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Application of Artificial Intelligence in Commercial Banks — Part2

The application of artificial intelligence in the field of customer service

Identity verification in baking service

Opening an account is the first step for customers to have banking services. It usually defines the future relationship between the customer and the bank. The process seems simple, but it usually involves a large number of paper manuals and takes a lot of time. Remote customer registration using artificial intelligence technology applies machine learning for fraud detection, replacing the traditional Knowledge-Based Authentication (KBA) method for Customer Due Diligence (CDD), saving processing time and cost.

Identity verification is a pre-work for customers to handle during the account opening process. Traditional identity verification mainly uses a combination of password verification and manual verification, and there are some pain points and difficulties that are difficult to solve. For example, account passwords are related to customer identities. Once the password is leaked, customer information is no longer protected.

The efficiency and accuracy of manual verification are affected by factors such as the staff’s ability as well. During peak business hours, the staff is under pressure, and mistakes are inevitable. Artificial intelligence-based new identity verification methods such as face recognition, fingerprint recognition, and live detection can enrich verification methods, increase the difficulty in using fraudulent accounts, and improve verification efficiency and accuracy.

Financial biometric identification platform

Commercial banks can use financial biometric identification platforms to solve the problem of identity verification. The biometrics management department can have access to the image database, deliver early warning information, and perform related user management, authority management, etc. In the bank business processing, the platform can identify and compare the face images collected on-site with those obtained from ID information and return the comparison scores and corresponding thresholds to the counter service personnel. They can use the information to judge whether the evidence belongs to the same person. After adopting the modified system, the bank’s identity verification process can be completed in as fast as one second, 58,000 facial recognition services can be processed every day, and the recognition accuracy rate can exceed 99.3%.

Voice chatbot in bank customer service

An intelligent voice robot is another actual application of AI in commercial bank customer service. Chatbots can be used for online virtual conversations, and they can automatically respond according to customer needs, and simulate human-computer interaction without bank customer service staff to solve customer queries in real-time.

In theory, chatbots can respond to customer needs faster and provide better service. Meanwhile, chatbots can handle many repetitive problems, saving human resource investments. Intelligent voice robots can also cooperate with other artificial intelligence technologies. For example, chatbots can understand different languages or dialects by using natural language processing technology. By observing customers’ actions, robots can also accumulate and optimize the general internal process and system knowledge, understand customer intentions, and provide more feedback in line with customers’ needs. The chatbot can diagnose the problem and determine whether the problem can be solved through the general internal warning and rule-based judgment logic in the IT knowledge base. If not, it will be passed to the artificial IT Support team.

The demand for data labeling continues to increase

At present, the demand for the highest quality AI training data in various industries is urgent. AI is implemented in various fields, such as education, law, intelligent driving, banking, and finance, etc. Each field has requirements for subdivision and specialization.

Among them, in particular, traditional enterprises with intelligent transformation and technology enterprises need the assistance of training data service providers with rich project experience to help sort out the data labeling instruction and to obtain more suitable data. The use of high-quality data in special scenarios reduces the research and development cycle, accelerates the implementation process, and helps enterprises to make faster and better intelligent transformations.

In the process of in-depth industrial landing, there is still a gap between artificial intelligence technology and enterprise needs. The core goal of enterprise users is to use artificial intelligence technology to achieve business growth. Actually, artificial intelligence technology itself cannot directly solve all the business needs. It needs to create products and services that can be implemented on a large scale based on specific business scenarios and goals.

What we need to be clear is for AI companies and the entire industry, data annotation is an important part of the realization of artificial intelligence. The accuracy and efficiency of the labeled data affect the final result of the artificial intelligence algorithm model.

ByteBridge, a human-powered and ML-powered data labeling tooling platform

ByteBridge is a data labeling SaaS platform with robust tools and real-time workflow management. It provides high-quality training data for the machine learning industry.


  • ML-assisted capacity can help reduce human errors by automatically pre-labeling
  • The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
  • Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
  • All results are thoroughly assessed and verified by a human workforce and machine
ByteBridge: a Human-powered and ML-powered Data Labeling SaaS Platform

In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.

In addition, clients can iterate data features, attributes, and workflow, scale up or down, make changes based on what they are learning about the model’s performance in each step of test and validation.

Configure Your Own 2D Images Annotation Project

  • Developers can control the labeling project from setting labeling instructions to output review on a pay-per-task model with a clear estimated time and price
  • Real-time management and monitoring of project
  • Real-time Outputs: clients can get real-time output results through API. (We support JSON, XML, CSV, etc. And we can provide customizable datatype to meet your needs)
ByteBridge: a Human-powered and ML-powered Data Labeling SaaS Platform

These labeling tools are available: Image Classification, 2D Boxing, Polygon, Cuboid.

We can provide personalized annotation tools and services according to customer requirements.


A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.

NLP Service

We provide different types of NLP in E-commerce, Retail, Search engines, Social Media, etc. Our service includes Voice Classification, Sentiment Analysis, Text Recognition and Text Classification(Chatbot Relevance).

Partnered with over 30 different language-speaking communities across the globe, ByteBridge now provides data collection and text annotation services covering languages such as English, Chinese, Spanish, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Russian and more.


If you need data labeling and collection services, please have a look at, the clear pricing is available.

Please feel free to contact us:





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A data labeling platform with robust tools for real-time workflow management, providing high-quality training data with efficiency. —

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