Best Data Labeling Companies for Fintech and Financial AI in 2027

The market for data labeling solutions and services is expected to reach $57.63 billion by 2030, growing at a compound annual rate of 20.3%, according to Grand View Research. Financial services are a key driver of this growth due to their heavy reliance on AI systems for fraud detection, risk assessment, and compliance.

AI systems overwhelmingly employed by companies across industries are only as good as the data used to train them.

A fraud detection model can only identify suspicious activity if it has been trained on accurately labeled examples. The same applies to credit risk, compliance monitoring, customer service automation, and other financial AI applications. Without high-quality labeled data, performance suffers.

This is crucial in fintech. McKinsey reports that fintech companies account for roughly 40% of its dataset but nearly 70% of AI initiatives. As adoption accelerates, the demand for reliable data labeling continues to grow.

Choosing among the many data labeling providers is not easy. Financial organizations face additional requirements around fraud prevention, regulatory compliance, data privacy, and complex documentation. Those factors raise the bar well beyond standard annotation work.

This guide compares 13 leading providers based on the needs and realities of fintech and financial AI.

Key Takeaways

• Financial AI depends on accurate labeled data. Fraud detection, KYC, AML, credit scoring, and document automation require reliable annotations and financial domain expertise.
• The top 13 data labeling companies for fintech and financial AI are Helpware, Scale AI, Appen, iMerit, TELUS Digital, Sama, CloudFactory, Labelbox, SuperAnnotate, Snorkel AI, Innodata, Cogito Tech, and Surge AI.
• Delivery models determine data access and oversight. Managed teams, contractor networks, and self-service platforms offer different levels of workforce control, scalability, and security.
• Compliance extends to labeling operations. SOC 2 and ISO 27001 provide security assurance, while GDPR, the EU AI Act, and DORA introduce additional requirements for applicable financial AI projects.
• Finance-specific experience matters. Labeling financial documents and transactions requires specialized knowledge, consistent quality checks, and clear audit trails.
• Provider selection goes beyond price per label. Pilot results, labeling accuracy, rework rates, security controls, and total costs provide a stronger basis for comparison.

Top 13 Data Labeling Companies for Fintech and Financial AI in 2027: Side-by-Side Comparison

Data labeling providers differ in how they manage their workforces, protect sensitive information, and support financial AI applications. Some offer dedicated annotation teams, while others rely on contractor networks or software platforms that allow you to manage labeling internally.

The table below compares 13 providers by delivery model, financial services experience, security credentials, and pricing. These differences can help you identify which companies fit your data sensitivity, quality, and operational requirements.

#1 Helpware

Compliance-focused annotation powered by dedicated specialists

Founded in 2015 and headquartered in Lexington, Kentucky, Helpware takes a managed workforce approach to data labeling. Instead of relying primarily on gig workers, the company uses trained, full-time specialists supported by automation and quality-control systems.

Its AI data annotation and LLM training services are part of a broader data operations offering. For financial organizations, Helpware labels transactions, documents, and audio data used in fraud detection, process automation, and other AI-driven workflows. In these operations, accuracy and judgment matter as much as speed.

Helpware operates across 19 locations in 11 countries and supports more than 45 languages through native-speaking teams. Its banking and financial services practice covers a range of regulated workflows, including loan processing and customer operations. The company maintains compliance with standards such as SOC 2, HIPAA, GDPR, and PCI DSS.

According to Helpware, its human-in-the-loop approach delivers 35% faster data processing, 30% fewer labeling errors, and 45% stronger bias detection.

Why we picked it

Helpware ranks first because it combines compliance expertise, experienced reviewers, and operational stability. Client relationships average more than five years, monthly employee attrition is just 2.8%, and its financial-sector experience includes work with crypto exchange Bittrex Global.

• Services offered: Data labeling and annotation across text, image, video, and audio, data collection, cleansing, and categorization, LLM training and fine-tuning, human-in-the-loop review, synthetic data generation, AI model testing and quality assurance.
• Pros: Vetted full-time annotators instead of crowdsourcing, native-speaker coverage in 45+ languages, 90% CSAT and 2.8% attrition, SOC 2, HIPAA, GDPR, and PCI DSS compliance, fintech reviewers for fraud and document tasks.
• Cons: Premium pricing compared to offshore commodity labelers, consultative onboarding that takes longer than a self-serve tool.
• Industry expertise: Fintech and banking, healthcare, SaaS and software, ecommerce and retail, gaming, public sector, automotive and logistics.
• Best for: Mid-market to enterprise fintech and financial AI teams that need compliant, domain-aware labeling with long-term continuity.
• Delivery model: Managed service with dedicated teams; staff augmentation also available.
• Employees: 4,000+
• Certifications: SOC 2 Type II, ISO 27001, and ISO 9001; PCI DSS compliant; HIPAA and GDPR compliant.
• Finance and insurance work: Fintech fraud detection and process automation in its AI data services; banking BPO covering KYC, AML, fraud monitoring, and loan processing.
• Pricing: Starting at $8 to $15 per hour depending on complexity, location, and engagement model.
• Year established: 2015
• Location: Lexington, Kentucky (HQ), with 19 locations across the USA, Mexico, Puerto Rico, the Philippines, Ukraine, Georgia, Uganda, Germany, Poland, and Albania.

