After the FCA confirmed its motor finance redress framework on 30 March, the sector’s first reflex was entirely predictable. Screens filled with headline numbers: total redress exposure, who took the largest provisions, and how far capital and funding costs would need to stretch. Those questions matter, but they only tell you who’s footing the bill for history. What they don’t reveal is who’s building a motor finance franchise that still looks investable five years from now.
If you want to see which lenders will emerge from the redress scheme with a genuinely resilient motor book, you need to change the lens. Stop asking who booked the biggest charge. Start asking where in the operating model you can move the forward economics of the portfolio. Collections, quietly but decisively, sits at the top of this list.
Collections is the real proving ground
Collections is where three realities collide in real time: live signals of household stress, the compound effect of years of underwriting and pricing decisions, and the practical test of conduct standards when money is short and tempers are frayed. If you want to know whether a lender has treated this redress scheme as a one-off compliance event or a structural reset, start by watching how it runs collections.
The constraints are clear. You don’t respond to a redress scheme by turning the screw on struggling customers; this is how you recreate the very harms that triggered a section 404 scheme in the first place.
But allowing viable accounts to drift into loss is no longer a background process failure. With higher funding costs, multi-billion-pound redress provisions, and tighter capital narratives, back-book leakage is now a board level problem. The hard work sits between these two walls. This is the space leading teams are moving into as they redesign what collections looks like.
Getting ahead of arrears
The most progressive shifts start before a payment is ever formally missed. Real time behavioral data has become a more reliable early warning system than any single arrears flag. It’s a request to move a payment date, trim a direct debit, switch contact channel, or query end-of-term options.
Once these micro signals reach a defined threshold, the account enters a different cadence, with human review where necessary, or proactive outreach with options that are specific, bounded, and affordable.
If the customer still falls into arrears, this same data becomes the foundation for genuine triage rather than queue management. Experienced collectors have always known that some customers will self-cure while others need intensive, tailored support. AI takes this tacit knowledge and turns it into a repeatable, auditable capability at scale.
Models trained on payment behaviour, prior contact patterns, and relevant external data can indicate, in near real time, who is likely to roll, who is likely to self-cure, and who responds to which contact strategy. Skilled agents then direct their scarce time where human judgement actually moves the outcome.
From blunt journeys to smarter strategy
Once you have a customer’s attention, what you do next has enormous impact. Many operations are still built around a handful of blunt journeys: letter, text, call, reminder, escalation. You can run a book that way, but you shouldn’t mistake it for strategy.
Even a basic experimentation capability opens up far more interesting questions as routine management information: Which early contact patterns lead to cures that still hold six months later? Where does additional flexibility in term, payment date, or balloon structure reduce re-default sufficiently to justify the cost? Which scripts and channels leave customers feeling informed and respected, and how does this show up later in complaints, data, retention, and vulnerability disclosures?
AI’s value here is methodological, not magical. It lets you test more variables across finer segments with clearer attribution of cause and effect. Over time, the data tells you which combinations of timing, tone, channel, and forbearance structure consistently move the metrics that matter most: cure and roll rates, complaint volumes, and vulnerability flags. The objective isn’t to automate judgement out of the system, it’s to stop relying on legacy journeys in a world never designed for them.
Self-service done properly
Channel strategy follows the same logic. Customers in difficulty are managing uncertainty, embarrassment, and competing financial pressures across their entire lives. Well-designed self-service takes this seriously, empowering customers to engage on their own terms, build repayment plans that genuinely fit their cash flow, and adjust as income and outgoings shift.
But self-service breaks down the moment firms treat it as the solution in its own right. Customers still need the option of a human conversation with someone who is informed, empowered, and not reciting a script they clearly don’t believe in.
This is where AI assistance proves its worth. It surfaces relevant policy detail, suggesting language that’s worked with similar customer profiles, flagging where a proposed arrangement conflicts with affordability or vulnerability data. It captures a clear rationale as the conversation unfolds. The human remains firmly accountable; the difference is that the quality of each interaction becomes more consistent, easier to defend, and more explicitly aligned with fair outcomes and Consumer Duty principles.
The Human-Plus-AI Collections Floor
Look ahead and the direction of travel is clear. A coordinated layer of AI agents will handle the groundwork. It includes verifying finance terms, checking valuations, modelling recovery curves across different strategies, testing for vulnerability indicators, and flagging any connection to redress history and scheme eligibility.
The human role in collections shifts from hunting for information toward interpreting it, weighing trade-offs and taking decisions. This is what a genuine human-plus-AI approach looks like in practice.
Done well, this changes more than the collections P&L. Losses fall because decisions are taken earlier, on better information, with a sharper sense of both risk and fairness. Customers move through arrears feeling informed rather than ambushed, even when the outcome is difficult. Complaints fall not because lenders have gone soft, but because outcomes make sense to a reasonable outsider and the route to those outcomes is properly documented. The audit trail transforms from a reluctant necessity into something you actively want regulators and auditors to see.
The opportunity hidden inside the redress bill
The FCA’s announcement has closed one chapter clarifying scope, timing, and the calculation of redress. The real story is how lenders convert this clarity into long-term competitive advantage.
The difference lies in whether executive teams treat collections as a cost centre to be digitised, or as the sharp end of the franchise where accountability, prudence, and genuine customer care meet in practice.
The lenders that get this right will be able to tell a cleaner capital story, defend their customer outcomes under intense regulatory and public scrutiny, and make a credible case for continued access to motor finance at scale in a market still digesting a historic redress bill.
The provisions will always matter. But the greater cost will fall on those who treat the FCA announcement as the end of the story and miss the opportunity to make collections smarter, faster, and more human.
About C&R Software
Trusted by 4 of the UK’s top 5 banks, C&R Software is the industry leader in AI native solutions for collections and recovery. Its flagship Debt Manager platform oversees more than $8 trillion across 20+ industries in 60+ countries. Learn more at inquiries@crsoftware.com.
