With the deepening of digital transformation in the financial industry, data has become a core asset, but balancing data privacy protection and risk control has always been a thorny issue. In recent years, breakthroughs in privacy computing technology have opened up a new era for the application of AI in financial risk control.
Privacy Computing is a collection of technologies that enable the circulation of data value without exposing raw data, mainly including federated learning, multi-party secure computing (MPC), trusted execution environment (TEE), and differential privacy. In the financial risk control scenario, institutions such as banks, insurance, securities, etc. have a large amount of sensitive customer data, and traditional data sharing models face strict compliance constraints. Privacy computing provides a technical path of "data available but invisible".
Federated learning is currently the most widely used privacy computing technology in the field of financial risk control. Multiple banks can jointly train anti fraud models without sharing raw customer data. For example, in the federal learning anti fraud project jointly carried out by ICBC and an Internet platform in 2025, the accuracy of model identification has increased by about 12%, while completely avoiding the cross-border flow of customer privacy data. This approach not only meets the requirements of the Personal Information Protection Law, but also realizes the innovative paradigm of "data remains unchanged and model is dynamic".
Multi party secure computing solves the problem of cross institutional data joint queries. In the credit approval scenario, different financial institutions can jointly query the borrower's multi position lending situation through MPC technology without exposing their respective customer lists. At the beginning of 2026, China UnionPay launched a cross bank risk control query system based on MPC, which has covered more than 30 commercial banks, with a single query delay controlled within 200 milliseconds, greatly improving credit approval efficiency.
The trusted execution environment provides hardware level isolation protection for highly sensitive data. TEE technologies such as Intel SGX and ARM TrustZone ensure that data is not intercepted by operating systems or other applications during processing, even on cloud servers. Several fintech companies have started applying TEE to real-time inference scenarios for credit card fraud detection models, providing hardware level security while ensuring inference speed.
Of course, the large-scale application of privacy computing in financial risk control still faces challenges. Currently, there is a lack of unified interoperability standards between privacy computing frameworks from different vendors, resulting in high deployment costs across institutions. At the same time, the additional communication overhead brought by encrypted computing cannot be ignored on ultra large datasets. But with the gradual improvement of national standards and the development of hardware acceleration technology, these technological bottlenecks are being overcome one by one.
It can be foreseen that privacy computing will become a key component of future financial AI infrastructure, enabling the financial industry to continuously unleash AI driven risk control innovation potential under the premise of data compliance.
The content of this article is comprehensively compiled from the official technical white paper of China UnionPay, the report of the Federal Learning Industry Alliance, and industry public technology sharing.