Financial Services & FinTech

Financial AI data built for
programs regulators will scrutinize.

Regulatory document classification, fraud signal annotation, and financial NER require annotators who understand the domain. We provide domain-expert annotators, SOC 2 compliant infrastructure, and regulatory text coverage across 155+ locales.

500k+
Expert evaluators across 300+ domains
155+
Locales for regulatory annotation
7
Welocalize ISO certifications plus SOC 2 Type II
ComplianceSOC 2 Type IIISO/IEC 27001:2013GDPR CompliantISO 9001:2015ISO/IEC 27701:2019FCA-aligned
The data gap

Where financial AI programs break down.

Financial AI annotation requires contributors who can distinguish a material risk disclosure from a forward-looking statement, and who understand that the same regulatory obligation reads differently in German, Mandarin, and Portuguese. General-purpose annotation vendors do not have this workforce.

01
Data gap

Domain-naive annotators on regulated content

Fraud signal labeling, regulatory clause classification, and earnings sentiment analysis require financial domain knowledge. Without it, annotation inconsistency is systematic rather than random, and difficult to detect until model performance fails in production.

Financial NERRegulatory DocsFraud Detection
02
Data gap

Compliance infrastructure that does not meet financial-sector standards

SOC 2 Type II, GDPR, FCA, and SEC data governance requirements apply to annotation pipelines processing regulated financial information. Most vendors cannot demonstrate record-level compliance documentation for financial data handling.

SOC 2GDPRFCASEC
03
Data gap

English-only annotation on multi-jurisdiction regulatory content

Regulatory terminology across MiFID II, FCA, and SEC frameworks diverges in ways that translation cannot bridge. In-country annotators with native-language regulatory expertise are required to produce accurate classification across jurisdictions.

Multi-JurisdictionRegulatory Text155+ Locales
Use Cases

Use cases for financial AI teams.

Use case

Regulatory Document Classification and NER

Named entity recognition and classification applied to regulatory filings, compliance documentation, and financial agreements. Tags entities, obligations, deadlines, and risk indicators across SEC, FCA, MiFID II, and GDPR frameworks.

NLPRegulatoryNER
Use case

Fraud Signal and Transaction Intent Annotation

Transaction sequence labeling, behavioral pattern tagging, and textual descriptor classification for fraud detection training data. Annotators are matched to fraud and risk domain backgrounds for each program.

Fraud DetectionStructured DataNLP
Use case

Earnings and Financial Sentiment Analysis

Annotation of earnings call transcripts, analyst reports, and financial news for sentiment classification, forward-looking statement extraction, and material signal identification across global capital markets.

NLPSentimentFinancial Text
Use case

Financial Document Intelligence and RAG Validation

Annotation and structuring of prospectuses, credit agreements, and regulatory submissions for retrieval-augmented generation validation and document intelligence training pipelines.

Document IntelligenceRAGStructured Data
Use case

Search Relevance for Financial Information Platforms

Query-result relevance judging for financial research platforms, data terminals, and FinTech applications, with multilingual evaluation across 155+ locales by annotators with financial domain knowledge.

Search RelevanceNLUMultilingual
Use case

Financial AI Model Evaluation

Multidimensional benchmarking of financial AI systems across reasoning accuracy, regulatory alignment, domain knowledge depth, and safety, with side-by-side analysis against prior model versions.

Model EvaluationBenchmarkingSafety
Data types

Financial data types we annotate.

01
Data type

Regulatory and Compliance Documents

Filings, compliance documentation, financial agreements, and regulatory submissions annotated with financial NER, obligation classification, and risk indicator tagging across SEC, FCA, and MiFID II frameworks.

02
Data type

Structured Transaction Data

Transaction sequences, behavioral datasets, and financial records labeled for fraud detection, credit risk modeling, and anomaly identification by annotators with financial domain backgrounds.

03
Data type

Financial Text and Market Data

Earnings transcripts, analyst reports, financial news, and market commentary annotated for sentiment classification, forward-looking statement extraction, and material signal identification.

04
Data type

Multilingual Regulatory Content

Regulatory and compliance text across 155+ locales annotated by in-country financial domain experts with native-language regulatory knowledge, not translated from English source documents.

Why Welo Data

Four reasons financial AI teams choose Welo Data.

Differentiator

Domain-expert annotators with financial and regulatory backgrounds.

Programs requiring financial annotation are staffed with annotators who have financial domain backgrounds: regulatory, compliance, analyst, and accounting expertise matched to the task type. Background verification runs through our NIMO platform before production access is granted.

500k+
vetted contributors, domain-matched
Differentiator

SOC 2 Type II and ISO 27001 infrastructure designed for regulated data.

Data handling pipelines are built to SOC 2 Type II, GDPR, FCA, and SEC governance requirements. Audit-ready compliance documentation is a standard program deliverable. Secure facility and clean-lab configurations are available for the most sensitive financial programs.

SOC 2 Type II
plus 7 Welocalize ISO certifications
Differentiator

NIMO: OFAC checks and adverse media screening at the annotator level.

For regulated financial data, annotator vetting cannot stop at identity verification. Our NIMO platform runs OFAC checks, adverse media screening, and politically exposed person screening on every contributor before production access, with continuous behavioral monitoring throughout.

130+
behavioral monitoring variables
Differentiator

Regulatory annotation in 155+ locales, jurisdiction by jurisdiction.

We staff in-country financial domain annotators who have native-language expertise in the regulatory frameworks of their jurisdiction. Each locale is built from ground-level financial expertise, not adapted from English annotation guidelines.

155+
locales, in-country regulatory experts
Common questions

What financial AI buyers ask us.

All annotators on financial programs are vetted through NIMO, which runs identity verification, OFAC checks, adverse media screening, and politically exposed person screening before production access. Domain-expert annotators handle tasks requiring financial expertise. SOC 2 Type II and ISO 27001 certifications apply to all data handling.

Regulatory document annotation is staffed by annotators with compliance and financial legal backgrounds. Earnings and sentiment programs use annotators with financial analysis expertise. Fraud signal annotation uses contributors with risk and fraud domain knowledge. All background verifications run through NIMO before production access.

155+ locales with in-country annotators who have native-language regulatory expertise in their specific jurisdiction. Regulatory terminology and compliance obligations are annotated as they exist in each jurisdiction, not translated from English.

SOC 2 Type II and ISO/IEC 27001 certified infrastructure applies to all financial programs. Secure facility and clean-lab delivery configurations are available. All annotators execute NDAs and complete identity verification before program access.

Yes. We operate programs across EU (GDPR, MiFID II), UK (FCA), and US (SEC) frameworks concurrently. In-country annotators with jurisdiction-specific regulatory expertise handle each market, ensuring classification accuracy reflects the actual legal standards of each target jurisdiction.

Work with us

Financial AI data that holds up under regulatory scrutiny.

Domain-expert annotators. SOC 2 compliant infrastructure. Regulatory text across 155+ locales.