ALEX: A Model-Agnostic Architecture for Governed, Traceable and Continuously Evaluated Financial AI Reasoning

ALEX: A Model-Agnostic Architecture for Governed, Traceable and Continuously Evaluated Financial AI Reasoning Abstract The increasing adoption of artificial intelligence in financial analysis introduces a fundamental challenge: AI systems can generate convincing financial conclusions without providing sufficient evidence, reliable calculations, or verifiable reasoning controls. This paper introduces ALEX, a modular financial intelligence architecture designed to separate financial evidence processing, analytical reasoning, runtime governance, and independent quality evaluation. The proposed architecture consists of four interconnected layers: Financial Intelligence, Financial Reasoning, ALEX Runtime, and ALEX Lab. Financial Intelligence provides structured financial data, deterministic calculations, and evidence provenance. Financial Reasoning identifies financial relationships, contradictions, and analytical hypotheses. ALEX Runtime introduces programmatic governance mechanisms that can allow, qualify, or block financial conclusions based on predefined constraints. ALEX Lab provides an independent evaluation framework for measuring analytical quality, detecting errors, identifying regressions, and supporting controlled system improvement. The architecture is designed to remain independent of the underlying foundation model, enabling financial institutions to apply consistent governance and evaluation mechanisms across different AI systems. An initial implementation has been developed within ALEX Finance, using Bulgarian public-company financial analysis as the first application domain. The implementation includes financial metrics, reasoning components, governance controls, benchmark datasets, deterministic evaluation mechanisms, and regression testing. Initial experiments include 31 simulated benchmark cases and 10 cases executed through the financial metrics and reasoning pipeline. Integration testing identified an inconsistency between the representation of financial contradictions in the reasoning engine and the evaluation layer, illustrating the value of cross-component validation. These results demonstrate implementation feasibility and the ability to detect specific integration defects. They do not yet establish general financial reasoning accuracy, production reliability, or superiority over alternative AI systems. Full end-to-end Runtime–Lab validation remains a subsequent research objective. The broader contribution of ALEX is a proposed architecture in which AI-generated financial conclusions are not merely produced but are subjected to explicit governance, traceability, and independent evaluation. The long-term objective is to develop a reusable infrastructure for trustworthy financial AI reasoning, applicable to investment research, financial institutions, portfolio analytics, and other high-stakes decision-support environments.