Top 10 AI application innovation cases in the financial field in 2023

Review:

In 2023, the field of financial technology is in the limelight, and AI application innovation is emerging one after another. This article will reveal the top 10 most compelling AI application innovation cases of this year, giving you a glimpse of AI in the field of financial technology.

First, the application status and development trend of AI in the field of financial technology

1. The scope of application continues to expand, and the market scale continues to grow

With the continuous development of technologies such as big data, cloud computing and deep learning, the scope of application of AI in the financial field continues to expand. From robo-advisors providing investors with personalized investment advice, to intelligent risk control to quickly and accurately assess loan risks, to smart insurance providing personalized insurance products according to customer needs and risk levels, AI applications have penetrated into various business fields in the financial field.

According to The Business (2023), the global artificial intelligence market in the financial technology field will grow from US$9.15 billion in 2022 to US$11.59 billion in 2023, with a compound annual growth rate of 26.8%, and it is expected that the artificial intelligence market in the financial technology field will reach US$31.71 billion in 2027, with a compound annual growth rate of 28.6%.

2. The rise of financial big models and the application of AI agents

2023 is the first year of the domestic financial big model, Du Xiaoman launched the first 100 billion-level Chinese financial model "Xuanyuan" in China, and immediately released the country's first retail financial model - "Sky Mirror". Compared with a dedicated model, a large model is more like a "person" who can think and learn, rather than a "machine" only suitable for specific scenarios, with greater versatility and content generation capabilities, and can be applied in a wider range of fields.

Although the large model will think, the output depends on the prompt word, the length of the memory context is limited, and the AI agent is used to add "eyes" and "hands and feet" to the large model, and because of the importance of AI agent, AI agent is considered the future development direction. In the future, AI intelligent twins can be applied to intelligent investment advisory and personalized financial management, intelligent risk control and anti-fraud, intelligent collection and voice robots.

The application of AI in the field of financial technology has entered a new stage of development, from large models to AI agents, AI will bring more innovation and change to the financial industry.

2. Top 10 AI application innovation cases of fintech in 2023

Case 1:

Mobile payments and smart cards

With the popularity of mobile payment, AI technology has made the payment experience more intelligent. Alipay and WeChat Pay, well-known domestic payment platforms, have launched smart card functions, allowing users to easily complete consumption and transfer payments by simply holding their mobile phones close to payment terminals. In addition, Google's Google Pay uses machine learning technology to provide users with personalized payment suggestions and offers by analyzing users' spending habits and preferences.

Case 2:

Robo-advisory and personalized financial management

Robo-advisor provides users with personalized asset allocation solutions through artificial intelligence technology. WarrenQ-Chat, an intelligent investment research platform jointly launched by Hang Seng Electronics and Hang Seng Juyuan, pursues the accuracy of financial information, allowing users to easily obtain financial quotes, information and data through dialogue instructions, and each generated dialogue supports the original text traceability to ensure that the source of the message can be traced. In addition, US robo-advisory platforms such as Robinhood and Wealthfront are also popular among users, providing users with personalized investment advice and asset allocation solutions through AI technology. These intelligent investment advisory services improve the accuracy and efficiency of financial services through AI technology, and have become an important direction for financial institutions to pursue technological innovation.

The difference between traditional investment advisory and robo-advisor:

Case 3:

Digital currency and blockchain

The application of digital currency and blockchain technology has brought new growth points to the financial field. Digital currencies such as Bitcoin and Ethereum are sought after by users around the world, while blockchain technology is widely used in the financial industry for regulation, trading and more. The combination of digital currency and blockchain technology has promoted technological innovation in the financial field and brought new business growth points and competitive advantages to the financial industry. The intelligent system of the prevention and control center that uses blockchain technology to supervise the local financial industry off-site can realize real-time synchronization of core information such as funds, assets, and transactions of financial institutions, and discover and timely warn enterprise risks in real time.

