Technological progress and innovation are the linchpins of fintech development and they are becoming increasingly sophisticated as demands arise. 

Recently, Tencent Financial Research Institute, Tencent Cloud, and KPMG jointly released the "Digital and Real Coexistence 2022 Top Six Trends of Fintech Trends Prospect Report." The report presents six technology trends in fintech over the next three years.

Our panel of experts from Tencent highlights six key trends in the fintech industry that will shape a trusted, secure, and intelligent future for the industry.

Growth of Virtual Banks

Virtual banks are improving the quality of life for many. You no longer need to wait in line at a bank to do banking in person. And in fact, digital banks mean more than that. They are digitalizing every level of banking, from front-end to back-end, making the services accessible to different customers anytime, anywhere. 

In the process of digitalization, virtual banks also make use of digital technology like AI, big data, and the internet of things to evaluate financial risks and user needs, offering personalized services that meet customers’ best interests.

Recently, there are local banks in China using Weixin Mini Program to provide digital bank services. Weixin Mini Program, which holds more than 450 million active users, is an in-app feature on Weixin that gives users instant access to enterprise services. It allows users of Weixin to access banking services online easier than ever.

Zero Trust Architecture(ZTA) Builds a Safety Net 

Zero Trust is a security framework that requires all users, including those inside an organization’s network, to be authenticated and authorized. User security configuration and posture need to be continuously verified before being granted access to applications and data.

ZTA plays an important role in the establishment of a trusted environment, especially in banking services. The digital transformation of banks is causing new problems such as data safety and business access security. ZTA offers another layer of safety for identity verification, authorization, and risk control, ensuring customers’ data safety and efficiency in using financial services.

Federated Learning Comes into Play

Machine learning algorithms are frequently used in the fintech industry to predict financial risk, discover market opportunities, detect frauds, and more. 

Banks need big data to create robust machine learning applications that serve companies in different sectors. However, it is risky for companies to share their data with financial institutions due to security and privacy concerns. That’s when the federated learning technique comes into play.

This technique allows the training of a centralized machine learning algorithm from decentralized data." In other words, the algorithm learns from the data without owning them and the security concern no longer exists. Besides, it helps solve the constraint of poor-quality data, making machine learning accessible to more SMEs.

Currently, federated learning is widely used in small loan services given to independent businesses, which usually have scattered data samples.

Low‑code Development Platform(LCDP) Enhances Accessibility

As new technologies emerge, programming and coding tasks are placing greater demands on the technology workforce in the fintech industry.

To enhance the financial industry's ability to provide agile services, LCDP simplifies the cumbersome coding process and provides a "fast track" for technological development. The platform allows developers to create applications through visual editors, which saves a lot of resources and time.

With LCDP, coders in the finance industry can quickly assemble new processes and build applications without having to research, write, and test new scripts. Even people with no professional knowledge in coding can create simple applications with basic training, making the best use of manpower and reducing pressure on professional developers.

The Digital Helper - Robotic Process Automation(RPA)

In recent years, more enterprises in China have started to use the RPA technology to automate digital tasks. The workflow in the finance industry is complex and the tasks are data-intensive. RPA is a solution that allows software robots to emulate actions humans perform on digital systems, which means a lot of repetitive tasks can be handed to the bots.

RPA is efficient and easy to use. As long as there is electricity, it can operate 24/7 with a high level of accuracy. Even people without an IT background can learn how to define instructions for the bots to perform. Besides, based on the technology of LCDP, RPA is inexpensive to develop and maintain. Enterprises can use and update their RPA software at low costs.

Being incorporated with AI technology like image recognition, sentiment analysis, and optical character recognition, RPA is expected to play a more important role in the industry.

Homomorphic Encryption Protects Data Privacy

Data analysis is a crucial pillar in fintech solutions. It requires a large amount of data in the computation process, which can cause data security concerns. Using an encryption algorithm is a common way to protect data against unwanted access but encrypted data complicates the process to analyze the data. 

Homomorphic encryption allows users to analyze data without decrypting it. And the results of the analysis are the same as those done by decrypted data. This encryption method can promote data sharing between companies while reducing the risk of data leakage. Nevertheless, the technology is still not widely used in the industry due to its high maintenance cost.