gets everything right the first time. The most effective programs are the ones that treat mistakes as learning opportunities and keep refining their strategy. Operational indicators may include: learning from fraud mistakes to strengthen future controls building sustainable fraud programs through continuous improvement adapting fraud strategies as fraud patterns evolve maintaining long-term fraud program resilience That willingness to keep experimenting and improving is what ultimately helps fraud teams stay ahead of attackers.