Posted: Oct 30, 2025
Banking App PFM & CX Training Datasets + LLM Fine-Tuning
Total Budget: $2,000
AWS SageMakerTextFixed Price
AWS SageMakerTextFixed PriceOverview
Dataset
Labeling Details
Hiring
Budget
Client
Labeling Overview
The labeling team will annotate a financial conversation dataset for an AI financial advisor. Each sample will include: Intent classification (e.g., spending insight, budgeting, card support) Transaction & Merchant category classification Assistant response labeling (tone, completeness, professionalism) Function-call tagging for backend data queries (get_user_summary, get_transactions, run_custom_sql) SQL validation labeling (mark if query is safe, parameterized, and read-only) Annotators will produce structured JSON records containing the user query, labeled intent, function name (if applicable), arguments, and assistant reply. Required skills: Familiarity with personal finance terminology Experience labeling chatbot or conversational data Understanding of function calling / API-style structured data Basic SQL literacy to identify safe vs unsafe queries
Total Budget: $2,000Fixed PriceIntermediate1-3 monthsAgency or Individual
Total Budget
$2,000
Pay per Label
-
Time Requirement
Flexible
Duration
1-3 months
Description of dataset
Text-based dataset for training an AI financial advisor that provides users with personalized financial insights, budgeting tips, and investment guidance. Includes labeled conversations, backend function-calling examples, and structured JSON outputs for querying user data (including custom SQL support)
Software
AWS SageMakerHiring Type
Agency or Individual
Required Location
Global Any Location
Workload / Schedule
One-time Project
Software
AWS SageMakerData Type
TextLabel Types
Classification
Fine Tuning
Function Calling
Question Answering
Text Generation
Subject Matter / Industry
Banking, Fintech, Finance, Function Calling
Language
Arabic
English
Job Type
Self Service
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Share link
trainai.co/project