#2 Scale AI

Enterprise AI training data and model evaluation for large-scale, complex projects.

Founded in 2016 by Alexandr Wang and Lucy Guo, Scale AI is a San Francisco-based provider of AI training data and model evaluation services. The company works with technology businesses, government agencies, automotive companies, and financial institutions.

Its Data Engine and GenAI Platform combine data labeling software with human annotation and evaluation by domain experts. Scale AI also provides reinforcement learning from human feedback (RLHF), red teaming, and model evaluation services for developers of advanced AI systems.

In June 2025, Meta acquired a 49% stake in Scale AI for $14.3 billion. Following the investment, several major customers reduced their engagements, while Scale AI increased its focus on enterprise and government programs.

Why we picked it

Scale AI has experience managing complex training data programs for frontier AI developers and government organizations, supported by SOC 2 Type II, ISO 27001, and FedRAMP High authorization. Its financial-sector experience includes an insurance solutions offering and references to financial companies such as Rocket Companies and Global Atlantic on its website. These credentials make it relevant for large financial AI programs, although its contractor-based delivery model requires careful assessment of who can access sensitive customer information.

• Services offered: Data labeling and annotation, AI model training data, GenAI data platform, RLHF, and model evaluation.
• Pros: Proprietary automation infrastructure, SOC 2 Type II and ISO 27001 certification, and extensive experience with frontier and enterprise AI projects.
• Cons: Customer concerns following Meta’s investment, limited financial services work, and no publicly disclosed pricing.
• Industry expertise: Technology, government and defense, automotive, financial services, and ecommerce.
• Best for: Large enterprises and AI labs running substantial, well-funded data labeling programs.
• Delivery model: AI data platform and managed services supported by a global contractor network.
• Employees: 1,000+ (company-reported).
• Certifications: SOC 2 Type II, ISO 27001, and FedRAMP High authorization.
• Finance and insurance work: Insurance solutions offering and financial-sector clients.
• Pricing: Custom quotes based on project volume and complexity.
• Year established: 2016.
• Location: San Francisco, California, with a global contractor network.

#3 Appen

Global language expertise and large-scale data annotation backed by decades of experience.

Appen has provided AI training data services since 1996, making it one of the longest-established companies in the industry. Headquartered in Sydney and publicly traded on the Australian Securities Exchange, Appen also maintains a significant presence in the United States.

According to its latest annual results, the company employs approximately 1,185 people and works with more than one million contributors across 200+ countries and territories, covering over 500 languages. Its AI-assisted annotation platform supports supervised fine-tuning, reinforcement learning from human feedback (RLHF), model evaluation, and data annotation for financial services and other industries.

Why we picked it

Appen’s global contributor network and extensive language coverage make it particularly relevant for financial AI projects requiring large multilingual datasets or speech data. Its long operating history also provides experience managing enterprise training data programs. The main trade-off is quality consistency across a large crowdsourced workforce, especially when annotations require specialized financial knowledge.

• Services offered: Data annotation, data collection, RLHF, supervised fine-tuning, model evaluation, and speech data services.
• Pros: Network of more than one million contributors, support for 500+ languages, AI-assisted annotation with smart labeling and pre-labeling capabilities, and a long enterprise track record.
• Cons: Potential quality inconsistencies across crowdsourced contributors and recent financial challenges.
• Industry expertise: Technology, automotive, financial services, retail, healthcare, and government.
• Best for: Organizations requiring large multilingual training datasets and speech data at scale.
• Delivery model: Managed services and annotation platform, with secure facilities in the UK, the Philippines, and China, plus a remote Secure Workspaces option.
• Employees: 1,185 as of December 2025.
• Certifications: SOC 2 Type II, ISO 27001, and ISO 9001; HIPAA-compliant environment.
• Finance and insurance work: Published financial services dataset offering; no named finance clients.
• Pricing: Custom pricing, with options ranging from self-service to fully managed engagements.
• Year established: 1996.
• Location: Sydney, Australia headquarters, with a US base in Kirkland, Washington, and additional global offices.

#4 iMerit

Specialist data annotation for complex financial documents and regulated AI workflows.

Founded in 2012 by Radha Basu and headquartered in San Jose, California, iMerit became part of EXL (Nasdaq: EXLS) following its acquisition in August 2026. Rather than relying on an open crowdsourcing model, iMerit works with trained annotation specialists and an expert network known as iMerit Scholars. The company has offices in San Jose, New Orleans, Kolkata, and Bengaluru.

