SupTech
SupTechAlso called supervisory technology
SupTech is the use of technology by financial supervisors to collect, process and analyse the data they need to oversee firms. Examples include automated data collection, machine-readable reporting, network analysis of transactions and language models that review firm documents. It is the supervisor's counterpart to RegTech.
How it works
Supervisors replace static template returns with data pipelines that pull granular data directly from firms or trade repositories. Analytics tools then flag outliers against peers, spot links between accounts in suspicious activity data and scan board papers, complaints and websites for risk signals. Supervisory teams use the output to decide which firms to contact and which questions to ask.
For regulated firms, SupTech means the data they submit is read by software as well as people. Inconsistencies across returns, late filings and weak data quality are easier for supervisors to spot. The Financial Stability Board reported in 2020 that most member authorities responding to its survey had a SupTech, innovation or data strategy, and that use of such strategies had grown significantly since 2016.
The rules
Reviews how supervisors and firms use SupTech and RegTech, with 28 case studies, and flags resourcing, cyber risk and data quality as concerns.
FSB member authorities and regulated institutionsCommon questions
What is the difference between SupTech and RegTech?
RegTech is technology that regulated firms use to meet their obligations, such as screening, monitoring and reporting tools. SupTech is technology that supervisors use to collect and analyse data and to oversee those firms.
What are examples of SupTech?
Common examples are automated data collection platforms, granular data reporting, analytics that compare firms against peers, network analysis of suspicious activity reports and natural language processing that reviews firm documents and complaints.
How does SupTech affect regulated firms?
Supervisors can analyse submitted data faster and across more firms. Errors, outliers and inconsistencies between returns are more likely to prompt questions, so firms put more weight on data quality and reporting controls.
