Financial AI uses three-part system to improve trust and accuracy
Beyond LLM Wrappers: Context Engineering and the Three-Engine Architecture for Reliable Financial AI
Artificial Intelligence
Summary
Financial AI tools often struggle to give accurate and trustworthy answers when handling payments and cash flow questions. The authors designed a system combining three parts: a financial knowledge graph that tracks relationships and history, a calculation engine for precise financial decisions, and a group of AI language agents that understand questions and check facts. This setup reduced mistakes like made-up invoices from about 7-9% down to under 1%. They also found challenges with data quality and tailoring to different companies, but their approach sped up deployment from six to two weeks.
What this means in practice
- •For financial software teams: Build AI tools that accurately answer financial questions by combining knowledge graphs, calculation engines, and language models for better trust and fewer errors.
- •For business intelligence developers: Improve enterprise analytics by integrating multi-agent AI validation and structured context engineering to ensure reliable financial insights across different company systems.
Authors
Pradeep Kumar GS, Ravi Tanniru
Abstract
Financial AI systems must support decision-bearing questions involving payments, collections, cash flow, and compliance while ensuring numerical accuracy, traceability, and auditability. Large language models (LLMs) enable natural language interaction but cannot independently guarantee reproducible calculations or evidence-grounded recommendations. We present a production-grounded architecture integrating three components: a financial knowledge graph for enterprise relationships, temporal context, and provenance; a deterministic optimization engine for financial calculations and decisions; and a multi-agent LLM layer for intent interpretation, task orchestration, explanation, and claim validation. A coordination layer, termed context engineering, selects, ranks, composes, and validates query-specific context across ERP systems, tools, and tenants. Based on observations from 15 deployments over approximately 11 months, single-agent pipelines exhibited hallucination rates of about 7-9% (5-10% across query types), including invented invoices and unsupported financial claims. Graph-backed multi-agent validation reduced observed rates to below 1%, although these findings are observational rather than causal. ERP data quality and tenant-specific schemas remained major implementation challenges. Reusable mappings, graph templates, and validation policies coincided with a reduction in deployment time from roughly six to two weeks. We argue that financial context engineering extends beyond text-centric retrieval-augmented generation by requiring structured relationships, temporal validity, exact numerical evidence, access controls, and validation against source records and solver outputs. We outline research challenges in evaluation, reproducibility, privacy, context quality, and cross-tenant generalization.