Its financial services teams support banks, payment providers, credit bureaus, and expense management platforms. Their work includes labeling transactions, extracting information from documents, and organizing unstructured text and visual data used for fraud detection, compliance, and operational automation.

The company also provides RLHF, red teaming, and model validation services for generative AI applications.

Why we picked it

iMerit excels in projects where accuracy carries significant business or regulatory consequences. Its subject-matter expertise and full-time workforce model make it a strong choice for financial organizations that want experienced reviewers handling sensitive data.

• Services offered: Computer vision, NLP, audio, and LiDAR annotation; model fine-tuning, validation, RLHF, and financial document labeling.
• Pros: Trained specialists, access to finance domain experts, dedicated financial services capabilities, and experience with fraud-related and document processing workflows.
• Cons: Project-based delivery and a smaller language coverage footprint than large crowdsourcing providers.
• Industry expertise: Financial services, medical AI, autonomous mobility, geospatial applications, commerce, and government.
• Best for: Banks and fintech companies labeling complex financial documents and transaction data under strict compliance requirements.
• Delivery model: Managed specialist teams and the iMerit Scholars expert network, supported by the Ango Hub platform.
• Employees: Not publicly disclosed.
• Certifications: SOC 2 Type II, ISO 27001, ISO 9001, and TISAX; HIPAA and GDPR compliance.
• Finance and insurance work: Published financial services solution brief; no named finance clients.
• Pricing: Custom quotes based on project scope.
• Year established: 2012.
• Location: San Jose, California headquarters, with offices in New Orleans, Kolkata, and Bengaluru; part of EXL.

#5 TELUS Digital (AI Data Solutions)

Enterprise-scale data annotation supported by a global AI contributor network and BPO infrastructure.

TELUS Digital was founded in 2005 as the technology and outsourcing arm of TELUS Corporation. The company became wholly owned by TELUS in October 2025, when its parent acquired the remaining publicly held shares. Its AI Data Solutions business developed through the acquisitions of Lionbridge AI and Playment.

TELUS Digital combines its global outsourcing infrastructure with an AI community of more than one million contributors covering 500+ languages. The company reports producing more than two billion labels annually, supporting AI development across industries including ecommerce, fintech, healthcare, technology, and travel.

Its annotation capabilities have also received independent recognition. Everest Group named TELUS Digital a Leader in its 2024 Data Annotation and Labeling assessment.

Why we picked it

TELUS Digital offers the scale needed for enterprise annotation programs that involve large datasets, multiple languages, or ongoing labeling requirements. Its backing by a major telecommunications group and established BPO operations provide additional organizational capacity. While the company claims it serves banking, financial services, and insurance industries, its AI data website does not publish a finance-specific case study. If you’re handling regulated financial records, it’s important to establish which delivery centers and contributors will have access to your data.

• Services offered: Data annotation, content moderation, trust and safety, generative AI data services, and intelligent automation.
• Pros: AI community of more than one million contributors, 500+ languages, over two billion labels produced annually, and backing from TELUS Corporation.
• Cons: Large-vendor processes and less individualized attention than smaller specialist annotation providers.
• Industry expertise: Technology and gaming, ecommerce and fintech, healthcare, communications and media, and travel.
• Best for: Enterprises seeking large-scale data annotation within an established global BPO relationship.
• Delivery model: Managed services combining employees with an AI contributor community of more than one million people.
• Employees: 82,000+ company-wide.
• Certifications: Not listed on its AI data services page.
• Finance and insurance work: Banking, financial services, and insurance listed among supported industries; no published finance case study.
• Pricing: Custom quotes.
• Year established: 2005.
• Location: Parent company headquartered in Vancouver, Canada, with delivery operations across 35+ countries.

#6 Sama

In-house data annotation combining quality assurance with an ethical workforce model.

Founded in 2008 as Samasource by the late Leila Janah, Sama is headquartered in San Francisco and operates as a Certified B Corporation. Unlike providers that primarily rely on crowdsourced contributors, Sama employs and manages its own annotation workforce through delivery centers in Kenya and Uganda, with additional partner delivery in India.

The company established its reputation in computer vision annotation before expanding into natural language processing and generative AI data services. Sama reports a 99% first-batch client acceptance rate and names major companies such as GM, Ford, Microsoft, Qualcomm, and Walmart among its clients.

Why we picked it

Sama combines a full-time workforce with structured quality assurance and an ethical employment model. This makes it relevant for financial institutions that consider labor practices and supply chain accountability alongside annotation quality when selecting vendors.

Its strongest experience remains in computer vision, however, rather than financial data labeling specifically. Although Sama lists banking, financial services, and insurance among its supported industries, it does not publish a finance-specific case study. Organizations requiring extensive fintech domain expertise should account for this distinction when evaluating its capabilities.