Case 4:

Intelligent risk control and anti-fraud

Intelligent risk control uses a risk control system built by multiple technologies to investigate customer background and use it for credit assessment, credit approval, post-loan collection, etc., which greatly improves the efficiency and customer experience of the financial industry. Through AI technology, banks can timely detect potential fraud risks and provide early warning, and at the same time build more comprehensive risk assessment models to improve the accuracy and efficiency of risk control. Intelligent risk control and anti-fraud have become the guardians of the financial industry and escort the safety of users' funds.

Case 5:

Intelligent collection and voice robot

The application of AI technology in the field of intelligent collection has become increasingly mature. The intelligent collection system analyzes the user's repayment behavior and credit status through machine learning technology, and provides personalized collection strategies for financial institutions. Voice bots are able to understand longer contexts and anthropomorphize thinking and answering, communicating more naturally. The application of intelligent collection and voice robots makes services more efficient and user experience better.

Case 6:

Smart insurance with personalized pricing

Smart insurance uses artificial intelligence technology to realize functions such as self-service insurance, business handling, risk pricing, and image loss assessment. Insurers are able to more accurately assess risk and develop personalized insurance pricing strategies based on customer needs and risk levels. New China Insurance's Magnum intelligent underwriting system is a good example, which uses AI technology to shorten the underwriting time and improve the efficiency and quality of insurance services. Smart insurance subverts the traditional insurance model and provides users with a more convenient and personalized service experience.

Case 7:

Virtual Banking and Digital Services

Virtual banking is a financial service model that has emerged in recent years, providing convenient financial services through technological means such as the Internet and mobile devices. The application of AI technology enables financial institutions to provide financial services more intelligently, such as virtual banks such as Atom in the UK and Neos in Germany to digitize and personalize financial services through AI technology.

Case 8:

Quantitative investment and AI algorithmic trading

In the field of financial investment, AI technology has become an important weapon to improve efficiency and accuracy. The combination of quantitative investment and AI algorithmic trading makes the investment strategy more scientific and accurate. Through big data analysis and machine learning algorithms, quantitative investment platforms such as Quantopian can achieve efficient algorithmic trading and asset management, making financial investment more intelligent.

Case 9:

Sentiment analysis and customer relationship management

The application of AI technology in the field of sentiment analysis and customer relationship management has become increasingly mature. Financial institutions use AI technology to analyze users' emotions and emotional states, so as to provide users with more intimate financial services. Sentiment analysis platforms such as Beyond Verbal in the United States and Pintail in the United Kingdom have achieved innovation in sentiment analysis and customer relationship management through AI technology, making financial services more humane.

Case 10:

RegTech and compliance management

With the rapid development of fintech, regtech has also become an important means to improve regulatory efficiency and accuracy. Regulatory authorities and financial institutions can achieve more intelligent and efficient regulatory compliance management through AI technology. The Prevention and Control Center uses artificial intelligence, big data, cloud computing, blockchain and other financial supervision technologies to improve risk research and early warning capabilities and rapid emergency response capabilities, solidly promote the construction of local financial risk monitoring and prevention systems, and achieve good results in carrying out local financial risk investigation, monitoring and early warning, local financial format supervision, risk disposal and resolution, etc., and creating the "Guangzhou Model" of local financial risk prevention and control.

3. How to grasp the development opportunities of financial technology and AI?

With the continuous development of fintech and AI technology, financial institutions need to actively embrace change and seize the opportunities brought by technological development. First of all, increasing the level of digitalization and intelligence is key. In the open source large model, such as Llama 2, Baichuan model, Tongyi Qianwen model, etc., choosing a large model suitable for the financial industry for fine-tuning can reduce costs and quickly improve efficiency. Secondly, pay attention to user experience, start from user needs, and combine AI technology to provide more intelligent, personalized and convenient financial services. Finally, strengthen internal technical team building and technology R&D investment, and cultivate financial technology talents to adapt to the rapidly changing technology environment.

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