1. Regtech for financial stability
Emmanuel Schizas
Lead, RegTech & Regulation
Cambridge Centre for Alternative Finance

2. Senior Management (MI)
Towards a RegTech taxonomy – what is really new?
Content Capture and Triage
Routing, CSA and Impact Assessment
Oversight and Governance
Data mostly internal
BPM applications
GRC applications
Voice Tech, Document Capture
Rules engines
Products
Senior Management (MI)
Obligations, policies, controls, standards
NLP, ML applications
Content
Business units
Oversight, Assurance and Risk Functions
Risk Types
Transactions
Reg Intelligence Platforms
Analytics, ML
Data mostly external
Supervisors
Counterparties
Data mostly unstructured
Data mostly structured
Source: JWG Group and author adaptations

3. Senior Management (MI)
Towards a RegTech taxonomy – what is really new?
Content Capture and Triage
Routing, CSA and Impact Assessment
Oversight and Governance
Data mostly internal
BPM applications
GRC applications
Need: volume, regulatory complexity, firm complexity
Integration: Demand for linkages, visibility and feedback
Talk: Hype among regulators, users and VCs
Walk: Industry fora and machine readable regs
Delivery: Cloud, APIs
Data and reporting standards: eg XBRL
Power: better and faster extraction, prediction
Voice Tech, Document Capture
Products
Senior Management (MI)
Obligations, policies, controls, standards
Content
NLP, ML
Business units
Oversight, Assurance and Risk Functions
Counterparties
Reg Intelligence Platforms
Risk Types
Analytics, ML
Data mostly external
Supervisors
Data mostly unstructured
Data mostly structured

4. Senior Management (MI)
Towards a RegTech taxonomy – some success stories
Content Capture and Triage
Routing, CSA and Impact Assessment
Oversight and Governance
Data mostly internal
BPM applications
GRC applications
Voice Tech, Document Capture
Rule engines
Codifying conduct or pricing obligations in trading
Products
Voice tech v market manipulation
Senior Management (MI)
Obligations, policies, controls, standards
Content
NLP, ML
Business units
Trade surveillance
Oversight, Assurance and Risk Functions
Transactions
Transaction reporting
CDD / KYC
Counterparties
Enforcement risk analytics
Reg Intelligence Platforms
Analytics, ML
Data mostly external
Risk Types
Supervisors
What most commercial success stories have in common is a fairly narrow focus on a long-trailed regulatory ‘big beast’ (Dodd-Frank, MiFID II, GDPR,)
Data mostly unstructured
Data mostly structured

5. Financial stability-related use cases
Operational risk analytics (endogenous ‘tail’ risks)
Network analysis revealing private knowledge to the market
Auditability of rules / senior management accountability
Real-time regulatory impact assessment / optimisation
Real-time risk assessment, linking to policies or controls
Resolution pack ‘translation’, reducing cost of acquisition
Intro centre 2 mins
Behavioural hurdles:
Is prudential supervision and resolution really rules-based?
Isn’t gaming of the regtech going to deepen blind spots?
Should we expect data, systems degradation in firms in distress?

6. Truly machine-readable regulation
What is still lacking?
A common language
Shared ontologies (internal & external)
Shared reference data (e.g. LEIs)
Truly machine-readable regulation
Treatment of ambiguity and discretion
Delivering on the promise of NLP and ML
Identification of obligations
Larger, more complete training datasets
Intro centre 2 mins

7. RegTech-enabled econ research: examples
More appropriate quantification of regulation opens the door to analysing its value as a public good – as an economic input or even as industry capital.
Crises and the bank lending channel of monetary policy transmission: mechanics of breakdown in transmission may be easier to test in a regtech enabled environment.
Mitigating contagion: Can regulatory MI provide credible signals to the market?
Intro centre 2 mins