Klippa Custom Field Extraction Limitations

Why template-based systems struggle with non-standard bank statement fields and what accounting firms need instead

January 29, 2025•8 min read

TL;DR

Klippa requires 500+ documents and manual annotation to train custom field extraction for non-standard bank statement data

Template maintenance burden when banks add new fields or change formats (reference numbers, memo columns, custom identifiers)

Zera AI dynamically extracts ALL available fields from any bank format without template training or configuration

What Are Custom Fields in Bank Statements?

Standard bank statement fields are universal across most institutions: transaction date, description, amount, and balance. These appear on nearly every bank statement from Chase to Wells Fargo to regional credit unions.

Custom fields are the non-standard data points that specific banks or account types include beyond these basics:

Reference Numbers

Transaction IDs, check numbers, wire reference codes, ACH trace numbers

Memo Fields

Additional transaction notes, merchant categories, custom identifiers

Account Metadata

Branch codes, account officer names, statement cycle identifiers

Regional Formats

International transaction codes, currency conversion details, tax withholding

These custom fields matter critically for accounting workflows. Reference numbers enable transaction matching across systems. Memo fields provide context for categorization. Regional formats support multi-currency reconciliation.

But template-based OCR systems like Klippa weren't trained to recognize these non-standard fields automatically. That's where custom field extraction training becomes necessary.

Klippa's Custom Field Extraction Process

Klippa requires a structured training workflow to extract custom fields that aren't part of their standard bank statement model. Here's what accounting firms face:

The Training Requirements

Data Collection: 500+ Documents

You must provide at least 500 sample bank statements containing the custom fields you want extracted. For regional banks or unusual formats, Klippa may require more examples to achieve acceptable accuracy.

Manual Annotation Process

Klippa's team (or yours, depending on service tier) must manually label each custom field in the sample documents. Reference numbers get tagged as "reference_number", memo fields as "transaction_memo", etc. This is labor-intensive and time-consuming.

80/20 Training Split

Klippa uses 80% of the annotated documents (400 from a 500-doc set) to train the custom model, reserving 20% (100 documents) for benchmarking accuracy. If accuracy falls below acceptable thresholds, more samples are required.

Template Configuration & API Setup

Once trained, you must configure your Klippa API integration to request these custom fields in API calls. This requires developer involvement and ongoing maintenance.

The entire process—from data collection to production deployment—typically takes 2-4 weeks for a single custom field configuration. If you need to extract multiple custom fields across different bank formats, multiply that timeline accordingly.

And unlike Zera AI's dynamic field extraction, this isn't a one-time effort. Every time a bank changes its statement layout or adds new fields, you start the training process again.

Real-World Limitations That Impact Accounting Firms

Template-based custom field extraction sounds workable in theory. In practice, accounting professionals encounter these friction points:

New Bank Formats Require Retraining

You onboard a new client with a regional credit union Klippa hasn't seen before. That bank includes a "loan reference number" field in checking account statements. Your existing Klippa template doesn't recognize this field.

Impact: You must collect 500+ statements from that credit union, submit them for annotation and training, wait 2-4 weeks, then update your API configuration. Your client's month-end close waits.

Template Maintenance Burden

Banks update statement layouts quarterly or annually. A major bank adds a "merchant category code" column to credit card statements. Your trained template suddenly misses this new field or misaligns existing ones.

Impact: You discover extraction errors during reconciliation. Now you're retraining templates for a bank you already configured, creating ongoing maintenance costs and operational risk.

Multi-Account Format Variations

Large clients often have checking accounts, savings accounts, and credit cards at the same bank. Each account type uses a different statement format with different custom field positions. Klippa needs separate training for each variation.

Impact: A single bank relationship now requires 3-4 trained templates, tripling your setup complexity for multi-account processing.

Developer Dependency for API Changes

Each custom field requires API parameter updates. Your bookkeeping team can't adjust field configurations themselves—they need a developer to modify integration code and redeploy.

Impact: Simple field adjustments become engineering tickets, adding days or weeks to changes that should take minutes.

Bottom line: Klippa's custom field extraction works for invoice processing (where vendor formats are stable and volume justifies training) but creates operational friction for bank statement workflows where format diversity and change frequency are high.

When Custom Fields Matter for Accounting Firms

Not every engagement needs custom field extraction. But these scenarios make non-standard fields essential for accurate bookkeeping:

Transaction Matching Across Systems

Reference numbers (check numbers, wire IDs, ACH trace codes) enable automatic matching between bank statements and accounting software. Without these fields, bookkeepers resort to manual matching by amount and description—error-prone and time-consuming.

