> ## Documentation Index > Fetch the complete documentation index at: https://docs.webacy.com/llms.txt > Use this file to discover all available pages before exploring further. # Data Quality and Confidence > Understand how Webacy validates risk data across onchain sources and third-party providers, minimizes false positives, and produces explainable risk scores. Confidence begins with the quality of the underlying data. Webacy aggregates data directly from supported blockchains and augments it with carefully selected third-party intelligence where additional context or redundancy improves accuracy. Rather than relying on a single external source, we intentionally validate critical datasets across multiple providers whenever possible. This approach is particularly important for financial data, where stale or inconsistent information, such as delayed oracle updates, incomplete market data, or inaccurate reserve information, has historically contributed to significant losses across the digital asset ecosystem. In many cases, discrepancies between trusted data sources are themselves meaningful risk signals that become inputs to the Webacy Risk Framework. ## Accuracy & False Positives Like any analytical system, no risk model can guarantee perfect accuracy. Blockchain ecosystems are dynamic, new protocols are introduced every day, and adversaries continuously develop new techniques designed to evade detection. Webacy minimizes false positives by grounding its analysis in verifiable blockchain data and explainable risk factors rather than opaque heuristics or probabilistic assumptions. Every score is derived from observable onchain behavior, measurable characteristics, and independently verifiable signals. This allows users to understand not only the outcome of an assessment, but also the evidence supporting it. Our methodology is continuously validated against real-world blockchain activity and refined as new threats, asset classes, and protocol designs emerge. Over years of deployment across millions of blockchain entities, Webacy has consistently demonstrated a strong ability to identify meaningful risk while maintaining a high degree of precision. Our detection systems have repeatedly identified material risks, including major stablecoin depegs, malicious token launches, address poisoning campaigns, and contract exploits, before they became widely recognized by the broader market. ## Explainability Over Black Boxes Rather than asking users to trust a score without context, Webacy is designed to produce explainable assessments. Risk scores can be traced back to the underlying risk factors, supporting evidence, and detection engines that contributed to the analysis. This transparency enables developers, institutions, regulators, and AI systems to evaluate the reasoning behind an assessment, build confidence in automated decisions, and apply additional policies where appropriate. ## Track Record: Proven in Production The Webacy Risk Framework is not a theoretical model. It has been continuously refined through real-world deployment across millions of blockchain entities and a rapidly evolving digital asset ecosystem. See for yourself: * [Metronome msUSD](https://www.webacy.com/blog/metronome-msusd-depeg-analysis) * [Stablecoin H1 2026 Recap](https://www.webacy.com/blog/stablecoin-depeg-failure-mechanisms) * [Main Street msUSD](https://www.webacy.com/blog/msusd-depeg-redemption-velocity-risk) * [apxUSD, MIM, USR, StabIR](https://www.webacy.com/blog/stablecoin-depegs-beyond-the-price) * [USDr, EURR](https://www.webacy.com/blog/alerting-on-the-stablr-exploit-before-the-peg-broke-heres-what-our-systems-saw) and read more on our [blog](https://www.webacy.com/blog). ## What's Next Explore how Webacy uses historical blockchain intelligence to identify recurring patterns, improve risk detection, and strengthen future analyses.