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DATA COLLECTION AND DATA LABELING

FOR THE BANKING AND FINANCE INDUSTRY
Training Data provides a full cycle of work on collecting and marking data for training neural networks and developing AI technologies in the banking, financial and insurance industries
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the fintech industry

Companies from this sector are coming to the use of artificial intelligence and machine learning to optimize their costs, increase the efficiency of internal processes, such as: fraud detection, improvement of recommendation systems, authentication and face or voice recognition and much more

Labeling images and videos

– Handwriting OCR
– Marking customer payments
– Object detection
– Marking of documents (passport, SNILS, license and others)

Data Collection

– Generation of synthetic data (audio, video, images, texts)
– Data parsing
– Crowdsourcing

Voice recognition

– Speech to text and text to speech translation
– Analysis of client mood by voice
– Voice recognition
– Voice assistant training
marking audio files for call center tasks

Market analysis

– Analysis of customer emotions based on reviews and messages
– Text recognition and extraction
– Classification of text description
Training in LLM systems and chatbots

Stages of work

  • Application

    /01
    Leave a request on the website for a free consultation with an expert. Th e acco unt manager will guide you on the services, timelines, and price
  • Free pilot

    /02
    We will conduct a test pilot project for you and provide a golden set, based on which we will determine the final technical requirements and approve project metrics
  • Agreement

    /03
    We prepare a contract and all necessary documentation upon the request of your accountants and lawyers
  • Workflow customization

    /04
    We form a pool of suitable tools and assign an experienced manager who will be in touch with you regarding all project details
  • Quality control

    /05
    Data uploads for verification are done iteratively, allowing your team to review and approve collected/annotated data
  • Post-payment

    /06
    You pay for the work after receiving the data in agreed quality and quantity

Timeline

  • 24 hours
    Application
  • 24 hours
    Consultation
  • 1 to 3 days
    Pilot
  • 1 to 5 days
    Conducting a pilot
  • 1 day to several years
    Carrying out work on the project
  • 1 to 5 days
    Quality control
You pay for the work after you have received the data
in the established quality and quantity

DIDN'T FIND THE NECESSARY INFORMATION?

Leave a request for a free consultation and a test dataset!

Agriculture Data Labeling Dataset

Collection and segmentation of plants from drone aerial imagery of plantations for monitoring the condition of crops

Botox Injections (Before & After) Dataset

A dataset of photos of individuals with and without Botox for facial classification before and after surgeries

Anti-Spoofing Replay Dataset Anti-Spoofing Replay Dataset

Anti-Spoofing Replay Dataset

50,000+ videos
Replay attack with video from Anti-spoofing Real

Anti-Spoofing Real Dataset

140,000+ files
1 selfie and 1 video of each person
70,000+ people

Why
Training Data

  • Quality Assurance:
  • Enhanced Data Accuracy
  • Consistency in Labels
  • Reliable Ground Truth
  • Mitigation of Annotation Biases
  • Cost and Time Efficiency
  • Data Security and Confidentiality:
  • GDPR Compliance
  • Non-disclosure agreement
  • Data Encryption
  • Multiple data storage options
  • Access Controls and Authentication
  • Expert Team:
  • 6 years in industry
  • 35 top project managers
  • 40+ languages
  • 100+ countries
  • 250k+ assessors
  • Flexible and Scalable Solutions:
  • 24/7 availability of customer service
  • 100% post payment
  • $550 minimum check
  • Variable Workload
  • Customized Solutions

THE FINAL COST OF THE PROJECT IS INFLUENCED BY

Scope of work
Markup complexity
Timing
Markup quality
Our data quality guarantee is 95%. When ordering markup with quality above 95%, we offer enterprise solutions

Project Team Leads

Anton Tseluiko
Operations manager
Arthur Kazukevich
Python-developer
Daria Yurkevich
Quality Control Manager
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Tell us about your project!