Top Ten Technological Trends of Financial Technology of the Year: Privacy Computing Blockchain is the key virtual person to reconstruct the financial manager

How should the technology trend of the fintech circle be seen?

Today, Peking University Guanghua-Duxiaoman Financial Technology Laboratory released the “2022 Global Financial Technology Top Ten Technology Trends”, covering privacy computing, large models, multi-modal learning, digital twins and other frontier fields.

Just after the first year of large-scale application of privacy computing , how will this year help the construction of the financial industry data ecology?

Large models , Metaverses , multi-modalities … How do these hotly-discussed technologies interact with finance?

And what cutting-edge technologies are already creating value for the industry?

Now I will take you to read the top ten technology prospects of financial technology of the year in one article. A quick overview is as follows:

Top Ten Technological Trends of Financial Technology of the Year: Privacy Computing Blockchain is the key virtual person to reconstruct the financial manager

Trend 1: “Data is available but not visible”, privacy computing helps the financial industry to build a data ecosystem

Privacy computing can realize the “availability and invisibility” of data in the process of circulation and integration. In the context of the rising demand for data interconnection and the continuous introduction of data security policies, Internet giants, technology companies, and financial institutions have entered the privacy computing industry one after another. In 2021, it is called the first year of large-scale private computing applications by the industry .

As a data-intensive industry , the financial industry has an urgent need for data interconnection and is the main industry for privacy computing technology. Privacy computing can help financial industry data flow under the premise of ensuring data security, and is mainly used in scenarios such as credit risk control, precision marketing, anti-fraud, and mobile payment face recognition.

In 2021, various countries will continue to strengthen data security legislation. For example, China has successively introduced a series of data regulations such as the “Data Security Law”, “Personal Information Protection Law”, “Credit Investigation Business Management Measures”, and South Korea issued “MyData” related legislation and data Service guide, strengthen data protection, etc.

In this context, with the successive release of privacy computing industry standards and the continuous iterative upgrade of related technologies, in 2022, privacy computing will play a greater role in the construction of financial data ecology.

Trend 2: Top companies increase their weight, and large models become the focus of global AI technology competition

With the continuous innovation of algorithms, the gradual increase of computing power and the massive explosion of data, pre-training large models has become a new direction of artificial intelligence. The basic training method of large models is self-supervised learning. Relying on complex pre-training targets and huge model parameters, it can store rich knowledge in a large number of implicit encodings of parameters, and can complete different tasks in different scenarios. The large model improves the versatility of AI and helps solve the problem of fragmentation of AI application scenarios.

Since OpenAI released the NLP (Natural Language Processing) pre-training model GPT-3 in 2020, large models have ushered in a global explosion and have become the focus of a new round of artificial intelligence technology competition. In 2021, Google released the trillion-level model Switch Transformer, Baidu released the Pengcheng-Baidu Wenxin knowledge enhancement large model, and Huawei released the Pangu large model… The large model began to expand from natural language processing to more fields.

In terms of application landing, the current large model is still in the active exploration stage of major institutions. With the continuous improvement of technical performance, the continuous maturity of the industrial model and the gradual establishment of the regulatory system, the large model is bound to set off a new wave of artificial intelligence applications.

Trend 3: With the help of “Metaverse”, a new wave of development in the VR/AR industry has emerged

With the gradual maturity of various links in the industry chain, the superimposed epidemic has promoted the increase in “zero-touch” demand, and VR/AR technology has entered a stage of rapid development after experiencing hype, trough, and recovery. In the “14th Five-Year Plan”, China even included “augmented reality/virtual reality” as one of the key industries of the digital economy.

In 2021, the concept of “Metaverse” is on fire. VR/AR can bring a new way of human-computer interaction, which is regarded as a hardware interface between the Metaverse and the real world. With the help of Metaverse, the VR/AR industry has ushered in a new round of development opportunities. According to IDC forecasts, global VR products will grow by about 46.2% year-on-year in 2021, and the compound growth rate for 2020-2024 will be about 48%. In 2025, global AR equipment shipments will reach 24.4 million units.

In 2022, Apple is expected to release its first MR product, Oculus may release Quest Pro to build the next VR flagship model, and Sony is expected to release a new generation of PS VR headsets.

