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Industry solution

AI training data for Fintech

Multilingual intent, NER, and preference data for financial assistants, reducing chatbot fallback across the languages your customers use.

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Overview

Data operations tuned to Fintech

Financial assistants fail quietly when a customer speaks a language or dialect the model was not trained on. Corpshore AI builds multilingual intent, entity, and preference data with native annotators, plus document AI for statements and forms, so assistants understand more requests and fall back to a human less often. Careful handling and access controls keep sensitive financial data contained.

Intent classification and entity recognitionMultilingual and dialect coverageDocument AI for statements and formsPreference data for assistant tuning
AI training data for Fintech at Corpshore AI
The challenge

Where the data breaks down

  • Assistants fall back to a human when a customer uses a language or dialect the model never saw.
  • Financial entities like amounts, account references, and dates are easy to mislabel without domain care.
  • Sensitive financial data needs handling and access controls, not a general labeling pool.
  • Document layouts vary widely, which breaks naive extraction.
Our approach

How Corpshore AI delivers

  • Native annotators build intent and entity data in the languages and dialects your customers actually use.
  • Financial entities are labeled against a domain taxonomy to reduce amount and reference errors.
  • Sensitive data is handled under agreed access controls with a trained, employed team.
  • Document AI labels are built across varied statement and form layouts for reliable extraction.
Delivery workflow

From scope to delivery, in fintech

Step through how a fintech engagement runs under one operator and a single SLA.

1. Scope

We map the intents, entities, languages, and dialects your assistant serves, and agree the access controls for sensitive data.

Stage 1 of 5
Data types

What we deliver

Intent and utterance classification
Financial named-entity recognition
Preference pairs for assistant tuning
Document AI extraction from statements and forms
Multilingual and dialect-specific conversational data
Multilingual
intent coverage in customer languages and dialects
Fewer fallbacks
more requests understood on first contact
Document AI
extraction across varied statement layouts
Controlled
sensitive data handled under access controls
Why Corpshore AI

The operator advantage for fintech

  • Native, in-region annotators cover the dialects that decide whether an assistant understands a customer.
  • One operator covers annotation, RLHF, and document AI, so the assistant improves along every axis at once.
  • Employed teams under access controls keep sensitive financial data contained.
Relevant services

Services behind this solution

Annotation & LabelingEvery modality, 97%+ accuracyRLHF & Preference DataLLM alignment at scaleAI ConsultingStrategy, analysis & delivery
FAQ

Fintech, answered

We build intent and entity training data in the languages and dialects your customers actually use, so the assistant understands more requests on first contact and hands off to a human less often.

Ready to scope a pilot?

Tell us your modality, volume, and languages. We'll return an indicative scope, timeline, and cost band.

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