This standard specifies the technical requirements for the construction of Large Scale Artificial Intelligence (AI) Models in Financial Risk Management (FRM). It includes data collection and processing, model architecture design, training methodology design, evaluation metric selection. The standard also defines the application of pre-trained models to downstream financial risk assessment tasks. This standard applies to financial institutions and suppliers involved in the development and deployment of large scale financial risk models. It primarily focuses on loan default risk assessment in lending business. For people working on other risk types or other financial sectors, it provides a reference to build corresponding large scale FRM models.
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Status
- Active PAR
- PAR Approval
- 2025-06-19
Working Group Details
- Society
- IEEE Computer Society
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Working Group
-
LSAIM-FRM - Large Scale AI Models in Financial Risk Management
- IEEE Program Manager
- Christy Bahn
Contact Christy Bahn - Working Group Chair
- Chao Li
Other Activities From This Working Group
Current projects that have been authorized by the IEEE SA Standards Board to develop a standard.
No Active Projects
Standards approved by the IEEE SA Standards Board that are within the 10-year lifecycle.
3410-2025
IEEE Approved Draft Guide for Large Scale Financial Risk Models
This guide aims to establish a standardized reference framework and technical protocols for implementing large-scale artificial intelligence (AI) models in financial risk management. It serves as an essential guidance for financial institutions seeking to build, iterate, utilize their large-scale AI models for managing financial risks. The guide offers best practice on integrating various data knowledge, including feature space, sample, and model knowledge, into large-scale financial risk management models. It also provides strategic guidance on designing pre-training process, fine-tuning process, and evaluation methodologies to augment the feature and risk comprehensive capabilities of the large-scale models. Furthermore, it elucidates on the swift adaptation of the large models to various financial lending risk scenarios and the iterative process of the large-scale models.
These standards have been replaced with a revised version of the standard, or by a compilation of the original active standard and all its existing amendments, corrigenda, and errata.
No Superseded Standards
These standards have been removed from active status through a ballot where the standard is made inactive as a consensus decision of a balloting group.
No Inactive-Withdrawn Standards
These standards are removed from active status through an administrative process for standards that have not undergone a revision process within 10 years.
No Inactive-Reserved Standards