Fintech Data Annotation & Machine Learning Labeling Services

AI training data for fintech enables models to enhance transaction analysis, fraud detection, risk assessment, and personalized financial services, ensuring smarter and more secure financial solutions.
Collage of financial images including a tax invoice, a cash receipt, a person typing on a laptop with a financial statement on the screen, a $100 bill under normal light, and the same bill under UV light showing security features.

Data Annotation for Fintech

Data annotation for fintech involves labeling financial data to train AI models for fraud detection, transaction categorization, credit risk analysis, and customer insights. This ensures accurate, efficient, and secure financial services.
Close-up of the top left corner of a US 100 dollar bill showing the number 100 in purple, serial number PF14349666 in green, Federal Reserve seal, and district letter F6 in orange.

Counterfeit Detection

Counterfeit detection leverages AI to identify fraudulent financial documents, fake currencies, and unauthorized transactions. By analyzing patterns, security features, and data inconsistencies, it ensures secure and trustworthy financial operations.
A woman uses an ATM while a man stands behind her in a light-filled room.

ATM Activity Monitor

ATM Activity Monitoring uses AI to track and analyze transactions, detect suspicious activities, and ensure operational efficiency. It enhances security, reduces fraud risks, and provides valuable insights for cash management.
Transaction receipt dated Fri 04/07/2017 11:36 AM showing merchant ID, terminal ID, transaction ID, purchase type, card type as Discover, approval status, and payment amounts including subtotal, tip, and total of USD$29.01.

OCR

OCR in fintech automates the extraction of text from financial documents such as invoices, checks, bank statements, and ID proofs. This technology enhances data accuracy, speeds up document processing, and streamlines workflows for financial institutions.
Orange credit card with chip, sample card number 0012 3456 7890 9870, sample name, expiration date 11/24, and CVV 0000.

Credit Cards & ID Verification

Accurately detect and localize credit cards and IDs using bounding box annotation for secure verification and fraud prevention in financial and identity verification applications.
Four professionals walking on a city street with digital facial recognition squares and ID info displayed around their faces.

Facial Recognition for KYC (Know Your Customer)

Facial recognition technology enables accurate identification and verification of individuals using AI-powered image analysis. It enhances security, streamlines authentication, and improves access control in various industries, from surveillance to customer engagement.
Stylized dark blue cursive signature on a light green rectangular background.

Signature Verification & Forgery Detection

Ensure authenticity with advanced signature verification and forgery detection. Detect inconsistencies, prevent fraud, and enhance security in financial and legal processes.

Save Both Time and Money!

Why AI Teams Choose Unitlab

One platform to manage, annotate, and curate training data across every modality, helping teams move faster while staying efficient at scale.

15X

Faster Data Annotation
Orange square with a white check mark inside a white circle.
Auto Data Collect
Orange square with a white check mark inside a white circle.
Auto Data Labeling
Orange square with a white check mark inside a white circle.
Auto Model Validation
Orange square with a white check mark inside a white circle.
Auto QA
Orange square with a white check mark inside a white circle.
Auto Model-In-the-Loop

60%

Free Up AI Engineer’s Time
Orange square with a white check mark inside a white circle.
Dataset Curation
Orange square with a white check mark inside a white circle.
Dataset Version Control
Orange square with a white check mark inside a white circle.
Dataset QA
Orange square with a white check mark inside a white circle.
AI Model Integration
Orange square with a white check mark inside a white circle.
AI Model Validation

5X

Save AI Development Cost
Orange square with a white check mark inside a white circle.
5X saving in Data Collection
Orange square with a white check mark inside a white circle.
10X saving in Data Labeling
Orange square with a white check mark inside a white circle.
5X saving in Dataset Curation
Orange square with a white check mark inside a white circle.
5X saving in AI Development
Orange square with a white check mark inside a white circle.
5X saving in Model Validation
15X
Faster Data Annotation
60%
Free Up AI Engineer’s Time
5X
Save AI Development Cost

Annotation types

Annotation types in fintech include text annotation for customer feedback analysis, entity recognition for financial documents, image annotation for check processing, and time-series annotation for transaction pattern detection. These enable precise AI model training for advanced financial solutions.
Receipt from shopping store with items listed, subtotal $7.99, tax $0.48, total $8.47, cash $10, change $1.53, and a note saying no refunds, exchanges, or returns.

Bounding Box for Fintech

Bounding box annotation in fintech is used to identify and highlight key elements within financial documents, such as signatures, account numbers, and transaction details. This enables AI models to efficiently process, categorize, and extract valuable information for automated financial operations.

Frequently Answered Questions

Is Unitlab AI a free data annotation tool?

Yes. You can use Unitlab AI for free, no credit card required. The free plan is great for getting started and testing workflows. If you need more scale, advanced features, or custom setups, paid plans are available.

Can I change my plan after creating an account?

Yep. Start free and upgrade anytime. Most teams begin on the free plan and move to a paid plan once their datasets or team size grow.

What types of data can I annotate with Unitlab AI?

Unitlab AI supports multimodal data annotation, including image, video, audio, text, and DICOM. You can manage and annotate different data types in one platform using the same workflows.

Does Unitlab support AI-assisted and auto-annotation?

Yes. Unitlab AI includes built-in AI models to speed up annotation with auto-labeling, tracking, and segmentation. You can review, edit, and validate everything to keep quality high.

Can I use my own AI models with Unitlab AI?

Absolutely. Unitlab supports Bring Your Own Model (BYO) workflows. You can plug in your own models, combine them with Unitlab’s built-in models, and run multiple models in a single annotation workflow.

How does pricing for data labeling services work?

Pricing depends on the type of annotation, dataset size, number of classes, and complexity. Labeling services start from $0.02 per image, with custom pricing available for multimodal and large-scale projects.

Who has access to my data?

You do. With on-premises deployments, all data stays inside your own infrastructure and Unitlab has no access to it.
 If you use Unitlab’s hosted platform, your data is encrypted and isolated, and is not visible to Unitlab staff. Access is strictly controlled and limited to your team based on roles and permissions.

Does Unitlab support team collaboration and review workflows?

It does. Unitlab is built for teams, with support for multiple annotators, reviewers, approval steps, and full annotation history so nothing gets lost.

Can Unitlab handle large datasets and production workloads?

Yes. Unitlab is designed to scale, from small experiments to production-level data annotation. Teams use it to manage large datasets, complex workflows, and long-running annotation projects.

Didn’t find the answer you are looking for? Contact our support