How RPA is Transforming Banking: Insights from a Chinese FinTech Expert

Introduction

In recent years, AI technology has matured, driving explosive growth in the RPA market. Research reports predict a 61.3% annual compound growth rate for RPA, with the Asia‑Pacific region expected to grow 181% in 2021, positioning RPA as a major emerging trend.

Interview Overview

Q: Could you introduce yourself?

My name is Tao Zheng. I graduated in 2016 from Nanjing University of Science and Technology, then worked at Shanghai Transsion as a project and product manager. I joined the RPA project team of China Construction Bank (CCB) FinTech in early 2019 and became a qualified demand analyst by the end of that year.

Currently I serve as a product manager in the Process Robot team of CCB FinTech’s Big Data Center. My main responsibilities include demand analysis—identifying suitable RPA use cases such as internal account reconciliation and automatic invoice entry in finance. Since early 2019, we have catalogued over 100 RPA applications across multiple branches, covering finance, HR, IT, and hosting domains.

RPA Development Stage and Applications

Q: What is the current development stage of RPA in your enterprise and what applications exist?

Our RPA solution focuses on enterprise‑level capabilities, automating repetitive manual tasks and providing integrated operations monitoring, production management, and scheduling. It has been deployed throughout the CCB group, receiving positive feedback from various departments.

To date, more than 500 RPA applications have been launched, serving 12 head‑office institutions, 37 branches, 2 overseas entities, and over 12,000 users. Daily, the solution saves more than 4,800 work hours and supports finance, business operations, IT maintenance, and human resources.

Example: the anti‑money‑laundering information supplement application launched on May 25, 2019. By introducing RPA, it covered 50% of rule‑based scenarios, doubled efficiency, required only four bots, and achieved 100% processing accuracy.

Challenges in RPA Adoption

Q: What difficulties have you encountered during RPA implementation?

In the early stage, RPA awareness was low, making it hard to promote its concepts, scope, and benefits. We conducted extensive internal training, created promotional materials, and performed roadshows to educate branches. Overcoming these hurdles gave us a sense of achievement as each application went live and users began to adopt the technology.

Through this process, we learned the importance of thoroughly mapping business processes, uncovering RPA opportunities, and expanding the technology’s value. Working on RPA projects also deepened our expertise in both business and technical domains, enabling us to propose better solutions.

Future Directions

Q: What do you see as the future development path for RPA?

RPA remains a highly technical field with broad business coverage. Currently it mainly addresses repetitive mechanical tasks, but its potential is not fully realized. Integrating AI will enable intelligent RPA operations.

We envision RPA evolving through four stages:

Desktop automation (initial stage)

Process automation (current stage)

Self‑service automation (automatically identifying suitable scenarios and generating RPA libraries)

Intelligent automation (true digital employees)

Conclusion

Through Tao Zheng’s sharing, we see how CCB has implemented RPA—from initiation and promotion to diverse application scenarios—providing valuable reference for the industry. While RPA currently focuses on mechanistic tasks, its future lies in deeper AI integration to achieve truly intelligent automation.