Banking & Fintech AI · Training Data Services
Annotation, data collection, and conversational AI data for bank statements, KYC documents, and transactions — under SOC 2, ISO 27001, and PCI DSS Level 1, at 95% accuracy.
Financial data annotation and collection is the end-to-end process of sourcing, labelling, and validating banking and fintech data — transactions, bank statements, KYC documents, invoices, SEC filings, voice recordings, and customer interactions — so machine learning models can detect fraud, automate compliance, and process documents at production accuracy.
Industry:
AI chatbots in the financial services space will have saved $862mn human hours by the year 2023.
Industry:
According to reports, AI in the financial services space will be valued at around $79bn by the year 2030.
In the next couple of years, AI-powered chatbots interactions will grow by 3,150%.
Fintech is one space where the precision of results and outputs immensely influences the livelihood of people and businesses. That’s why your fintech brand needs the most relevant and tailored datasets for AI training purposes. We offer conversational AI, data annotation and collection services across a range of demographics and market segments to enable you to launch the most sophisticated fintech application.
Custom collection and sourcing of banking and fintech training data: transactional records, audio call-centre recordings, multilingual speech, document images, and synthetic financial documents. Shaip ships consent-cleared, geographically diverse datasets — plus an off-the-shelf catalog of bank statement, payslip, cheque, and invoice datasets — under ISO 27001 and SOC 2 controls.
Custom collection and sourcing of banking and fintech training data: transactional records, audio call-centre recordings, multilingual speech, document images, and synthetic financial documents. Shaip ships consent-cleared, geographically diverse datasets — plus an off-the-shelf catalog of bank statement, payslip, cheque, and invoice datasets — under ISO 27001 and SOC 2 controls.
Bounding-box, NER, and key-value labelling on bank statements, payslips, invoices, SEC filings, loan applications, and tax forms. Used to train intelligent document processing (IDP) and OCR models — Shaip’s annotators tag dates, amounts, account numbers, signatures, and clause boundaries with 95% accuracy.
Annotation of ID cards, passports, driver’s licences, and selfie-verification frames for KYC automation and onboarding models. Includes face-match validation, document-type classification, and forgery indicators — annotated under PII-controlled environments aligned to GDPR and SOC 2.
Sequence and anomaly labelling on transactional data: card payments, ACH, wire transfers, and account behaviour. Trains fraud-detection, AML, and chargeback models — Shaip annotators tag fraud typologies, money-laundering signals, and synthetic-identity patterns.
Named-entity recognition, sentiment analysis, intent classification, and Q&A pair creation on financial documents, earnings call transcripts, regulatory filings, news feeds, and customer support logs. Used for LLM fine-tuning, financial chatbots, and market-sentiment models.
With our high quality training data, you could let your machine learning modules do wonders.
Annotated transaction histories, loan applications, and bureau pulls train credit-risk and default-prediction models for retail and SME lending.
Labelled fraud typologies (card-not-present, synthetic identity, structuring, account takeover) train real-time fraud and AML models for digital banks and payment processors.
Annotated ID documents, selfie-match pairs, and forgery indicators train onboarding flows that reduce manual KYC review by removing low-risk applications from the queue.
Intent-labelled chat logs and multilingual speech datasets train conversational AI for retail banking, complaint routing, and IVR self-service.
Clause-tagged regulatory filings (SEC, FINRA, RBI, FCA) and contract data train RegTech models for compliance monitoring and disclosure analysis.
Sentiment-labelled earnings transcripts, financial news, and social posts train models for trading signals, brand monitoring, and equity research.
Dedicated and trained teams:
Highest process efficiency is assured with:
The patented platform offers benefits:
Global pool of 500K+ vetted annotators with finance and banking domain training
A powerful platform that supports different types of annotations
Minimum 95% accuracy ensured for superior quality
Global projects across 60+ countries
Enterprise-grade SLAs
Best-in-class real-life driving data sets
Financial data annotation and collection is the end-to-end process of sourcing, labelling, and validating banking and fintech data — transactions, bank statements, KYC documents, invoices, SEC filings, loan applications, voice recordings, and customer interactions — so machine learning models can recognise patterns, detect fraud, automate compliance, and process documents at production accuracy.
Shaip offers both. Banks and fintech AI teams can hand over their own data for annotation only, or commission Shaip to source and collect new training data — audio recordings, multilingual speech, document images, KYC samples, and transactional records — across 100+ languages and target geographies. Shaip also licenses off-the-shelf banking datasets (bank statements, payslips, cheques, invoices, tax documents) through its data catalog.
Shaip handles structured and unstructured financial data: transactions, bank statements, payslips, invoices, cheques, SEC filings, loan applications, KYC documents, ID cards, earnings transcripts, financial news, regulatory filings, customer support logs, and voice recordings. Modalities include text NER, OCR, bounding-box, key-value, sentiment, intent, audio transcription, and multilingual speech collection.
Shaip provides custom annotation and labelling for bank statements, SEC filings, loan applications, payslips, tax forms, invoices, and contracts — using bounding-box, NER, key-value, and table-structure annotation. Annotation runs on Shaip’s proprietary platform under SOC 2, ISO 27001, and PCI DSS Level 1 controls, with NDA-bound finance-trained annotators and isolated client environments.
Shaip enforces a 95% accuracy floor through a 6 Sigma stage-gate QA process owned by certified Black Belts. The workflow includes calibration rounds against gold-standard data, inter-annotator agreement (IAA) tracking, multi-stage sample audits, and a continuous improvement feedback loop. Accuracy targets are agreed per project and reported in delivery summaries.
Yes. Shaip runs KYC and identity document annotation — ID cards, passports, driver’s licences, selfie-verification frames, and forgery indicators — inside isolated environments with NDA-bound annotators, role-based access, and audit logs. Workflows are aligned to SOC 2, ISO 27001, and where applicable GDPR and CCPA. Shaip can also embed annotators directly into the client’s tool when data cannot leave the client environment.
Shaip collects, transcribes, and annotates speech and text data in 100+ languages and dialects, including all major European, Indic, Southeast Asian, Middle Eastern, and African languages. The team has shipped multilingual datasets for banking chatbots, voice IVRs, and call-centre analytics, with linguistic QA performed by native speakers.
