Globalization of financial reporting has increased the need of a correct, efficient and harmonized reconciliation with varying Generally Accepted Accounting Principles (GAAP). The volatility of digital assets and fiat-based financial ecosystems takes on an additional dimension of complexity to crypto-native businesses in the form of cryptocurrency exchanges, custodians, and decentralized finance (DeFi) solutions. The increase in the use of cryptocurrencies to record transactions, as well as the use of fiat-based reporting, exposes these organizations to foreign exchange (FX) and digital asset valuation risks that are compounded by each other. The pre-existing instruments of reconciliation and hedging like forwards, options and swaps do not sufficiently take care of the real-time, decentralized, and high volatility nature of crypto environments.

The current paper presents a multi-GAAP reconciliation model that is AI-enabled to efficiently manage compliance in different jurisdictions by using machine learning, algorithmic FX hedging and intelligent automation. Through a comparison of the traditional process of reconciliation and the AI-powered one, as well as examination of the case-studies in the real-world scenario, the proposed model considers each financial model, technical infrastructure of technical environment, and regulatory parameters as a complex of elements that are integral parts of a single solution. The framework not only positions cross-border financial flows in the direction of greater financial transparency, but also manages to overcome the operational, legislative as well as technical turmoil to which crypto-native companies are exposed. The project advances a dynamic field, intelligent financial systems, and creates a scalable basis of future-proofed treasury and reporting processes within the digital asset economy.