Multi-Currency Reconciliation

International businesses need currency conversion details and foreign transaction codes from bank statements. These custom fields support accurate P&L reporting when clients operate across borders.

Audit Trail Documentation

CPAs preparing for audits need complete transaction metadata. Memo fields and reference codes provide the paper trail auditors require to verify cash flow statements and validate transactions.

Enhanced Categorization Context

Merchant category codes and transaction memos improve AI categorization accuracy. More context means fewer miscategorized transactions during automated processing.

Key insight: Custom fields aren't optional extras—they're critical data points for professional accounting workflows. Tools that can't extract them reliably force manual workarounds that eliminate the time savings OCR should deliver.

Klippa vs Zera Books: Custom Field Extraction

How template-based training compares to dynamic AI extraction for non-standard bank statement fields

Feature	Klippa	Zera Books
Training Required	500+ sample documents + manual annotation	No training needed
Setup Timeline	2-4 weeks per bank format	Immediate (upload & extract)
New Bank Formats	Restart training process (500+ docs)	Automatic recognition
Template Maintenance	Required when banks change formats	No templates to maintain
Field Coverage	Only fields you've trained	All available fields extracted
Multi-Account Variations	Separate training per account type	Handles all formats dynamically
API Configuration	Developer-dependent field setup	No configuration needed
Best Use Case	Invoice processing (stable formats)	Bank statements (high format diversity)

See how Zera AI handles all bank formats without template training

Try for one week

How Accountants Handle Unusual Bank Formats

"My clients send me all kinds of messy PDFs from different banks. This tool handles them all and saves me probably 10 hours a week."

Ashish Josan

Manager, CPA at Manning Elliott

Ashish's firm manages 80+ clients across British Columbia, each with unique banking relationships. Regional credit unions, international banks, and specialized financial institutions all produce statements with different custom fields. Before Zera Books, his team spent hours manually extracting transaction details and reference codes.

"We don't have time to train templates for every new bank format we encounter," Ashish explains. "With Zera, we just upload the statement and get all the fields automatically. It doesn't matter if it's a bank we've never seen before—the AI figures it out."

Why Zera AI Doesn't Need Custom Field Training

Zera Books takes a fundamentally different approach to field extraction that eliminates template training entirely:

Trained on Millions of Real Documents

Zera AI was trained on 3.2+ million real financial documents (2.8M+ bank statements, 420K+ invoices, 847M+ transactions) from accounting professionals. This massive training set includes every common field variation plus thousands of non-standard custom fields from regional banks, credit unions, and international institutions.

Unlike Klippa's per-customer training approach, Zera's model already knows how to recognize reference numbers, memo fields, account metadata, and regional formats—regardless of bank.

Dynamic Field Detection

When you upload a bank statement, Zera AI analyzes the document structure and identifies ALL available data fields—standard and custom—without requiring you to specify what to extract. It recognizes field types contextually (dates, amounts, codes, descriptions) and labels them appropriately.

This means you get reference numbers, memo fields, and regional identifiers automatically extracted, even if you've never processed that bank format before.

Continuous Learning Without Retraining

Zera AI receives weekly model updates based on real-world accounting workflows. When banks introduce new statement formats or add custom fields, these variations get incorporated into the core model—benefiting all users without requiring individual retraining.

You're not maintaining templates. You're accessing a continuously improving AI that adapts to banking industry changes automatically.

What This Means for Your Workflow

Upload any bank statement and get all available fields extracted—no setup required
Onboard new clients instantly, regardless of their banking relationships
No developer dependency for field configuration or API changes
Zero maintenance when banks update statement layouts
Complete transaction metadata for reconciliation and audit trails

Template-based systems like Klippa made sense when OCR was limited to predefined layouts. But modern AI trained on millions of documents can recognize patterns dynamically—eliminating the custom field training bottleneck that slows down accounting operations.

That's why firms processing diverse bank formats choose Zera Books: they get comprehensive field extraction without the operational overhead of template management. For more context on the training burden, see our comparison of AutoEntry vs Klippa template requirements or read about Nanonets' similar template challenges. See also our guide to bank statement converter software and Zera Books pricing.

Extract All Bank Statement Fields Without Training

Stop configuring templates for every bank format. Zera AI dynamically extracts standard and custom fields from any statement—reference numbers, memos, regional identifiers, and more.

Try for one week

$79/month unlimited conversions • No template training required