Building Trust with AI: Fraud Detection and Vendor Scoring

Trust is the foundation of any marketplace. When you're buying fashion from someone you've never met, you need to know they're legitimate, their products are real, and they'll actually deliver. At Shadar, we don't leave trust to chance — we engineered it.

Our Fraud Detection system operates as a continuous monitoring layer across the entire marketplace. Every vendor action — new listings, pricing changes, response patterns, delivery outcomes — generates signals that feed into our fraud scoring engine.

The system watches for specific patterns: a vendor who suddenly lists items far outside their usual category, pricing that doesn't match market norms, review patterns that suggest manipulation, or unusual spikes in activity. Each signal contributes to a composite fraud score. When the score crosses a threshold, the vendor is flagged for review before any buyer is affected.

But catching bad actors is only half the equation. We also need to surface the best vendors. That's where Vendor Intelligence comes in. Every vendor receives a continuously updated performance profile based on their completion rate, on-time delivery rate, average ratings, and dispute history.

These metrics are aggregated into a vendor tier — from new vendors still building their track record to top-performing sellers with proven reliability. The tier influences search ranking, so buyers naturally see the most trustworthy vendors first.

Our prediction engine goes a step further. Using historical patterns, it can forecast when a vendor's performance might decline — perhaps due to taking on too many orders or seasonal shifts in their supply chain. We surface these predictions to our operations team so they can proactively support vendors before problems arise.

The combined effect is a marketplace where trust scales automatically. As Shadar grows from hundreds to thousands of vendors across West Africa, the AI systems ensure that quality remains high without requiring manual review of every transaction.