Technological progress and innovation are the key to the development of financial technology. With the increasing demand, the technology development is also increasingly mature.

Recently, Tencent Financial Research Institute, Tencent Cloud and KPMG jointly released the report "Data Real Symbiosis · 2022 Financial Technology Trend Outlook". The report puts forward the technological trends in the field of financial technology in the next three years.

Tencent financial technology experts emphasized that the following six trends will shape a credible, safe and intelligent future for the financial technology industry.

Rapid development of digital banking

Digital banking has improved the quality of life of many people. People can handle all kinds of business without waiting in line at bank outlets. In fact, the role of digital banks is more than that. They digitize banking business at all levels from the front end to the back end, so that different customers can access services anytime and anywhere.

In the process of digitalization, digital banks can also use digital technologies such as AI, big data and the Internet of Things to assess financial risks and user needs, and provide personalized services that meet the best interests of customers.

Recently, some banks in China are using WeChat applet Provide digital banking services. As WeChat One of the built-in functions, WeChat applet It connects users and enterprise services, and currently has more than 450 million active users. WeChat Users via Applet It is very convenient to access and use banking services.

Zero trust architecture (ZTA) creates a secure and trusted boundary

Zero trust architecture is a trusted environment, which requires that any subject entering the network should be verified first, including users in the organization network. Before authorizing the principal to access applications and data, the identity of the access principal will be subject to dynamic and continuous verification and management.

ZTA assists the banking industry in creating a secure access environment. Digital transformation of banks brings new challenges, such as data security and external access security. ZTA provides a layer of security guarantee for authentication, authorization and risk management to ensure the data security of customers when using financial services.

Federated learning improves data interaction efficiency

The financial technology industry often uses machine learning to predict financial risks, discover market opportunities, detect fraud, etc.

Banks need a lot of data to build accurate machine learning tools to serve enterprises in different industries. However, due to security and privacy issues, there are risks when enterprises share their data with financial institutions. Federal learning can play a role in this link.

This technology allows the machine to use decentralized data for training calculation, which means that the system does not need to have data, but can also conduct training, and the potential safety hazard no longer exists. In addition, it can help solve the limitation of small sample size, so that more SMEs can use machine learning tools.

At present, federal learning is widely used in granting micro loan services to small and micro enterprises, whose data sample size is generally scattered.

Low code development platform (LCDP) makes program development easier

With the emergence of new technologies, a large number of programming and coding work put forward higher requirements for scientific and technological talents in the financial field.

In order to improve the agile service capability of the financial industry, LCDP simplifies the lengthy coding process and provides a shortcut for application development. Developers can create applications through the platform's visual editor, thereby saving a lot of resources and time.

With LCDP, financial industry coders can quickly assemble and build applications without studying, writing, and testing manuscripts. After basic training, even non professional developers can create simple applications, which can make full use of manpower and reduce the pressure of professional developers.

Digital Assistant Robot Process Automation (RPA)

In recent years, more and more Chinese enterprises have begun to use RPA technology to automate various digital tasks. The workflow of the financial industry is tedious, and the task contains complex data. The RPA solution allows software robots to simulate human manipulation of digital systems, which means that a large number of repetitive tasks can be handed over to robots.

RPA is efficient and easy to use. As long as there is electricity, it can operate around the clock with high accuracy. Employees without IT background can also learn how to set instructions for robots to perform tasks. In addition, based on LCDP technology, the development and maintenance costs of RPA can also be reduced. Therefore, the cost for enterprises to use and update RPA software is low.

After integration with AI technologies such as image recognition, emotion analysis and OCR, RPA is expected to play a greater role in the financial industry.

Homomorphic encryption protects data privacy

Data analysis is an important pillar of the financial technology industry. The calculation process requires a large amount of data, which may cause data security problems. To avoid data leakage, the common method is to use encryption algorithms, but encrypting data is not convenient for data analysis, which will make the process more complex.

Homomorphic encryption allows users to analyze data without decrypting it, and the analysis results can be almost the same as the analysis results of decrypted data. This encryption method can promote data sharing among enterprises and reduce the risk of data leakage. However, due to the high operation and maintenance costs, this technology has not been widely used in the industry.