RegTech in regulated firms: evaluating tools without adding risk
The market for regulatory technology tools has expanded rapidly, driven by increasing compliance complexity, availability of machine learning capabilities, and the competitive dynamics of a sector where technology vendors are competing to serve compliance teams with limited budgets and stretched resources. RegTech tools now span the full range of compliance functions: automated KYC and AML screening, transaction monitoring, trade surveillance, regulatory reporting, policy management, and Consumer Duty outcome monitoring. The FCA has encouraged innovation in this space through its regulatory sandbox and TechSprints, and has been broadly supportive of firms using technology to improve compliance outcomes. However, the adoption of RegTech tools introduces specific risks that compliance officers and boards should not underestimate.
The most fundamental risk is the delegation risk. Regulated firms cannot outsource regulatory responsibility to technology vendors. This principle applies to RegTech as much as to other outsourced services: if a transaction monitoring tool fails to generate alerts on suspicious activity because its rules are misconfigured, the firm — not the vendor — is responsible for the resulting AML failure. This requires firms to retain meaningful oversight of their RegTech tools: understanding the logic of alert generation, reviewing the calibration of rule sets, validating the output against independent sources, and ensuring that human review of alerts is genuinely substantive rather than rubber-stamping. Firms should also assess whether their reliance on a particular vendor creates concentration risk, and whether they have adequate contingency arrangements if the tool becomes unavailable.
Model risk is a specific concern for AI-assisted RegTech tools. Where a tool uses machine learning models to classify customer risk, detect anomalous behaviour, or generate regulatory outputs, firms should apply model risk management principles: understanding the training data and its limitations, testing for bias, validating model outputs against known outcomes, and maintaining human oversight of decisions generated by the model. The FCA's AI principles (discussed in its 2022 AI and Machine Learning guidance and updated in 2024) apply to compliance uses of AI as much as to customer-facing applications.
Data quality is consistently the biggest practical challenge in RegTech implementation. Tools that rely on poor quality, inconsistent, or incomplete data will generate unreliable outputs. Firms frequently underestimate the data remediation work required before a new RegTech tool can be deployed effectively. A structured data readiness assessment — reviewing data completeness, consistency, and currency across the systems that will feed the new tool — should be a mandatory pre-implementation step. Where data quality issues are identified, the remediation plan should be completed before go-live, not treated as a post-implementation improvement.
Procurement and due diligence
RegTech vendors should be subject to the same third-party due diligence as other material outsourced service providers. This includes operational resilience and data security assessments, review of sub-processor arrangements (particularly relevant where tools process personal data or sensitive client information), and contractual provisions covering data access, audit rights, and exit. Firms should also assess whether the vendor's financial stability is adequate and whether the tool has been audited or accredited by an appropriate third party.