Collect. Annotate. Validate. Build Better AI
Financial models fail when document fields, identities, and claims are labeled inconsistently. We annotate forms, statements, policies, and claim images through a multi-stage QA process, so your model trains on labels it can trust.
The accuracy of your labels is the accuracy of your AI model, and our process is built around that.
BFSI data arrives in many layouts, languages, and scan qualities. A cheque, an invoice and a loan file each place the same field in a different position. Handwriting, stamps and low-resolution scans reduce extraction accuracy, and small errors in a name, amount or date carry real financial consequences.
Labeling mistakes become failed document extraction, weak fraud signals, and compliance reviews that have to be repeated.
Annotators are trained on your field schemas and validation rules before production begins. Every batch passes internal and external quality checks, in a secure facility with biometric access, device restrictions, and GDPR and ISO/IEC 27001:2013-aligned controls.
Our data annotation services label fields, tables, headings, signatures, and stamps on financial forms and statements. This data labeling teaches models where each piece of information sits across document types, templates, and issuers.
We annotate document type, data fields, photos, security features, and layout on identity documents, and mark signs of alteration where your project requires it. This annotated data helps models verify customers faster with fewer manual exceptions.
Our bounding box annotation and transcription cover printed and handwritten text, labeled with its place in the document layout. This AI data annotation trains OCR models to read varied fonts, scan quality, and languages.
We label dates, amounts, account details, payees, line items, totals, and tax fields for each document type, including skewed scans, stamps, and handwriting. Our image annotation services help models perform on real submissions, not only clean samples.
Our text annotation services label entities, values, and relationships in text and tables, then classify each document or data point by your taxonomy. This NLP data labeling prepares models to extract accurate information from reports, filings, and correspondence.
Annotators read merchant names, descriptions, and amounts and assign each payment a category from your list. This annotated training data supports budgeting tools, spending insights, and transaction analysis.
We label application forms, income and employment evidence, property documents and agreements, marking the fields and clauses that drive decisions. Cross-document checks help models read complete application packs, using data annotation for AI.
We label coverage terms, exclusions, dates, amounts, claimant details, and incident descriptions on policy and claims documents. This annotation for machine learning helps models route claims and check them against coverage.
Our semantic segmentation services outline vehicle damage with polygons and label damage type, severity, and the affected part. These image annotation labels train models to assess claims from photographs.
We label documents, transactions, and text with the fraud indicators you define, such as mismatched fields, altered content, and unusual patterns. This AI annotation gives models dependable examples that separate normal cases from suspicious ones.
We annotate regulatory text, obligations, definitions, and policy references following guidelines from your compliance experts. These labels help models find relevant clauses and match them to internal controls.
We label clauses, parties, dates, obligations, amounts, and conditions, and classify each clause by type. This data annotation supports models that review, compare, and extract contract terms.
We tag entities, figures, instruments, and topics in fund documents, reports, and statements. This data labeling and annotation helps analytics models organize information for analysts.
Our text annotation services tag reviews, surveys, emails, and call transcripts for sentiment, topic, and intensity. Audio annotation covers recorded calls, so models can track satisfaction and recurring issues.
We label intents, entities, dialogue acts, and conversation turns, and review responses against your tone and escalation rules. This AI training data helps banking and insurance chatbots answer accurately within your policies.
Explore our data collection services.
Domain-trained expert annotators and daily throughput tracking against SLA targets.
A rigorous multi-level quality assurance process that ensures a 97%+ quality benchmark, validated on every project.
A risk-free 2-day pilot before full production, and team deployment within 24 business hours of sign-off. You see our work before you commit to volume.
Every engagement is tailored to your goals, with annotation guidelines, QA, and delivery cadence adapted to your project.
Pay-per-verified-productive-hour billing. You are not paying for our learning curve or mistakes, our internal rework, or idle bench time.
ISO/IEC 27001:2022, ISO 9001:2015, TISAX Level 3, HIPAA, and GDPR compliance. Biometric access, 24*7 CCTV monitoring, and policies and procedures that prohibit cameras and unauthorised storage devices on the floor.
100% in-house delivery. No outsourcing, no work-from-home access to client data, no exceptions.
Financial data annotation is the structured labeling of data so a machine-learning or analytics system can learn from examples or be evaluated against defined criteria. For banking, financial services & insurance, the work begins with a written label taxonomy, clear inclusion and exclusion rules, and representative sample data. The exact scope depends on the model objective and the client's acceptance criteria.
Depending on the approved scope, banking, financial services & insurance projects may involve documents, forms, transaction descriptions, customer-support text, claims material, and structured fields, subject to the agreed scope. The team should define each label, the unit being annotated, how to treat uncertain cases, and whether the task is frame-level, object-level, document-level, or text-level. These examples describe possible project requirements, not a guarantee that every modality or use case is currently offered.
Output formats and tooling should be confirmed during discovery rather than assumed from a general industry page. The project brief should specify the required schema, field names, class IDs, metadata, export structure, and any client platform access. Qualitas Global should confirm compatibility with the requested tools before the pilot begins; no specific format or third-party platform is promised here.
A quality plan should define the annotation guideline, reviewer responsibilities, sampling or review coverage, error categories, escalation rules, and acceptance criteria before production. Multi-level quality checks are a stated part of the intended service approach, but the exact workflow and quality SLA need internal confirmation. Disagreements should be documented, corrected, and reflected in updated guidance where the same issue could recur.
Data access, storage, transfer, retention, deletion, and physical work arrangements should be agreed before any sensitive files are shared. The project owner should confirm the applicable security controls and whether any certification or regulatory commitment applies to the specific engagement. Do not assume a particular ISO standard, HIPAA status, GDPR status, or other certification without current internal evidence.
Pricing should be scoped around the task definition, data complexity, annotation guideline, expected review effort, language or domain requirements, and the delivery arrangement. The stated commercial model is based on productive hours, with scope and acceptance criteria agreed in advance. A quote should clarify what counts as productive work, how rework is treated, and which activities are included, without relying on an unverified public price.
Yes, the recommended first step is to discuss a representative sample and agree on a pilot scope before committing a larger dataset. The pilot can test the label definitions, edge-case handling, reviewer workflow, output structure, and acceptance criteria. Start dates, sample size, turnaround, staffing, and scale-up capacity should be confirmed by the project team after reviewing the data and requirements.
Common challenges include Sensitive information, inconsistent document layouts, ambiguous fields, domain terminology, and the need for traceable review decisions. A clear guideline should show positive and negative examples, explain ambiguous cases, and define when annotators should escalate rather than guess. To get started, share the intended model or business use, data types, approximate volume, required outputs, security constraints, and a sample if permitted. Qualitas Global can then confirm fit and propose a pilot.
We audit, correct and validate auto-annotated aerial data against your guidelines before it reaches your training pipeline.
Whether you need precisely annotated training data, real-world data collection, a dedicated in-house team, or other staffing solutions, Qualitas Global delivers all three under one roof. Backed by over a decade of experience, we back every engagement with a risk-free 2-day pilot before you commit.
