David had reviewed hundreds of deals, but this one felt routine: summarize a target’s last three years of cash flows and call out anomalies for a partner meeting at 4 p.m. He fed the documents into an AI assistant, scanned the summary, and sent it along. The meeting went badly. The summary cited a working capital swing that didn’t exist – an AI hallucination that looked confident, read convincingly, and was completely wrong. A client noticed. Trust frayed.
In financial services, precision is not a luxury; it’s the currency. And while AI has transformed productivity, the industry has learned the hard way that speed without accuracy creates risk. The central question is no longer “Can AI help?” but “How do we ensure that it answers correctly, consistently, and provably – especially under regulatory scrutiny?”
This advertorial explores how leading finance teams are eliminating hallucinations by combining local AI on AI PCs with structured data preparation – reducing errors by up to 78x and turning AI from a risky experiment into a trusted analytical partner.
Why Hallucinations Happen (And Why They Hit Finance Hard)
From a first principles perspective, large language models are probabilistic. They generate likely next tokens based on patterns in training data. That’s powerful for language, but unstructured enterprise content – multiple versions, subtle differences in definitions, footnotes, and exceptions – can trip models into plausible but wrong answers.
Finance amplifies these challenges:
- Terminology is precise; a subtle shift can flip meaning
- Documents are versioned, redlined, and cross-referenced across systems
- Regulatory and audit requirements demand traceability for every figure and claim
If the model can’t anchor to the correct, authoritative source, it may invent. And in finance, invention is unacceptable.
The Fix: Pair AI With Prepared, Authoritative Data
Accuracy rises when you control three things:
1) Locality: Keep analysis on AI PCs so sensitive financials never leave the firm’s control; no external training risk, no network dependency, and lower latency for iterative work.
2) Retrieval: Point the assistant only at a curated, authoritative corpus – approved policies, final financial statements, audit trail documents, and deal rooms – so answers are grounded in the right sources.
3) Preparation: Structure documents so the assistant can resolve definitions, link references, map versions, and normalize units and timeframes. This single step turns a tangle of PDFs into a coherent, queryable knowledge set.
Firms that implement this trio – locality, retrieval, preparation – report error reductions of up to 78x. Analysts get the speed of AI without sacrificing the accuracy finance demands.
What “Prepared Data” Looks Like in Practice
Consider a typical corpus: audited financials, MD&A, credit memos, diligence Q&A, covenants, and board minutes. Preparation layers include:
- Version Control: Prefer the final, audited version; mark superseded drafts
- Definition Mapping: Tie terms like “Adjusted EBITDA” to the exact firm-specific definition used in the deal, with linkbacks
- Unit Normalization: Standardize currency, thousands vs. millions, fiscal vs. calendar
- Table Extraction: Lift structured tables from PDFs with preservation of headers and footnotes
- Cross-Reference Graphs: Link schedules, notes, and covenant references for explainable answers
Prepared once, this corpus serves many use cases: portfolio monitoring, credit reviews, covenant checks, comps, and board reporting. Accuracy scales with reuse.
Snapshots From the Field (Anonymized)
- A top law firm cut contract review by 99%, using clause libraries tied to authoritative definitions and instant citations in outputs
- A Big 4 team assembled RFPs in seconds, pulling right-fit content with traceable references
- Pharmaceutical leaders processed two million legal documents, saving 62,000+ hours with explainable outputs
- Compliance automation reduced policy mapping time by 98%, with audit-ready reports generated instantly
Finance is not alone in craving accuracy, but it is uniquely penalized for errors. The playbook that works for legal and compliance translates directly into underwriting, diligence, and reporting.
Local AI on the Analyst’s Desk: Why Device Matters
AI PCs combine CPU, GPU, and NPU so analysts can run compact, high-quality models locally:
- Security: Sensitive financials never leave the device
- Speed: Low-latency responses enable rapid iteration and exploration
- Availability: Works offline – on flights, in secure rooms, during connectivity issues
- Predictability: No token overages or surprise fees for heavy diligence weeks
For many tasks, the “killer feature” is not model size; it’s proximity to the data and the analyst. When the assistant sits next to the spreadsheets and diligence PDFs – under the firm’s security controls – usage rises and risk falls.
From Hours to Minutes: High-Value Finance Tasks
Underwriting & Credit Memos
- Generate first-draft summaries with figures linked to audited tables and footnotes
- Flag covenant definitions and map them to deal-specific language
Portfolio Monitoring
- Pull quarter-over-quarter changes with source citations
- Surface anomalies in working capital, margins, or segment reporting
M&A Diligence
- Compare seller-provided metrics to audited statements; highlight discrepancies
- Create side-by-side comp tables with normalized units and timeframes
Board Reporting
- Draft narrative sections from updated financials; provide inline links to sources
- Generate appendix exhibits programmatically from consistent table extraction
Each output is explainable: every number, sentence, or claim links back to the underlying source document and page.
What Changed for David’s Team
David’s group rebuilt their workflow around local AI with prepared data:
1) They established an authoritative corpus for each deal – final financials, executed agreements, and auditor letters. 2) They structured the corpus with definition maps and cross-references. 3) They ran analyses locally, so nothing left the device and performance stayed high.
Within two weeks, error rates fell dramatically. The team stopped wasting time validating basic facts and spent more time on judgment and nuance. Outputs went out faster – with citations attached.
The Economics: Trusted Speed Pays
Firms report three reinforcing returns:
- Time: Tasks that were measured in hours take minutes; minutes become seconds
- Quality: Explainable outputs reduce rework and partner-level review cycles
- Cost: Local, per-device licensing costs roughly one-tenth of cloud substitutes over a device lifecycle
At scale, organizations see annual labor savings measured in the millions. But the bigger story is risk: fewer reputational hits, cleaner audits, stronger client trust.
Controls and Governance (Without the Friction)
Because the assistant runs locally, firms can fit it into existing controls:
- Device management and encryption policies apply as usual
- Access to sensitive corpuses follows existing role-based permissions
- No external data transfer minimizes exposure and simplifies approvals
Auditors appreciate explainability. When every output cites a specific document and page – and definitions are explicit – review becomes faster and less adversarial.
Why This Works Now
Two shifts enable this approach:
1) Model efficiency improved dramatically. What needed large, server-class models a year ago is now possible with smaller, smarter models suitable for local use.
2) AI PCs are mainstream. CPU, GPU, and NPU acceleration on modern devices make local inference responsive, power-efficient, and reliable.
Together, they bring finance-grade AI to the desktop – without the cloud overhead or accuracy trade-offs.
To explore explainable, on-device AI designed for finance-grade accuracy, visit https://iternal.ai/airgapai. For platform overviews and customer stories, see https://iternal.ai.
