Verifying theApplication Package
How a leasing company catches every mismatch in vehicle financing applications.
Every mismatch caught consistently. Underwriters only review flagged applications. Each decision auditable with a field-by-field verification trail.
A leasing company receives vehicle financing applications through a web portal. Applicants upload documents one by one (applicant ID, pay slips, bank statements, purchase agreement), but the required set varies per applicant. A soldier must submit military service documents. Someone with a second job needs multiple income declarations. The underwriting team manually cross-checks names, addresses, and income figures across whatever documents arrive. During peak season, 12% of mismatches go undetected. The same pattern applies to any loan origination flow: a loan application, like a lease, is a bundle of mismatched documents, and bank statement extraction is usually the hardest part. That is what makes financial document processing hard: every decision depends on reconciling documents that were never designed to agree.
Ingestion
Each time an applicant uploads a document through the portal, it is submitted into the pipeline via HTTP API. Documents arrive individually, not as a complete bundle.
File Name Classification
As each document arrives, a File Name Classifier reads its filename and matches it against configured patterns, identifying whether it is an applicant ID, income proof, bank statement, purchase agreement, or any other expected document type. Each match assigns the corresponding structure to that document.
Extraction
Each classified document is immediately routed to its own Extractor. The applicant extractor pulls name and address from the ID, the income extractor reads line items from pay slips, the bank extractor captures account balances, and the purchase extractor gets the vehicle price. Financial data extraction happens on arrival; the pipeline doesn't wait for the full set.
Validation
Because the required document set varies per applicant, the pipeline cannot know on its own when a group is complete. An API-Based Cross-Document Checker collects extracted data as documents arrive. When the applicant submits their application on the portal, the portal signals MiruIQ via API that the group is complete. Only then does the checker run full cross-document verification, KYC document verification across the applicant's ID, income, and bank records: does the name on the ID match the pay slip? Does stated income match actual pay slip amounts? Separately, an API Value Checker calls an external affordability service to verify that the applicant's salary meets the required threshold.
Delivery
The pipeline archives the original documents in S3 and writes the extracted fields per applicant to PostgreSQL, creating a complete audit trail. It then sends a verification score and any mismatch details to the underwriting system via HTTP API. The underwriting team sees at a glance: all checks passed, or exactly which fields are inconsistent and why.
API-Based Cross-Document Checking
Documents arrive individually over time and are extracted on arrival. An external system signals when the group is complete, triggering full cross-document verification. Inconsistencies are flagged with specific field-level detail.
External API Validation
Extracted values are verified against external services. The pipeline calls third-party APIs and incorporates the response into its routing logic.
Parallel Verification Chain
Multiple verification steps run in parallel across the extracted data. The final output reflects all checks combined.
Cross-document verification: truth emerges from comparing the same facts across multiple documents.
Lending Questions
What banks and lenders ask about loan-document automation.
Yes. A loan file is a bundle of mismatched documents: salary statements, IDs, employment contracts, tax declarations, bank statements. MiruIQ extracts each against its own schema and then cross-compares the bundle: does the name match everywhere, does the employer agree, do the salary calculations add up, the checks that reveal both errors and forgeries.
KYC extraction where the documents never leave your infrastructure: identity documents, registry extracts and statements are processed by local AI models on-premise or in Swiss hosting, results land in your core systems, and every decision keeps an auditable trail.
See a Pattern That Fits Your Workflow?
Every pattern is built from the same modular components: classifiers, extractors, validators, and connectors. Let's find the right combination for your documents.