Banking and fintech workflows run on documents where a wrong or unexplainable value becomes credit risk, fraud risk, or a compliance finding. Onboarding, loan origination, and regulatory review need more than accurate extraction. They need audit-ready extraction, where every value is traceable to its source and verifiable by a reviewer.
LandingAI Agentic Document Extraction (ADE) turns loan applications, Know Your Customer (KYC) packs, and financial statements into structured data grounded to the source document.
Audit-Ready Means Every Value Is Defensible
Regulators and internal audit require provenance: where each extracted value came from, not just what it says.
Template-based extractors and generic LLM extraction return values without reliable source links, so a KYC conclusion or credit decision built on an untraceable value cannot be defended in review.
ADE produces a complete chain from value back to page:
- Parsing grounds every block with its page, its position in the output, and a bounding box on that page, and each table cell grounds independently, so a figure inside a multi-page table traces to its exact cell.
- Extraction ties every value to the span of source text it was read from, and marks values the model inferred rather than copied, so judgment is separable from transcription.
- A document ID carries through from parse to extract, linking every extracted value back to the parse job that produced its input.
A reviewer moves from the value, to the text it came from, to the region on the page. That chain is what makes a result defensible in a regulated audit.
As the Head of Data and Analytics at a Fortune 100 financial services firm put it in the Tier-1 bank due diligence case study: "What matters is provenance, traceability, and control. LandingAI gives us confidence that every extracted value can be traced back to its source, audited, and defended."
Reviewer Verification in Practice
- A reviewer jumps straight to the source region instead of re-reading the document, which is what keeps review time down at volume.
- Fields route to review on that grounding and on your own business validation rules, so the routing logic stays explicit and auditable rather than resting on an opaque score.
- A field ADE cannot find comes back empty rather than guessed, which is a distinct signal from a located value and can be routed separately.
- Parsed output preserves layout and structure, with page and coordinate citations recorded for every block, documented in the Parse response reference.
Banking and Fintech Use Cases
- Client onboarding and Customer Due Diligence: find and verify KYC and Know Your Business fields across entity documents, articles of incorporation, and identity documents, reducing financial crime and counterparty risk and supporting anti-money laundering alerting. A global Tier-1 bank cut manual due diligence review 40 to 60 percent on multi-lingual corporate documents of 200 to 300 pages per client, per the bank case study.
- Loan origination and underwriting: extract borrower data and reconstruct income from tax returns, paystubs, letters of explanation, and identity documents. Autyn reports 94 to 98 percent field-level accuracy and end-to-end income extraction of a full borrower package in one to three minutes, against one to two hours manually, across 500 loan files and 3,000 documents in production, per the Autyn case study.
- Regulatory reporting and deal analysis: extract figures, risk indicators, and terms from financial statements, filings, and capitalization tables into structured, auditable output for financial services workflows.
Built for Complex Financial Documents
- Accurate parsing of dense tables that span multiple pages and contain merged cells.
- Multi-language content, handwriting, scans, and the signature and stamp blocks common in financial paperwork, which ADE classifies as their own grounded elements.
- Schema-driven extraction where the schema defines what to extract rather than where to find it, so it holds up as formats vary across issuers.
Security, Compliance, and Deployment
- SOC 2 Type II and GDPR, documented on the Security and Compliance page and verifiable through the Trust Center.
- Zero Data Retention: when enabled for your account, LandingAI retains your data only long enough to process the request and deliver the result. Available on Team and Enterprise plans.
- Deployment where regulated data has to run: cloud, private VPC, or on-premises, with a dedicated EU region on AWS Ireland for data residency.
- Encryption applies uniformly, with TLS 1.2 or higher in transit and AES-256 at rest. Confirm current certification and residency details for your requirements through the Trust Center.
Fitting Into Financial Data Infrastructure
- A REST API with Python and TypeScript libraries, plus a Snowflake Native App for teams whose data already lives in Snowflake.
- Structured, grounded output flows into underwriting systems, case management, and analytics without proprietary formats.
- Test against your own documents in the Playground before any integration work.
FAQ
What makes document extraction audit-ready for financial workflows?
Every value traces back through a complete chain: the extracted value, the span of source text it was read from, and the page and coordinates that text occupies. A reviewer or auditor can verify any value against its origin, and that provenance rather than accuracy alone is what lets a KYC or credit decision be defended.
How does ADE support KYC and due diligence at scale?
ADE finds and verifies KYC and KYB fields across entity documents, incorporation records, and identity documents, with every value traceable to its source. A global Tier-1 bank cut manual due diligence review 40 to 60 percent on 200 to 300 page multi-lingual client files, per the bank case study.
What compliance and deployment options are available for financial data?
SOC 2 Type II and GDPR, a Zero Data Retention option on Team and Enterprise plans, and deployment in the cloud, a private VPC, or on-premises, with an EU region for data residency. Confirm current certification and residency status for your specific requirements through the Trust Center.
How do pipelines decide which fields go to human review?
On grounding and your own business validation rules. A value that fails a business check routes to review with grounding pointing at the exact source region, and a field ADE cannot locate returns empty rather than guessed, giving the pipeline a second distinct signal to route on.