• Services offered: Data annotation, validation, model evaluation, computer vision, and generative AI data services.
• Pros: Full-time in-house annotation teams, 99% first-batch acceptance rate, Certified B Corporation status, ethical sourcing practices, and an integrated quality assurance platform.
• Cons: Stronger historical focus on computer vision and limited published evidence of finance-specific annotation expertise.
• Industry expertise: Automotive, retail and ecommerce, consumer products and media, medical technology, manufacturing, agriculture, banking, financial services, and insurance.
• Best for: Organizations prioritizing annotation quality, workforce accountability, and an auditable, ethically managed data labeling operation.
• Delivery model: Managed annotation services using a full-time in-house workforce.
• Employees: No current 2026 figure published; Sama reported more than 5,000 data experts in 2025.
• Certifications: ISO 27001 and ISO 9001.
• Finance and insurance work: Banking, financial services, and insurance listed among supported industries; no published finance case study.
• Pricing: Custom, based on project requirements.
• Year established: 2008.
• Location: San Francisco, California headquarters, with delivery centers in Kenya and Uganda and partner delivery in India.

#7 CloudFactory

Dedicated analyst teams for complex financial data labeling and AI workflows.

Founded in 2010 in Kathmandu and now headquartered in Reading, UK, CloudFactory provides managed teams of trained data analysts rather than relying on open crowdsourcing. The company reports a workforce of more than 7,000 analysts, primarily based in Nepal and Kenya, with corporate teams in the UK and the US.

CloudFactory emphasizes structured training, continuous skill development, and regular performance monitoring to maintain consistent data quality. Beyond traditional annotation, its services include model tuning, prompt engineering, retrieval systems, and reinforcement learning from human feedback (RLHF). The company has also expanded into AI platform and consulting services, combining technology with human validation across data, model, and AI agent workflows.

Why we picked it

Financial data labeling often involves sensitive records and complex classification rules that require consistent human judgment. CloudFactory’s managed workforce model provides greater accountability and continuity than a rotating pool of crowd contributors. Its financial services offering specifically covers transaction logs, customer records, and risk reports used to train fraud detection and credit-scoring models. The company also has testimonials from Expensify and Levitate, providing examples of its fintech-related experience.

• Services offered: Managed data labeling and annotation, data processing, generative AI data services, RLHF, and prompt engineering.
• Pros: Dedicated analyst teams, structured quality assurance and documentation, and more than a decade of operational experience.
• Cons: Annual spending commitment required; data labeling is one part of a broader AI platform and consulting offering.
• Industry expertise: Financial services, insurance, healthcare, retail, geospatial applications, and autonomous vehicles.
• Best for: Finance and insurance teams seeking a consistent, trained annotation workforce rather than a rotating contributor network.
• Delivery model: Managed workforce combined with AI platform and consulting services.
• Employees: Approximately 700 corporate employees, plus a managed workforce of more than 7,000 data analysts.
• Certifications: ISO 27001, SOC 2, and HIPAA Business Associate attestation.
• Finance and insurance work: Published financial services offering with named fintech testimonials, along with finance and insurance case studies.
• Pricing: Consumption-based pricing with an annual spending commitment.
• Year established: 2010.
• Location: Reading, UK headquarters, with delivery teams in Nepal and Kenya.

#8 Labelbox

Software-first data labeling with flexible access to expert annotation services.

Founded in 2018 by Manu Sharma, Brian Rieger, and Dan Rasmuson, Labelbox provides a data labeling platform that allows organizations to manage annotation workflows internally or access external experts through its labeling services and Alignerr community.

Backed by investors including Kleiner Perkins, Andreessen Horowitz, SoftBank Vision Fund, and Databricks, Labelbox supports supervised fine-tuning, RLHF, and model evaluation. Its platform includes annotation management, quality assurance, and integrations with machine learning pipelines. The company works with Fortune 500 organizations and leading AI development teams.

Why we picked it

Labelbox gives financial AI teams direct visibility into how training data is labeled, reviewed, and managed. Its workflow controls, audit trails, and integrations make it relevant for organizations that want to maintain oversight of their annotation processes. The company also offers an invoice and document processing solution, although it does not name specific finance clients.

The main distinction is how labeling work gets done. Labelbox’s self-service tiers require you to supply your own annotators or contractors, while its Enterprise tier includes fully managed human data services through Alignerr experts and vetted partner firms. If your project involves sensitive financial information, confirm which team or partner will access your records before choosing a managed arrangement.

