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Data Annotation — How Data Labeling Service Helps Build a Smarter Finance Industry?

Smart Finance

With the large-scale commercial application of deep learning and computer vision technology, the combination of the financial industry and artificial intelligence has become more closely, and the wave of intelligent finance has begun to sweep the whole financial industry.

From product design to customer service, From external management to internal monitor, artificial intelligence technology has a clear landing scenario in each of the financial industry value chains, effectively reducing the operating cost and financial risk.

Behind the ecological reshaping is the breakthrough in AI technology. Computer vision, voice interaction, and natural language processing are more closely integrated into the financial industry, and the application of these technologies cannot be apart from the data annotation industry.

Computer Vision

In the financial industry, computer vision is mainly used in the field of internal process optimization, customer service, face recognition, object detection.

Such technology provides simplicity and convenience, for example, face swiping for fast payment. This mode not only simplifies the payment process and improves efficiency, but also greatly improves the user’s payment experience. The previous method is that the user enters the password before paying. It’s relatively complicated, and there is a password leak problem.

This kind of computer vision technology requires different annotation types, such as key points, 2D boxing, etc.

Voice Interaction

In the financial industry, especially in bank institutions, the staff always communicate with clients. There are a variety of scenarios, such as business consulting, customer service, and marketing.

At present, many financial institutions are equipped with voice interaction technology. And customer service robots are the most typical ones.

For example, a question answering system (QA) is a kind of chatbot that can answer automatically human questions in natural language. Understanding voice is a half process, and the other is giving responses. The competitive advantages of chatbots are communication simplification and labor cost reduction.

Due to the great differences in terms and expressions in different scenes, there are high requirements for scenario-based and customized data annotation services.

ByteBridge, a Human-powered and ML-powered Data Labeling Tooling SaaS Platform

Natural Language Processing

Applications of natural language processing include semantic analysis, information extraction, text analysis, machine translation, and so on. In the financial industry, the main application scenarios are text checks, information search, translation, etc.

For example, through semantic analysis of the text content, the intention is analyzed, and the response is finally formed through text synthesis.

The integration of artificial intelligence has profoundly changed the traditional financial industry and reshaped the new ecology. In the future, with the development of artificial intelligence technology, there will be more vertical applications in finance.

Common Labeling Tools in Finance:

Common Labeling Types in Finance:

  • Financial Text &Speech Classification
  • Chatbot Customer Service
  • Fraud Detection Regulation Compliance
  • KYC Automation
  • Customer Survey Feedback Analysis
  • Clauses and Entities Extraction
  • Text Categorization


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Relevant Articles:

1 Chatbot — One of the Most Popular Applications in the NLP Field

2 Main Applications of Natural Language Processing(NLP) — 1

3 Data Labeling Service: How to Ensure Data Quality for Machine Learning and AI Projects?

4 Data Labeling — How to Select a Data Labeling Company

5 The Best Data Labeling Company in 2021



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