LandingAI says its Agentic Document Extraction system can link financial data to the exact source page and region, giving banks and fintechs an auditable trail for KYC, lending and regulatory workflows.
LandingAI is pitching its Agentic Document Extraction system as a way to make financial paperwork not only machine-readable, but defensible under review. In banking and fintech, where a single incorrect figure can affect lending, fraud checks or compliance outcomes, the company says speed alone is not enough. The aim, according to LandingAI, is to turn loan applications, KYC packs and financial statements into structured data that can be traced back to the original page, line and region. That audit trail is meant to give reviewers a clear path from extracted value to source evidence, which is crucial in regulated workflows.
The company’s central claim is that provenance matters as much as accuracy. Rather than returning isolated fields, ADE links each output to the text span it came from, the page it appeared on and the coordinates of the relevant region. It also distinguishes copied text from inferred values, allowing a reviewer to separate transcription from model judgement. LandingAI says that structure gives compliance teams and internal auditors something template-based systems and generic large language models often cannot: a result that can be defended, not merely accepted.
LandingAI points to financial use cases where that distinction can be material. In client onboarding and customer due diligence, it says the system can locate and verify KYC and KYB data across incorporation papers, identity documents and corporate records. In loan origination, it says ADE can assemble borrower information from tax returns, pay slips, letters of explanation and identity documents, while in regulatory reporting and deal analysis it can extract figures and risk terms from financial statements and filings into structured output. The company cites a case study in which a global Tier-1 bank reduced manual due diligence review by 40 to 60 per cent on multi-lingual client files of 200 to 300 pages each, while Autyn says it achieved field-level accuracy of 94 to 98 per cent and cut income extraction time from one to two hours to one to three minutes across hundreds of loan files.
The broader pitch is that the system is designed for documents that are awkward even for modern automation tools: dense tables that run across pages, merged cells, handwriting, scans and signature blocks. LandingAI says ADE is schema-driven, so teams specify what they want extracted rather than where to find it, which can help when formats vary from one issuer to another. The company also says the platform can be deployed through a REST API, with Python and TypeScript libraries, via a Snowflake Native App, or in cloud, private virtual cloud or on-premises environments, with an EU data-residency option and a zero data retention setting available on some plans. According to LandingAI, those features are intended to let financial firms plug grounded extraction into existing underwriting, case management and analytics systems without introducing opaque workflows.
Disclaimer: This article is intended to inform and educate, not to recommend or endorse any financial product, investment or strategy. Please consider your own financial circumstances and seek professional advice where appropriate before making financial decisions.