• Services offered: Data labeling platform, on-demand expert annotation, RLHF, supervised fine-tuning, and model evaluation.
• Pros: Direct workflow control and transparency, strong integrations with machine learning pipelines, and access to an expert annotation network.
• Cons: Fully managed labeling is available only through the Enterprise tier, and some work may be handled by partner firms.
• Industry expertise: Technology, enterprise AI, and research, with cross-industry applications including financial services.
• Best for: In-house AI teams that want to manage, monitor, and audit their own data labeling workflows.
• Delivery model: Annotation platform with self-service tiers and a fully managed Enterprise option.
• Employees: Not publicly disclosed.
• Certifications: SOC 2 Type II.
• Finance and insurance work: Published invoice and document processing and insurance solutions; no named finance clients.
• Pricing: Free, Starter, and Enterprise tiers, with published labeling rates of $8 per hour and from $10 per hour; custom Enterprise pricing.
• Year established: 2018.
• Location: San Francisco, California headquarters, with a remote expert network.

#9 SuperAnnotate

End-to-end annotation platform with managed experts for LLM development and evaluation.

Founded in 2018 and headquartered in San Francisco, SuperAnnotate provides a unified platform for dataset creation, annotation, curation, and model evaluation. Alongside its software, the company offers access to a professionally managed annotation workforce.

Its platform supports the development, fine-tuning, and evaluation of large language models (LLMs), with tools for reinforcement learning from human feedback (RLHF), quality assurance, and data curation. SuperAnnotate’s investors include NVIDIA, Databricks, and Dell Technologies Capital, while its customers include Databricks and ServiceNow.

Why we picked it

SuperAnnotate combines annotation management, quality controls, and LLM evaluation in one platform. This makes it relevant for financial AI teams that need to prepare training datasets and assess model performance without managing separate tools for each stage.

The trade-off is that teams need time to learn the platform, and financial services is not one of SuperAnnotate’s primary specialties. If you’re building models for financial applications, you may need to supply the domain expertise required to develop accurate labeling guidelines and evaluate complex cases.

• Services offered: Annotation platform, LLM fine-tuning and evaluation tools, RLHF, data curation, model evaluation, and managed workforce marketplace.
• Pros: Unified annotation and evaluation tools, integrated quality assurance and automation, strong LLM development capabilities, and access to vetted experts.
• Cons: Platform learning curve and limited published specialization in financial services.
• Industry expertise: Technology, healthcare, autonomous driving, security, and enterprise AI.
• Best for: Machine learning teams seeking one platform for developing, fine-tuning, and evaluating LLMs.
• Delivery model: Platform with a vetted expert network, with options to manage client and third-party vendor teams.
• Employees: Not publicly disclosed.
• Certifications: SOC 2 Type II and ISO 27001.
• Finance and insurance work: None published.
• Pricing: Starter, Pro, and Enterprise tiers; prices not publicly disclosed.
• Year established: 2018.
• Location: San Francisco, California headquarters, with a remote expert network.

#10 Snorkel AI

Programmatic data labeling for complex, frequently changing financial datasets.

Founded in 2019 as a spinout from the Stanford AI Lab, Snorkel AI is headquartered in Redwood City, California. The company specializes in programmatic data labeling, which allows organizations to label large datasets without manually reviewing every record.

Instead of annotating individual examples, subject-matter experts create labeling functions that encode rules and domain knowledge. Snorkel Flow then applies these functions to generate and manage labels at scale. Since 2025, the company has also offered completed datasets as a service, combining its software with subject-matter experts.

Snorkel’s experience spans projects involving 10-K document review and know-your-customer (KYC) processes at large US banks, including a custodial bank, although the project descriptions do not identify the institutions. Its named Fortune 500 clients include BNY, Chubb, and Wayfair.

Why we picked it

Financial AI teams often work with proprietary documents that change as business requirements, reporting standards, or regulatory rules evolve. Snorkel’s programmatic approach allows labeling rules to be updated and reapplied across datasets, reducing the need to manually relabel records whenever requirements change.

The amount of internal expertise required depends on the engagement model. If you license Snorkel Flow, your technical team must develop and maintain the labeling functions. If you purchase completed datasets, Snorkel’s subject-matter experts handle that work instead.

• Services offered: Programmatic data labeling, data development platform, dataset curation, model evaluation, and expert dataset services.
• Pros: Programmatic labeling suited to private and changing datasets, published banking projects, and the ability to update labels quickly when requirements change.
• Cons: Learning curve associated with weak supervision and no published size for its expert network.
• Industry expertise: Banking and financial services, insurance, healthcare, and the public sector.
• Best for: Banks and fintech companies extracting structured information from proprietary financial documents that require frequent updates.
• Delivery model: Data-as-a-service combining software with subject-matter experts, alongside the Snorkel Flow platform.
• Employees: Not publicly disclosed.
• Certifications: SOC 2 and HIPAA; the type of SOC 2 report is not specified.
• Finance and insurance work: Published banking projects involving KYC and 10-K reviews, with BNY and Chubb among named clients.
• Pricing: Custom enterprise pricing.
• Year established: 2019.
• Location: Redwood City, California.

#11 Innodata

Established data engineering and AI annotation services with financial compliance expertise.

