In the financial industry, AI has already been used for credit approval, fraud detection, and risk control decisions. However, the reality is that these models are often black boxes, unable to explain their fairness to customers or prove their compliance to regulators.
Lagrange's DeepProve is filling this gap. It leverages zero-knowledge proofs (ZKP) to allow financial institutions to generate a verifiable proof when training or using AI models. This proof ensures:
The model training actually follows the pre-set rules;
Sensitive data has not been leaked;
The results are verifiable and traceable, rather than simply relying on "trust".
Imagine multiple banks jointly training a fraud detection engine. Each institution contributes data, but the data itself never leaves the institution's security boundary. The final model will come with "Proofs of Training", allowing regulators to verify the compliance of the training process and results without directly accessing customer information.
Combined with Lagrange's DeepProve roadmap, this is not only a technological innovation, but also a new infrastructure for financial compliance and trust mechanisms. It turns "fairness and transparency of AI" from a slogan into a verifiable fact.
At the economic level, $LA tokens run through governance, staking, and incentives, driving the stable operation of the decentralized verification network. As Proofs of Training are implemented in financial scenarios, the use value of $LA will continue to increase.
Lagrange uses DeepProve to make financial AI not just smart, but trustworthy, transparent, and compliant.
