Manual data entry costs finance, lending, and insurance teams more than they realise — not just in hours, but in errors that compound downstream. Data capture software solves this by automatically extracting structured information from invoices, forms, contracts, and scanned documents, pushing it directly into your existing systems.
This guide compares the seven best data capture platforms in 2026, based on extraction accuracy, integration depth, workflow configurability, and total cost of ownership for mid-market financial services teams.
1. Floowed — Best for Financial Services and Lending Teams
Floowed is an AI-powered document processing platform built specifically for financial services, credit, lending, and insurance workflows. Where most data capture tools stop at extraction, Floowed combines extraction, validation, intelligent routing, and system integration into a single configurable workflow — making it a complete document processing solution for financial services and lending teams.
Key Features
Pros
Cons
Best For
Banks, lenders, fintechs, and insurance companies processing high volumes of loan documents, KYC packets, invoices, or claims forms who need accuracy above 94% and direct integration with their core systems.
2. ABBYY FlexiCapture — Best for High-Volume Enterprise Capture
ABBYY has been in the intelligent document processing space longer than most. FlexiCapture is their enterprise platform — a mature, configurable system capable of processing millions of documents per year with strong OCR accuracy across many document types and languages.
Key Features
Pros
Cons
Best For
Large enterprises with standardized document types, in-house IT resources, and a need for on-premise deployment or strict data residency requirements.
3. Kofax TotalAgility — Best for Enterprise IDP + RPA Combination
Kofax (now part of Tungsten Automation) combines intelligent document processing with robotic process automation in a unified platform. If your automation strategy already involves RPA bots, TotalAgility lets you connect document capture directly into those bot workflows without a separate integration layer.
Key Features
Pros
Cons
Best For
Enterprises already invested in Kofax/Tungsten RPA who want to add document capture within the same vendor ecosystem.
4. Nanonets — Best for SMB and No-Code Teams
Nanonets is an AI document automation platform designed for accessibility. Teams without technical resources can train custom extraction models through a visual interface, connect to common business tools via Zapier or native integrations, and start processing documents in hours rather than weeks.
Key Features
Pros
Cons
Best For
Small to mid-size teams processing moderate document volumes who need quick deployment without IT involvement.
5. Rossum — Best for Accounts Payable and Invoice Processing
Rossum is a document processing platform with a strong focus on accounts payable workflows. Its Cogito Document Brain uses a large language model trained specifically on financial documents, which delivers strong accuracy on invoices and purchase orders across different vendor formats.
Key Features
Pros
Cons
Best For
Finance teams processing high volumes of invoices and purchase orders who need ERP integration and can justify the per-year subscription cost.
6. Hyperscience — Best for Regulated Industries and Government
Hyperscience is an enterprise ML platform that specialises in automating complex, high-stakes document processes in regulated environments — government agencies, insurance carriers, and financial institutions with strict compliance requirements. It focuses heavily on human-in-the-loop workflows for cases where confidence thresholds require human review.
Key Features
Pros
Cons
Best For
Government agencies and large regulated institutions processing complex, high-stakes documents where compliance requirements are stringent and accuracy is non-negotiable.
7. UiPath Document Understanding — Best for Teams Already on UiPath
UiPath Document Understanding is the document capture module within the UiPath RPA platform. If your automation workflows are already built on UiPath bots, Document Understanding lets you add intelligent document extraction directly into those workflows without a separate vendor relationship.
Key Features
Pros
Cons
Best For
Teams with existing UiPath RPA deployments that want to extend automation to include document capture without adding a new vendor.
How to Choose the Right Data Capture Software
If you're in financial services, lending, or insurance: Floowed is purpose-built for financial services and lending teams — handling loan files, KYC packets, claims forms, and invoices with high accuracy and direct integrations with your core systems.
If you're an enterprise with standardised, high-volume documents and in-house IT: ABBYY FlexiCapture or Kofax offer proven platforms with deep enterprise tooling, but expect months of implementation time and ongoing IT involvement.
If you primarily process invoices and purchase orders: Rossum delivers the highest accuracy for AP-specific workflows and has strong ERP integrations, but the entry price requires committing to meaningful volume.
If you need something working this week without a developer: Nanonets is the fastest path to live document extraction for moderate volumes, though per-page pricing will scale with you.
If you're already on UiPath: Start with Document Understanding before adding a new vendor — it may already cover your use case.
Teams building end-to-end document automation workflows in financial services should evaluate platforms on their full workflow coverage, not just extraction. Finance and operations teams in financial services are consolidating document processing, AP automation, and intelligent extraction into a single end-to-end platform.
Frequently Asked Questions
What is data capture software?
Data capture software automatically extracts information from physical and digital documents — invoices, forms, contracts, scanned images — and converts it into structured data that flows into your business systems. Modern platforms use AI and machine learning rather than fixed templates, allowing them to handle variable document formats with 90–97%+ accuracy.
How accurate is AI data capture?
Accuracy varies by platform and document type. Purpose-built platforms for specific document categories (like Rossum for invoices, or Floowed for financial services documents) typically achieve 94–98% accuracy. General-purpose platforms range from 88–95% depending on document complexity and scan quality.
What's the difference between OCR and AI data capture?
OCR (Optical Character Recognition) converts image pixels to text characters — it can read the text on a page but doesn't understand what the text means. AI data capture goes further: it classifies the document type, identifies which text belongs to which field, validates the extracted values against business rules, and flags exceptions for human review. OCR is an input layer; AI data capture is a complete processing system.
How long does implementation take?
It depends heavily on the platform and your document types. Cloud-native platforms like Nanonets can be running in hours for common document types. Platforms like Floowed typically take days for initial deployment. Enterprise platforms like ABBYY or Kofax often require weeks to months of implementation and configuration work.
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