Founded in 1988 and headquartered in Ridgefield Park, New Jersey, Innodata has more than three decades of experience in data engineering and information processing. Publicly traded on Nasdaq under the ticker INOD, the company provides AI data preparation, annotation, model evaluation, and AI safety testing through its Digital Data Solutions business.

At the end of 2025, Innodata employed 10,107 people across operations in the Philippines, India, Sri Lanka, Canada, the UK, Israel, the US, and Germany. Its broader business includes Synodex, which transforms medical records into structured data for insurers, and Agility, a media intelligence platform.

Why we picked it. Innodata combines an established operating history with experience in financial data processing. Its financial services offering includes KYC and AML documentation, ISDA contract extraction, earnings-call annotation, and fraud-related datasets. The company has also announced a five-year contract with one of the world’s largest banks.

These capabilities make Innodata relevant for financial institutions seeking a large, publicly accountable data services provider with experience handling complex documents. Its main trade-off is a more traditional BPO and data engineering orientation compared with newer providers built primarily around LLM development.

• Services offered: AI data preparation and annotation, model evaluation, AI safety testing, data transformation, and compliance-related data services.
• Pros: More than three decades of operating history, public-company governance, and a published financial services annotation offering.
• Cons: Traditional BPO orientation and less emphasis on LLM-native services than newer competitors.
• Industry expertise: Financial services and compliance, technology, healthcare, media, and government.
• Best for: Enterprises seeking an established, publicly accountable data engineering and annotation partner.
• Delivery model: Managed services delivered primarily by employees.
• Employees: 10,107 as of December 2025.
• Certifications: ISO 27001 for specified facilities in Asia and New Jersey.
• Finance and insurance work: Financial services solutions covering KYC, AML, ISDA contract extraction, and fraud datasets; Synodex provides services to insurers.
• Pricing: Custom quotes.
• Year established: 1988.
• Location: Ridgefield Park, New Jersey headquarters, with operations in eight countries, including the Philippines, India, and Sri Lanka.

#12 Cogito Tech

Managed data labeling with compliance controls and traceable training data.

Cogito Tech traces its origins to 2011 and is headquartered in Levittown, New York, with annotation delivery operations in India. The company uses an in-house workforce and has established innovation hubs across North America, Europe, and Asia.

Its Enterprise Data Labeling Services cover reinforcement learning from human feedback (RLHF), model fine-tuning, red teaming, prompt engineering, and data curation. Cogito Tech’s website displays GDPR, SOC 2, HIPAA, ISO 27001, and ISO 9001 compliance credentials.

The company also offers DataSum, a framework designed to document how training data was collected and prepared. This provides a record of data provenance, which is particularly relevant when financial AI datasets need to be reviewed or audited.

Why we picked it

For regulated financial AI applications, understanding where training data originated and how it was processed is an important part of maintaining accountability. Cogito Tech combines in-house annotation with documented data sourcing and security credentials, making it relevant for organizations that require traceable labeling workflows.

Its main limitation is scale. Cogito Tech operates with a smaller delivery footprint than the largest providers in this comparison, which may be a consideration for exceptionally large or geographically distributed projects.

• Services offered: Enterprise data labeling and annotation, RLHF, fine-tuning, red teaming, prompt engineering, and data curation.
• Pros: Broad published security and compliance credentials, traceable data sourcing, and trained in-house annotators.
• Cons: Smaller operating scale than major global providers and lower brand recognition.
• Industry expertise: Finance, healthcare, automotive, agriculture, and defense.
• Best for: Regulated financial AI teams requiring auditable data labeling with documented data provenance.
• Pricing: Custom quotes.
• Delivery model: Managed annotation services delivered through an in-house workforce.
• Employees: Not publicly disclosed.
• Certifications: SOC 2, ISO 27001, and ISO 9001; GDPR and HIPAA compliance indicated by website badges.
• Finance and insurance work: Finance and insurance listed among supported industries; no named finance clients.
• Year established: 2011.
• Location: Levittown, New York headquarters, with delivery in India and international innovation hubs.

#13 Surge AI

Expert language annotation and model evaluation for advanced, reasoning-intensive AI systems.

Founded in 2020 by Edwin Chen and headquartered in San Francisco, Surge AI specializes in reinforcement learning from human feedback (RLHF), reinforcement learning environments, and high-quality language data for advanced AI models.

The company emphasizes specialist expertise rather than annotation volume and delivers services through a global contractor network. It does not publicly disclose the size of its contributor network or employee workforce. Its clients have included major AI developers such as OpenAI, Anthropic, Microsoft, and Meta.

Why we picked it

Some financial AI applications require reviewers to evaluate complex reasoning and interpret nuanced information rather than apply straightforward labeling rules. Surge AI’s experience with language annotation and advanced model evaluation makes it relevant for these demanding tasks.

