UI
Architectural Diagram

AuditIQ — Agentic Financial Audit + Investor Health Snapshot

AuditIQ is a modular crew of AI agents that turns financial statements into verifiable, explainable audit outputs and investor-facing company health checks. By combining a standards-aware auditor agent (Ind AS1 & AS 7), an analyst agent that cross-checks statements against synthetically generated transaction data, and an investor assistant agent with live web access, AuditIQ delivers provable numeric verification, standardized explanations, and real-time signal integration — all at scale.

What’s creative about AuditIQ

Dual-use, single pipeline: the same agent orchestration provides both rigorous compliance checks and succinct investor health snapshots — avoiding duplicated engineering and increasing impact per dataset.
Synthetic-transaction cross-checking: we generate realistic transaction streams to stress-test and validate reported figures, revealing inconsistencies that pure-statement analysis can miss.
Agentic orchestration: specialist agents (ingest → reconcile → verify → explain → summarize) collaborate deterministically, combining symbolic checks with LLM-powered narratives for robust outputs.

Why it’s scalable

Service-oriented design: AuditIQ exposes reusable services — reconciliation, provenance store, standards retrieval (RAG), numeric-verifier, natural-language reporter — that can be composed into many downstream products (bank credit briefs, SME cashflow coach, retail investor snapshots).
Pipeline parallelism: deterministic checks run at scale (batch numeric validation), while agentic writers produce human-readable summaries asynchronously per report, enabling high throughput without sacrificing traceability.

Responsible & explainable by design

Standards-first verification: the auditor agent checks compliance specifically against authoritative standards (Ind AS 1, AS 7) and cites the exact clause or line-item provenance for every claim.
Provenance & confidence: every metric, flag, and recommendation includes (a) the source line/transaction IDs, (b) a confidence score, and (c) an uncertainty interval. This makes outputs auditable and actionable.
Deterministic safety gates & human-in-the-loop: any high-stakes recommendation (loan sizing, covenant waiver language) requires a human sign-off; automated alerts include suggested remediation but never execute decisions autonomously.
Mitigation of LLM failures: numeric claims are always backed by deterministic arithmetic checks; retrieval-augmented generation (RAG) is used for standards citations to reduce hallucination.

Explainability

Line-item traceability: every sentence in the investor snapshot links to the exact balance-sheet/income-statement row (and synthetic tx support when applicable).
Audit trail: an immutable provenance store records agent decisions, prompts, and verification steps for regulatory review.
Failure-mode reporting: the system emits structured failure reports (numeric inconsistencies, unsupported journal entries, missing disclosures) prioritized for human review.

Evidence & inspiration

ACL FinNLP: Financial analysis benchmarks and methods.
Kim, Financial Statement Analysis with Large Language Models.
Multimodal long-form summarization (finance) — numeric-hallucination taxonomy.
Recent arXiv work on agent orchestration for financial tasks.

References:

AuditIQ converts opaque financial filings into verifiable audit evidence and clear investor intelligence — reducing manual audit effort, speeding credit decisions, and democratizing financial insight while preserving audibility, safety, and human oversight.