The core inefficiency in onchain capital allocation is not a shortage of data but a shortage of readable trust. Every major DeFi protocol can observe every transaction in real time, yet most still demand 150%+ over-collateralization because they cannot reliably distinguish a high-quality long-term participant from a temporary extractor or a newly funded sybil. @zerufinance zScore addresses this gap by converting longitudinal wallet behavior, including lending history, protocol diversity, conviction signals, governance participation, liquidity provision patterns, and risk-taking consistency, into a single 0 to 1000 trust score. With coverage already exceeding 320 million wallets and native visibility on Etherscan, BaseScan, and other major explorers, the score moves reputation from a marketing claim into infrastructure that applications can query instantly. This is a practical prerequisite for under-collateralized credit, smarter airdrop filters, and access gating that does not require KYC. The longer and more consistent the behavioral history, the harder it becomes to fake, which is why behavior-centric models tend to outperform pure snapshot or identity-based approaches in fully open systems. As tokenized markets and autonomous AI agents scale, the wallets and agents that can prove sustained positive-sum activity will systematically receive better capital terms, higher limits, and preferential access. zScore from ZeruAI is currently one of the few primitives making that differentiation operational at meaningful scale across dozens of chains.