Its focus on frontier AI development comes with trade-offs. Surge typically operates at a premium price point, which may be difficult to justify for routine fintech annotation projects. The company also doesn’t demonstrate any finance-specific work on the website, and its disclosed security attestation is SOC 2 Type I rather than Type II. These distinctions matter when assessing its suitability for sensitive financial datasets.

• Services offered: RLHF, reinforcement learning environments, language data annotation, LLM training data, model evaluation, and red teaming.
• Pros: Highly vetted annotators and strong capabilities for reasoning-intensive and safety-critical data.
• Cons: Premium pricing and a stronger focus on frontier AI labs than routine fintech operations.
• Industry expertise: Frontier AI development and enterprise technology, with high-precision annotation capabilities applicable across industries.
• Best for: Teams developing reasoning-intensive or safety-critical financial AI applications that require high-quality expert annotation.
• Delivery model: Managed expert network serving advanced AI developers.
• Employees: Not publicly disclosed.
• Certifications: SOC 2 Type I.
• Finance and insurance work: None published.
• Pricing: Premium usage-based pricing and managed contracts.
• Year established: 2020.
• Location: San Francisco, California headquarters, with a global expert community.

Helpware: Our Top Choice

Among the 13 providers reviewed, Helpware stands out because it approaches data labeling as expert work, not a volume exercise. While many competitors focus on crowdsourced scale or software platforms, Helpware relies on trained, full-time reviewers who understand financial documents, transaction data, fraud indicators, and compliance requirements.

Three factors set the company apart:

• Strong compliance coverage, including SOC 2, HIPAA, GDPR, and PCI DSS.
• A global operating footprint spanning 19 locations and more than 45 languages.
• Long-term client relationships, with partnerships averaging more than five years.

According to the company, its human-in-the-loop model also contributes to 35% faster processing times and 30% fewer labeling errors.

Helpware’s pricing is typically higher than commodity labeling providers, and onboarding can take longer than with self-service platforms. For organizations that view data quality, compliance, and model performance as strategic priorities, that trade-off may be worthwhile.

Who Handles Your Data Under Different Delivery Models?

Financial AI training datasets often contain sensitive information, including account numbers, identity documents, transaction histories, and customer call recordings. When outsourcing data labeling, you need to know exactly who will access these records, where the work will take place, and what security controls protect the information.

The answers depend on how your provider organizes its workforce. Some use full-time employees in managed delivery centers, while others rely on contractors, external experts, or your own annotation team. Several providers combine these approaches, so it’s important to understand which model will apply to your project.

• In-house employees at managed delivery centers. Helpware, Innodata, Sama, and Cogito Tech use their own staff to perform annotation work. This model provides clearer oversight of employee access and working conditions. Confirm which facilities will handle your data and whether the provider’s security certifications cover those locations. Innodata, for example, specifies the facilities covered by its ISO 27001 certification.
• Managed teams with specialist networks. iMerit and CloudFactory combine trained teams with domain experts who handle more specialized tasks. CloudFactory’s analysts work primarily in Nepal and Kenya, while iMerit operates offices in the US and India. When evaluating these providers, establish whether you’ll receive dedicated teams or share analysts with other clients. This affects workforce continuity, familiarity with your labeling requirements, and access control.
• Crowdsourcing and contractor networks. Appen, Scale AI, Surge AI, and TELUS Digital’s contributor community use large networks of remote workers. These models offer access to extensive talent pools, but sensitive financial data requires tighter controls over who can view it. Appen also provides secure facilities in the UK, the Philippines, and China, along with a remote Secure Workspaces option. Confirm whether your project can be restricted to vetted contributors in approved locations.
• Platforms with optional expert labeling. Labelbox and SuperAnnotate allow you to manage annotation with your own team or use experts supplied by the vendor. Labelbox’s managed services involve Alignerr experts and partner firms. If you choose external labeling support, confirm which organization will perform the work and require any subcontractors to be identified in your agreement.
• Data-as-a-service. Snorkel AI combines its software with subject-matter experts to produce completed datasets. If you use this model, establish who will access your financial records, how the data will be processed, and whether your information will be used for purposes beyond the agreed project.

Regardless of the delivery model, your security review should cover the same essential questions. Confirm where your data will be processed and stored, whether personal information will be masked before annotation, and whether reviewers work in controlled facilities or remotely. You should also establish whether any third-party AI models or services will access your records during automated pre-labeling.

These controls become particularly important when your labeled data will be used to train financial AI systems subject to regulatory requirements.

Regulatory Requirements for Financial AI Data Labeling Vendors

Data labeling providers are not usually regulated as financial institutions themselves, but their work can affect your organization’s regulatory obligations. This is especially relevant when outsourced teams prepare training data for credit decisions, insurance pricing, or other financial AI applications.

Alongside your existing fintech regulatory compliance responsibilities, two EU regulations deserve particular attention: the EU AI Act and the Digital Operational Resilience Act (DORA).

