As large-scale remediation programmes such as the motor finance review continue to unfold, the Financial Conduct Authority (FCA) has made clear that every single compensation outcome must be fully auditable and transparent.
This principle — known as explainable redress decisions — places a significant compliance burden on firms, requiring them to reconstruct the entire decision-making process for any given customer and demonstrate that their approach aligns with FCA redress guidance. Falling short of this standard is not an option; firms that fail risk enforcement action, reputational damage, and even the wholesale invalidation of their redress scheme.
IntellectAI, which offers AI tools for wealth and insurance firms, recently delved into what makes a redress decision explainable to regulators.
At the heart of this requirement is what regulators refer to as decision lineage — a structured, chronological record that traces the path from raw customer data all the way through to the final compensation figure. This lineage must capture the precise source of the customer’s loan amount and payment history, the specific eligibility criteria that triggered their inclusion in the scheme, the compensation formula that was applied, and any assumptions or manual overrides that were made along the way, it said. Crucially, it must also record the final payout figure alongside the relevant payment transaction identifier.
Both the FCA and the Financial Ombudsman Service (FOS) need to independently verify that automated or manual calculations genuinely reflect the firm’s regulatory obligations, and the explanation produced must be comprehensible not just to auditors but to the affected consumer themselves.
The growing use of automation in remediation programmes introduces additional complexity. Where advanced algorithms are deployed, there is a real risk that systems become so-called “black boxes” — producing outputs that cannot be traced back to any transparent logic. This is where the push for explainable AI in redress becomes particularly relevant, IntellectAI stated.
RegTech solutions must ensure that data extraction tools output confidence scores and flag areas of uncertainty, that the rules engine logic is written in human-readable code that maps directly to FCA guidance, and that the audit trail itself is immutable — meaning it cannot be altered after the fact.
For more insights, read the full story here.
Copyright © 2026 FinTech Global
Copyright © 2018 RegTech Analyst
