Process a loan application packet

Split a mixed packet into typed, extracted records.

Goal. Take the pile of documents a borrower submits — financial statements, tax returns, bank statements, incorporation papers, IDs — and turn it into one structured application record, without asking a human to sort it first.

This is the composition recipe. It reuses Classify, then extract and Extract many documents at once at packet scale, and the interesting parts are the routing and the assembly rather than any single call.

Before you start

• An API key, exported as $DOCAI_API_KEY.
• A packet — individual files, or a ZIP.
• A publicly reachable HTTPS endpoint if you want push completion, per Build an event-driven pipeline. Recommended: a packet is a lot of ~90-second extractions.

The shape of the problem

A packet arrives with no reliable labelling. Filenames are scan_004.pdf. One PDF may hold three documents. Some types matter to your credit decision and some are supporting material.

So the pipeline is: identify everything → decide what to extract → extract in parallel → assemble → review what is weak or missing.

Step 1 — Classify everything

python -c "
import base64, glob, json
files = [{'content': base64.b64encode(open(p,'rb').read()).decode()}
         for p in sorted(glob.glob('packet/*.pdf'))]
json.dump({'files': files}, open('classify-bulk.json','w'))
"

curl -X POST "https://api-docai-uat.uptiq.ai/classify/bulk" \
  -H "X-Api-Key: $DOCAI_API_KEY" \
  -H "Content-Type: application/json" \
  --data-binary @classify-bulk.json

POST /classify/bulk also takes metadata and context arrays. Both must be the same length as files — use metadata to carry your own filename or document ID through, so you can correlate results back to source files afterwards.

For a ZIP, each member is classified separately and the results carry classificationGroupId, classificationGroupIndex and classificationGroupTotal.

Step 2 — Route on type and confidence

Map classified types onto what your process does with them:

EXTRACT_FOR_SPREADING = {
    "BalanceSheet", "ProfitAndLossStatement",
    "BusinessFinancialStatement", "AuditedBusinessFinancialStatement",
}
EXTRACT_FOR_VERIFICATION = {"BankStatements", "ArticlesOfIncorporation", "W9"}
FILE_ONLY               = {"AuditNotes"}

CONFIDENCE_FLOOR = 0.80

def route(result):
    doc_type   = result["type"]
    confidence = result["confidence"]

    if confidence < CONFIDENCE_FLOOR:
        return "review", doc_type
    if doc_type in EXTRACT_FOR_SPREADING:
        return "spread", doc_type
    if doc_type in EXTRACT_FOR_VERIFICATION:
        return "verify", doc_type
    if doc_type in FILE_ONLY:
        return "file", doc_type
    return "review", doc_type          # unknown type -> human

Default unrecognised types to review, never to discard. The registry is larger than any routing table you write by hand, and it grows without a release — see Discovering document types. A document silently dropped because nobody added its type is the failure mode that actually hurts.

Step 3 — Extract what matters

Submit the routed documents as a batch, each with the type classification found:

files = [
    {"documentType": doc_type,
     "content": encoded[path],
     "metadata": {"sourceFile": path, "applicationId": APP_ID}}
    for path, doc_type in to_extract
]
submit("/extract/bulk", {"files": files})

Setting metadata per file matters here. Group results come back keyed by document type, not by submission order, so metadata is how you get back to "which file was this".

Pass industry on financial documents to load a default chart of accounts — see Spread a set of financials.

Step 4 — Assemble the record

Collect children as they complete, whether by webhook or by GET /document-extractions/group/{id}:

application = {"applicationId": APP_ID, "documents": [], "flags": []}

for child in children:
    metrics = child["result"]["extractionMetrics"]
    application["documents"].append({
        "type":       child["documentType"],
        "sourceFile": child.get("metadata", {}).get("sourceFile"),
        "data":       child["result"]["extractedData"],
        "accuracy":   metrics["accuracyScore"],
        "missing":    metrics["missingFields"],
    })
    if metrics["accuracyScore"] < 80:
        application["flags"].append(f"low confidence: {child['documentType']}")

Step 5 — Check completeness against your policy

The packet being processed is not the same as the packet being complete. Check what you require, not just what arrived:

REQUIRED = {"BalanceSheet", "ProfitAndLossStatement", "BankStatements"}
present  = {d["type"] for d in application["documents"]}

for missing in REQUIRED - present:
    application["flags"].append(f"missing required document: {missing}")

This is where the value shows up. A packet missing its P&L is identified in seconds rather than at the end of a credit review.

Step 6 — Screen for document integrity

For anything that carries weight in the decision, check the fraud signals on the extraction record before you rely on the numbers:

fraud  = child.get("fraudResults", {})
failed = [r for r in fraud.get("preflight", {}).get("results", [])
          if r["status"] == "FAIL"]
if failed:
    application["flags"].append(
        f"{child['documentType']}: {len(failed)} integrity check(s) failed")

Remember that fraud analysis finishes after extraction does — see Reading fraud and control checks before wiring this into a gate.

What a good outcome looks like

Outcome	Meaning
No flags, all required types present	Straight through to the credit process
Low-confidence extractions	Route those documents to review, keep the rest
Missing required documents	Go back to the borrower immediately, with specifics
Failed integrity checks	Hold for the risk team
Unrecognised types	Human triage — and consider extending the routing table

Related pages

• Classify, then extract — the two-step this recipe scales up.
• Extract many documents at once — batch submission and group retrieval.
• Spread a set of financials — what happens next with the financial documents.
• Reading fraud and control checks — the integrity gate.
• Document Types — the registry your routing table draws on.