EU AI Act

The EU AI Act classifies certain financial AI applications as high-risk, including systems used to evaluate an individual’s creditworthiness or calculate credit scores, as well as systems used for risk assessment and pricing in life and health insurance. AI used specifically for financial fraud detection is excluded from the high-risk creditworthiness category.

For systems classified as high-risk, Article 10 establishes data governance requirements that extend to how training datasets are prepared and maintained. These requirements explicitly cover annotation, labeling, cleaning, updating, enrichment, and aggregation. Training data must also be relevant, sufficiently representative, and as free of errors as possible.

Following the 2026 amendment under Regulation (EU) 2026/1744, these obligations apply from December 2, 2027.

If you’re developing credit-scoring or insurance-pricing models covered by these rules, you’ll need evidence that your training data was prepared and reviewed appropriately. Your labeling provider should therefore be able to supply annotation guidelines, quality assurance results, and records showing how labels and datasets changed over time.

This documentation helps you demonstrate how the data was prepared and identify potential problems when models are tested, audited, or updated.

Digital Operational Resilience Act (DORA)

DORA has applied since January 17, 2025, introducing requirements for how covered financial entities manage operational risks associated with information and communication technology (ICT).

Among these obligations, financial institutions must maintain a register of contractual arrangements with ICT third-party service providers. Relevant contracts must also address matters such as where services are delivered and where data is processed and stored.

Whether your data labeling provider falls within DORA’s ICT third-party requirements depends on the services it provides. A vendor hosting your datasets or operating the annotation platform may be treated differently from one supplying annotation personnel who work entirely within your own systems.

Before contracting, work with your legal or vendor-risk team to determine whether the provider falls within DORA’s scope. If it does, make sure your agreement documents the relevant service arrangements and data locations.

Both regulations reinforce the importance of understanding how outsourced labeling work is performed. Clear documentation of data access, processing locations, quality controls, and third-party involvement helps you maintain oversight of your financial AI projects and meet the regulatory requirements that apply to them.

How to Choose the Right Data Labeling Partner for Financial AI

Choosing a data labeling provider for financial AI starts with understanding what your project requires. A fraud detection model trained on sensitive transaction records needs tighter data access controls and more specialized review than a customer support chatbot. The consequences of incorrect labels also differ, from poor customer responses to missed fraud or inaccurate credit decisions.

These differences matter because data labeling providers operate in different ways. Some offer self-service platforms, while others use crowdsourced contributors, programmatic labeling software, or managed teams of trained specialists. The right model depends on your data sensitivity, internal resources, and the expertise required to produce reliable labels.

Rather than comparing providers on price alone, use the following five steps to narrow your options.

1. Assess your data security requirements. Start with the information your provider will handle. If your datasets contain account numbers, identity documents, or payment card information, prioritize vendors that can restrict access to vetted personnel in approved locations. Establish whether sensitive information can be masked before labeling.
2. Verify security certifications and their scope. SOC 2 Type II and ISO 27001 are among the most common credentials published by the providers in this comparison. Confirm which facilities, systems, and operations they cover. Few vendors publicly disclose PCI DSS compliance, so request specific evidence if payment card information will be involved.
3. Look for relevant financial services experience. Innodata, iMerit, CloudFactory, and Snorkel AI publish some of the most detailed financial services material in this comparison. Other providers list finance among their supported industries without showing specific projects. Prioritize evidence that matches your intended application rather than relying on general industry claims.
4. Test labeling quality with a paid pilot. Give two or three shortlisted providers the same representative dataset and evaluate their results against a reference set labeled by your internal experts. Compare accuracy by label category, agreement between annotators, turnaround times, and the amount of rework required. If your workflow combines automated data labeling with human review, agree on confidence thresholds and quality audit sampling before the pilot begins.
5. Compare total costs using the same assumptions. Pricing structures vary considerably. Labelbox publishes labeling rates starting at $8 per hour, while CloudFactory uses consumption-based pricing with an annual spending commitment. Most managed annotation providers offer hourly or project-based quotes after reviewing a sample. Request proposals for the same data volume, complexity, quality requirements, and turnaround expectations to make costs comparable.

Finance-specific experience deserves particular attention during the final evaluation. For example, Innodata and Snorkel AI publish work involving KYC document extraction, while Innodata also covers AML compliance documentation and ISDA contract extraction. CloudFactory describes labeling transaction records and data for fraud detection and credit-scoring models. Labelbox offers invoice and document processing solutions, while Scale AI and CloudFactory publish insurance-related capabilities, including document and claims work.

These examples provide useful starting points, but they don’t establish how a provider will perform on your data. Before making a final decision, request a relevant project example and confirm that the provider can meet your security requirements and accuracy targets.

The strongest choice is the provider that delivers reliable labels under the controls your financial AI application requires, at a total cost that makes sense for your project.