The Context
LSportsis a leading global sports-data technology provider. As part of the company's strategy to deliver a complete, end-to-end ecosystem for clients within the ARENA360 platform, DEFEND was launched - a new product that integrates seamlessly into the suite to power real-time risk evaluation and sports-betting monitoring.
DEFEND grants sportsbook operators worldwide comprehensive, real-time control over financial exposure, player behavior, and bet acceptance - a pure B2B system used by betting professionals across three distinct operational tiers.
Leading the design end-to-end - from the initial MVP through to full product launch - I shaped how three distinct operator tiers experience risk in real time, in an environment where a single delayed decision translates directly into financial exposure.
Build a unified, clear, and modular platform capable of processing millions of data points per second - surfacing critical metrics under time-critical constraints and translating complex risk frameworks into an intuitive interface.
The People Behind the Book
DEFEND is a pure B2B system used by sports-betting professionals across three operational tiers - each approaching risk from a different angle, and under very different time constraints.
The primary daily operators of the platform, working in a high-stakes, fast-paced environment. They monitor live financial liability and make quick accept/reject calls on abnormal bet slips - most critically on the Bet Referral screen, where a 60-second countdown forces a decision before the system auto-rejects.
Responsible for risk-policy configuration - from global Platform Limits down to granular constraints by sport, league, or specific fixture. They need to toggle seamlessly between multiple betting-operator accounts (Multi-Operator Management) with maximum efficiency.
Internal LSports specialists who run risk operations on behalf of external clients. They depend on the platform for instantaneous indicators of fraudulent behavior, player anomalies, and daily P&L reporting across the books they manage.
Core UX Challenges & Solutions
Two systemic problems sat at the heart of the platform - making high-stakes decisions under a hard clock, and preventing self-inflicted risk through conflicting rules. Each was met with a targeted UX solution.
Decisions in 60 Seconds → A Time-Critical Bet Referral Screen
Bet slips that breach preset risk limits are automatically routed to the Referral view, giving traders exactly 60 seconds to review the slip, diagnose the restriction trigger, and Accept or Reject. The legacy layout scattered this crucial data across multiple areas - traders missed their window, and the experience suffered.
I redesigned the Referral interface entirely around a Time-Critical UI approach. A highly visible countdown timer now anchors a consolidated Context Card that surfaces the core referral triggers, player-profile parameters, and current market liabilities at a single glance. Stripping the surrounding visual noise let traders make confident, informed decisions within seconds.
Conflicting Rules → Real-Time Limit-Hierarchy Validation
Configuring risk parameters means navigating a multi-layered hierarchy - Sport → Country → League → Fixture. Operators frequently created rules that unintentionally conflicted with or overrode existing constraints, opening critical financial exposures and vulnerabilities in the risk model.
Rather than letting users enter rules that inherently break system logic, I built real-time validation across every form field: each input fires a backend check for overlapping conflicts. Directly beneath the creation area, a dynamic table lists all relevant existing limits and filters down live as each field is filled - giving instant visibility of similar thresholds. If an override is detected, a toast notification fires and the conflicting limit is highlighted on screen.
Designing for AI
As a core component of the product's 2026 roadmap, DEFEND evolved from rigid, rule-based workflows into a proactive system driven by machine learning - with trust and explainability as the central design constraint.
To eliminate the need for traders to manually build custom parameters to categorize users - VIP, Sharp, or Monitored - I designed the interface to accommodate a new ML model that analyzes player behavior autonomously and in real time. The primary objective was Building Trust through Explainable AI: the UI never simply tags a player as “high-risk,” it surfaces clear Data Insights explaining why the model reached that conclusion - such as abnormal betting velocity or sharp line timing. The integration improved identification of high-risk users by 20%.
Business Outcomes & OKR Alignment
Leading the design of DEFEND from its MVP inception delivered measurable results directly aligned with corporate business objectives.
A user-centered data display combined with automated AI models yielded a >30% reduction in false-positive fraud alerts - allowing far more precise mitigation of financial losses without burying real threats in noise.
Risk professionals reported a major leap in operational control over corporate exposure, driving up overall platform sentiment and aligning with our target NPS score of ≥25.