Part 3: Using AI in Redress and Remediation: Preparing for the Car Finance Mis-selling Regulations

In this series, we are looking at how to plan ahead for potentially large-scale remediation projects, and how developments in technology, particularly Machine Learning (ML) and Artificial Intelligence (AI), can impact efficiency and cost-effectiveness in such projects.

The Financial Conduct Authority (FCA) announced that it has extended the Discretionary Commission Agreement (DMA) ruling from September 2024 to May 2025. Reportedly, these delays are due to affected organisations ‘struggling to supply the data needed in the requested time’. This further highlights the needs for such firms to continuously gather, understand and optimise their data. Ongoing data management eases the process of addressing gaps, particularly ahead of such decisions.

The FCA’s recent update also pointed out that a consumer redress scheme the likely outcome upon review of the current data. The timescale for potential mis-selling of Personal Credit Finance (PCP) deals runs between 2007 and 2021. This outcome could mean large-scale projects, resource utilisation and costs for the financial lenders affected. It’s likely that the FCA will also set a completion date. Lenders must consider, and plan for this, at the earliest opportunity.

At Talan UK, we aim to apply our 20+ years of experience in Redress & Remediation and Regulatory Projects to help firms set up for success in such projects. Alongside lessons learned over the years, we have taken the view that companies can further improve their efficiency with modern processes and technologies, such as Machine Learning (ML) and Artificial Intelligence (AI).

Taking a closer look at the elements of the Closure stage of a remediation project, we’ve identified key areas that organisations should consider when planning redress programmes:

• Identify the measures and KPIs required for the completion date.
• Ensure the processes planned for the project are auditable, can be evidenced, and the correct governance is in place.
• Make technical validation a priority from the outset. In doing so, you avoid re-doing large elements of the project. An experienced testing function is crucial.

Here’s where we would apply ML and AI to add efficiencies to the Closure stage…

Regulatory Reporting

Data analysis tools leveraging Natural Language Processing (NLP) enable the automated compilation of reports. Lenders can use this to gather their evidence to show compliance with regulatory requirements.

Stakeholder Engagement

By using automated survey generation and analysis techniques, we can engage in sentiment analysis. Lenders can use this to study stakeholder views and feedback, informing final review discussions. Predictive analytics models can forecast the impact of any unresolved issues on project closure. Lenders can choose to deploy interactive visualisation tools. These will present project outcomes and performance metrics, facilitating data-driven discussions with stakeholders.

We hope our remediation blog series has proven a useful starting point. Get in touch for more information on how Talan UK can help you prepare ahead of the FCA ruling in May 2025.