Top Ten Technological Trends of Financial Technology of the Year: Privacy Computing Blockchain is the key virtual person to reconstruct the financial manager

Trend 4: Multi-modal learning is favored, giving birth to diversified application scenarios of artificial intelligence

Multimodal learning first began in 1970, and after several stages of development, it entered the deep learning stage after 2010. One of the earliest multi-modal research applications is audio-visual speech recognition. By fusing the two modalities of video and sound, multi-modal learning begins to show its excellent learning ability.

Since 2020, in the face of the normalized protection needs of “wearing masks” and “zero-touch” epidemics, as well as the personal privacy risks of frequent leakage of single biometric information such as fingerprints and faces, multi-modal solutions have begun to be favored by the market. With its high accuracy, strong security, and wide application scenarios, multi-modal biometrics is becoming the mainstream of the market, and is gradually being used in multiple scenarios such as finance, public security, immigration, security, and education.

In 2022, under the theme of strengthening the protection of personal information, multi-modal biometrics will integrate the advantages of multiple biometrics. It can flexibly choose appropriate technology integration methods and decision weights, and can adapt to changes in needs in different application scenarios. More landing application scenarios.

Trend 5: Low-code and no-code applications are heating up, accelerating digital transformation in the financial sector

In 2014, Forrester Research put forward the concept of “low-code development platform”, and then Gartner named this category with aPaaS-based high-productivity platform (hpaPaaS). In 2018, Siemens acquired Mendix, a manufacturer in the field of low-code application development, and low-code platform OutSystems received US$360 million in financing. The development of low-code applications is rapidly heating up in overseas markets.

In the domestic market, the contradiction between the expansion of software demand driven by digital transformation and the existing R&D system is increasing, and the traditional architecture cannot cope with the changing market demand. Low code/no code can better solve this problem.

Low-code/no-code development platforms can release productivity in the financial sector and accelerate the digital transformation of the financial sector. On the one hand, the low-code/no-code development platform can meet the changing business needs of the organization at any time; on the other hand, it can realize architecture visualization, modeling visualization, and development visualization. In 2022, low-code/no-code will drive the financial sector to achieve faster business innovation.

Trend 6: The cloud-native technology ecosystem is gradually expanding, injecting new momentum into the innovation and development of the financial industry

In 2015, the Cloud Native Application Foundation (CNCF) was founded, and the development of cloud native technology and applications began to enter the fast lane. In recent years, the cloud-native technology ecosystem has also expanded from its early focus on technical fields such as containers, microservices, and DevOps to many branches such as underlying technologies, orchestration and management technologies, security technologies, monitoring and analysis technologies, and scenario-based applications.

With the deepening of enterprise digital transformation, enterprise applications need to be built based on cloud-native technology, architecture and services. In 2021, global cloud native applications will continue to rise. Cloud-native applications can be flexibly expanded and scalable. Through cloud-native transformation, server resources can be maximized and server costs can be effectively saved. At the same time, the cloud-native application platform can increase the iterative speed of business applications and flexibly respond to the needs of various scenarios.

At present, some innovators in the financial technology field continue to innovate based on cloud-native architecture in terms of technical architecture, product iteration speed, user experience improvement, and customer portrait accuracy. According to Gartner’s forecast, by 2022, 75% of global companies will use cloud-native containerized applications in their production.

Top Ten Technological Trends of Financial Technology of the Year: Privacy Computing Blockchain is the key virtual person to reconstruct the financial manager

Trend 7: Industry attention is increasing, and digital twin generation is an important starting point for the digital transformation of enterprises

In 2003, Professor Michael Grieves of the University of Michigan proposed the concept of “digital twins” for the first time. Digital twins were first used in the aerospace field. With the maturity of the new generation of information technology, digital twins have begun to expand into vertical industries such as smart manufacturing, smart cities, transportation, medical care, and agriculture, becoming an important driving force for promoting the digital transformation of enterprises and promoting the development of the digital economy.

Currently, the world is actively deploying digital twin applications. In 2020, the United States and Germany have established the Digital Twin Alliance and the Industrial Digital Twin Association respectively to accelerate the construction of a digital twin production industry collaboration and innovation ecosystem. In China, exploring the construction of digital twin cities has been written into the “14th Five-Year Plan” and has become a national development strategy. The Global Industry Analysts report shows that the global digital twin market will be worth US$4.6 billion in 2020 and will reach US$28.7 billion in 2026.

