aiqbench analysis
This paper proposes a compliance-first architecture where regulatory intent acts as an orientation layer rather than a rigid ruleset, embedding admissibility criteria directly into AI execution flows. By binding evidence and decision logic to a permissioned Directed Acyclic Graph (DAG) with deterministic timestamping, the framework ensures verifiable audit trails essential for regulated finance in the DACH region. The approach balances efficiency with supervisory oversight, demonstrating how multi-agent systems can operate within strict legal boundaries without sacrificing transparency.
Compliant AI Infrastructure for Regulated Finance: A tiered multi-agent framework with DLT audit trails for financial operations in DACH
arXiv:2609.27632v1 Announce Type: cross Abstract: We present a compliance-first architecture for AI in regulated finance that treats regulation as an orientation layer rather than a deterministic ruleset. A matrix of regulatory intent and exposure provides a compact classification handle, which a governed policy compiler then maps into concrete prohibitions, obligations and runtime budgets. Prohibitions constrain feasibility and block externalisation, while obligations extend tasks with artefacts that must meet explicit admissibility criteria. Committee activation remains policy-driven and proportionate, preserving efficiency while ensuring supervisory oversight. Evidence, decisions and reason codes are bound to a permissioned DAG with deterministic timestamping, enabling replay, provenance checks and clear attribution of failure. Clause-level legal indexing with effective dates and capability-based agent routing ensure portability across DACH and the wider EU. The result is assurance by construction: compliance is embedded in execution and verifiable by auditors without sacrificing proportionality or transparency.
Abstract:We present a compliance-first architecture for AI in regulated finance that treats regulation as an orientation layer rather than a deterministic ruleset. A matrix of regulatory intent and exposure provides a compact classification handle, which a governed policy compiler then maps into concrete prohibitions, obligations and runtime budgets. Prohibitions constrain feasibility and block externalisation, while obligations extend tasks with artefacts that must meet explicit admissibility criteria. Committee activation remains policy-driven and proportionate, preserving efficiency while ensuring supervisory oversight. Evidence, decisions and reason codes are bound to a permissioned DAG with deterministic timestamping, enabling replay, provenance checks and clear attribution of failure. Clause-level legal indexing with effective dates and capability-based agent routing ensure portability across DACH and the wider EU. The result is assurance by construction: compliance is embedded in execution and verifiable by auditors without sacrificing proportionality or transparency.
16 pages, 3 figures. Published in Swissi AI Journal under CC BY 4.0
General Finance (q-fin.GN); Artificial Intelligence (cs.AI)
arXiv:2609.27632 [q-fin.GN]
(or arXiv:2609.27632v1 [q-fin.GN] for this version)
https://doi.org/10.48550/arXiv.2609.27632 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.5281/zenodo.21901251 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 259 words
