Despite many efforts to increase access to financial services, 1.4 billion people still are unbanked. One significant barrier to decreasing this number is the lack of official personal documents (e.g., government- issued identification or utility bills) to comply with the necessary KYC/AML regulation. Innovative schemes can recognize one by using inputs like the personal trail generated when one uses the phone or engages in some digital activity. This paper proposes a formal language- based approach for modeling financial inclusion services and for representing in a structured way the existing KYC/AML compliance rules from different countries. Currently, those rules are written in an unstructured format using natural language and spread in regulatory documents from these jurisdictions. Our proposed language is a core building block of a computational trust and risk engine model, also discussed in this paper. Our approach supports the use of traditional and innovative recognition schemes, helping to overcome the barrier for those who cannot comply with conventional KYC/AML requirements. Moreover, it can also be used to power the risk calculation of computational trust and risk engines. Finally, the proposal is generic enough to be applied to both traditional and decentralized finance.