As digital twin technology continues to mature, it can also be used in financial scenarios in the future. Financial institutions can use digital twin technology to build “digital managers” to provide customers with personalized and customized services. They can also be applied to financial product development and iterative design of products in the digital twin model.

Trend 8: Provide core technological capabilities and blockchain promotes supply chain finance to the 3.0 era

In today’s world, countries compete with each other, and the manufacturing industry has been placed in an increasingly important position. In the final analysis, the competition in the manufacturing industry is the competition between the industrial chain and the supply chain. Therefore, building an efficient supply chain financial system becomes crucial. Since the epidemic, eight ministries and commissions including the Central Bank have joined the “Opinions on Regulating the Development of Supply Chain Finance and Supporting the Stable Cycle and Optimizing Upgrading of Supply Chain Industry Chains” to promote the development of supply chain finance and encourage “the use of blockchain, big data, artificial intelligence and other new technologies. A generation of information technology will continue to strengthen the security of supply chain financial service platforms, information systems, etc.”.

Vigorously develop supply chain finance, or will become the next home of fintech companies. In September 2021, Xu Renyan, the former president of Zheshang Bank, joined Duxiaoman Finance to be responsible for the supply chain financial technology business and concurrently serves as the chairman of Duxiaoman Supply Chain Technology Co., Ltd. It can be seen from the personnel changes that Duxiaomanga is in the supply chain The intention of the financial technology layout. In December, Du Xiaoman applied blockchain technology to support small and medium-sized enterprises in issuing the first supply chain innovative direct-financing product on the Beijing Financial Assets Exchange. Another giant online business bank also launched a digital technology-based supply chain financial solution “Dayan System” in October 2021.

At present, blockchain technology is mainly used in the supply chain 2.0 model around core enterprises to solve collaboration problems, trust problems, digital credit certificate problems, and automatic contract performance problems. In the future, by combining technologies such as the Internet of Things and big data to build a blockchain-based collaboration network and asset network, blockchain technology is expected to fundamentally change the business model of supply chain finance, and promote supply chain finance to the core enterprise 3.0 The model is moving forward.

Trend 9: “Never trust, always verify”, zero trust builds a financial network security barrier

Different from the traditional border-based network security method, Zero Trust is based on the principle of “never trust, always verify”, takes identity as the basis of access control, and minimizes real-time authorization as the core, continuous authentication, dynamic access control, authorization, Auditing and monitoring are a chain, so as to realize dynamic and continuous trust assessment.
In 2021, the United States issued a number of zero-trust policies including the “Federal Zero-Trust Strategy” and “Zero-Trust Maturity Model”; 82% of European companies increased their budgets for zero-trust construction; in China, from the Ministry of Industry and Information Technology in 2019 Since the release of the “Guiding Opinions on Promoting the Development of the Cyber ​​Security Industry (Draft for Comment)” listed “Zero Trust Security” as a “key cyber security technology that requires breakthroughs”, domestic Internet giants and technology companies have also continued to explore the layout of zero trust .

Trend 10: Cloud, edge, and end collaboration, and edge computing help financial institutions to create a dual advantage of “security + efficiency”

Edge computing is regarded as an extension of cloud computing, which complements and develops in coordination with cloud computing. Edge computing has strong real-time data processing and analysis and can provide users with higher real-time services. At the same time, because edge computing is only responsible for tasks within its own scope, data processing is based on local, and data transmission is highly secure. Edge computing does not need to use too much bandwidth, the load of network bandwidth is reduced, and the energy consumption of smart devices is further reduced.

With the continuous maturity of technologies such as 5G and the Internet of Things, edge computing is rapidly emerging. Faced with the challenges of massive data computing, new computing scenarios, and real-time processing of small data, edge computing is becoming an important computing platform for the implementation of technologies in the data age, continuously meeting the needs of application intelligence, real-time business, and privacy protection in the digital transformation of various industries.

For many scenarios with high real-time and strong security in the financial field, edge computing can bring more possibilities for their development. As the edge is given more and more data storage and computing resources, edge computing may become the mainstream deployment in the future.

*This article is qubit authorized to be published, and the opinions are only the author’s.

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