8 application scenarios of AI in financial field


liu, tempo Date: 2021-07-19 11:15:15 From:ozmca.com
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Artificial intelligence is recognized as the greatest revolution in human history, far surpassing the cognitive revolution, agricultural revolution and industrial revolution. Artificial intelligence has grown from a concept to gradually become the focus of global attention, and now it has risen to a national strategy. It has experienced more than 60 years of development. During this period, it has experienced two ups and downs. The third outbreak is due to many vivid application cases. From AlphaGo to perception (Microsoft Xiaoice) and then to decision-making (IBM Watson), artificial intelligence is surpassing human intelligence in more and more fields, and its application scenarios have also gone from laboratories to all walks of life.

 

 

At present, AI technology has been implemented in many fields such as finance, medical treatment, security, education, and so on, and its application scenarios are becoming more and more abundant. The commercialization of artificial intelligence has played a positive role in accelerating the digitization of enterprises, improving the structure of the industrial chain, and improving the efficiency of information utilization.

 

 

Every development of artificial intelligence is accompanied by breakthroughs in research methods, and deep learning is one of the most important representatives of breakthroughs in machine learning technology in recent years. With the continuous extension of artificial intelligence research and application fields, artificial intelligence will usher in the combined application of more kinds of technologies in the future.

Smart Finance

 

The applications of AI in financial field

 

 

Kaifu Li once had a famous view: “The best application field of artificial intelligence is Internet finance.”

 

 

Finance is an important application scenario of artificial intelligence. The application of artificial intelligence in the financial industry has changed the rules of the financial service industry. Traditional financial institutions and technology companies participate together to build a larger-scale high-performance dynamic ecosystem. Participants need to interact extensively with external parties to obtain the resources they need. Therefore, in the financial technology ecosystem, financial institutions and technology – a deep-level relationship of trust and cooperation will be formed between companies to enhance the commercial efficiency of financial companies.
The following focuses on the hot AI technologies in the financial field.

 

 

Natural language processing

 

 

Natural language processing is the use of computers to process, understand, and use human language. Because natural language is the fundamental sign that distinguishes humans from other animals. Without language, there is no way to talk about human thinking, so natural language processing embodies the highest task and state of artificial intelligence, that is to say, only when the computer has the ability to process natural language, the machine can be regarded as real intelligence. . From the perspective of research content, natural language processing includes grammatical analysis, semantic analysis, and text comprehension.

 

 

Speech recognition and speech synthesis

 

 

Speech recognition is a key technology to realize human-computer interaction. The problem to be solved is to enable computers to “hear” human speech, and then combine natural language processing technology to “understand” the meaning of human language through semantic understanding. Speech recognition technology mainly converts speech into computer-readable input through technical methods such as speech feature extraction, pattern matching, and model training. Speech recognition is an interdisciplinary subject, and the fields involved include signal processing, model recognition, probability theory, information theory, generation mechanism and auditory mechanism, artificial intelligence, etc.

 

 

Speech synthesis is to convert any text information into standard and smooth speech to read it aloud, allowing the machine to speak like a human. Speech synthesis includes three aspects. First, language processing should simulate the process of human understanding of natural language and give the pronunciation prompts of words, then prosody processing plans the characteristics of voice intensity according to the voice, and finally performs acoustic processing to output speech. With the addition of artificial intelligence algorithms, the simulation of timbre, emotion, etc. can be improved, making the synthesized voice more natural, and to a certain extent, it can reach the level of real human speech.

 

 

Object recognition
Object recognition is a basic research in the field of computer vision. Its task is to identify what object is in the image and report the position and direction of the object in the scene represented by the image. The current object recognition methods can be classified into two categories: model-based or context-based recognition methods, two-dimensional object recognition or three-dimensional object recognition methods. It is generally implemented based on big data and deep learning, and is applied to business scenarios such as image or video content analysis, and photo recognition.

 

 

Face recognition

 

 

Face recognition is one of the main fields of computer vision applications, and it is a way to identify identity by analyzing and comparing the visual feature information of the face. Face recognition technology can be divided into three processes: detection and positioning, facial feature extraction, and face confirmation. The application of face recognition technology is mainly affected by multiple factors such as illumination, shooting angle, image occlusion, age, etc. Face recognition is relatively mature under constraints, and face recognition technology is still improving under free conditions.

 

 

OCR recognition

 

 

The full name of OCR in English is Optical Character Recognition, and in Chinese it is called Optical Character Recognition. It is already the earliest and most mature achievement in the field of computer vision research. OCR uses optical technology and computer technology to read the text printed or written on paper, and convert it into a format that the computer can accept and understand.

 

 

Image search

 

 

Search for pictures with pictures, search for the same or similar pictures in the designated gallery, which is suitable for scenes such as precise picture search, similar material search, photo search for the same product, and similar product recommendation. With deep learning and large-scale machine learning technology as the core, it is realized through image recognition and search technology.

 

 

Biometrics
The content of biometric recognition technology is very extensive, including computer vision, voice recognition and other technologies. It mainly uses the inherent characteristics of the human body, such as fingerprints, facial features, iris, palmprints, voiceprints, and behavioral features such as handwriting, Voice, gait, etc. for personal identification. At present, as an important intelligent identity authentication technology, biometrics has been widely used in the fields of finance, public safety, education, and transportation.

 

 

Smart Customer Service

 

 

Intelligent customer service refers to the ability of users to answer simple questions and solve user’s questions about products or services through human-computer interaction. The maturity of natural language processing technology is relatively low in all kinds of artificial intelligence technology, but it can play a higher value in the field of customer service. Manual customer service has the problems of high training costs, difficulty in unifying service effects, and high liquidity. Intelligent customer service based on big data, cloud computing, especially artificial intelligence technology accelerates the intelligentization of enterprise customer service, relying on knowledge graphs to answer short-answer repetitive questions, reducing the use of manual customer service, and improving customer service efficiency and effectiveness. Customer service robots have replaced 40%-50% of manual customer service work. With the continuous improvement of technology, more customer service work will rely on artificial intelligence to complete.

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