The financial industry is very cautious about the use of AI because the tolerance for error in risk assessment and decision-making is extremely low. A single erroneous risk model can lead to significant losses. So how can we make AI's decision-making results more credible? The answer provided by @Lagrange Official is zkML and verification networks from #lagrange .
Through Lagrange, financial institutions can allow AI models to perform complex calculations off-chain, such as credit risk scoring and portfolio analysis, and then generate mathematical proofs through the Prover Network. This proof enables on-chain smart contracts to quickly verify the results without needing to inspect the underlying models or data. This means that banks can meet regulatory and risk control requirements while protecting customer privacy.
The core drive comes from economic mechanisms. The $LA token serves as a certificate and incentive tool for node participation, ensuring that each verification task has honest participants. Nodes stake $LA and bear potential penalties, providing the system with self-regulation.
This combination not only reduces the trust costs for financial institutions but also offers more transparency to users. In the future, when users see an AI analysis result for a financial product, it may come with not a 'manual' but a verifiable mathematical proof. Lagrange transforms financial AI from 'guessing' to 'proving.'
