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AithozPM

Financial Services

Collections Intelligence

Using data and AI to help financial organisations optimise field and digital collections.

  • Client engagement
  • live
  • Behavioural modelling
  • Channel and treatment strategy
  • Field operations optimisation
  • Responsible collections design
Problem
Most collections operations treat a diverse book as if it were uniform. The same reminder cadence goes to a customer who simply forgot and to one in genuine financial distress. Field visits — the most expensive instrument available — are allocated by geography and queue age rather than by likelihood of recovery. Capacity is consumed on accounts that would have paid anyway, while the accounts that need intervention get it too late.
Solution
A data-driven collections layer that scores accounts on propensity and preferred channel, then assigns the treatment that fits: self-serve for customers who need only a nudge, digital contact for those who respond to it, and field capacity reserved for the cases where it changes the outcome. Field routing is optimised against recovery value rather than against distance alone.
Business impact
Recovery improves and cost-to-collect falls at the same time, because the two are not actually in tension — they are both consequences of allocating effort well. Customers in genuine distress reach a workable arrangement sooner, which is better regulatory outcome and better commercial one.

The problem worth solving

Collections is one of the few functions where the cost of the operation is a material fraction of what it recovers. That makes allocation the whole game — and most operations allocate badly, not through negligence but because the book is treated as homogeneous.

Three distinct customers sit inside the same overdue bucket:

  1. The customer who forgot. A single well-timed message resolves it.
  2. The customer with a short-term cash flow problem. They will pay, but need an arrangement.
  3. The customer in genuine distress. Pressure does not produce recovery here; a restructured plan sometimes does.

Treated identically, all three receive the same escalating cadence. The first is annoyed by contact they did not need. The second is pushed toward an arrangement that does not fit. The third receives expensive field visits that recover nothing and generate regulatory risk.

Meanwhile field capacity — by a wide margin the most expensive instrument in the book — is typically routed by postcode and queue age. Neither correlates with whether the visit will work.

How the engagement works

Segment on behaviour, not on arrears bucket

The starting point is a propensity model built on behaviour rather than on days-past-due alone. Payment history, channel responsiveness, prior arrangement performance and contactability turn one undifferentiated queue into segments that warrant genuinely different treatment.

Match the channel to the customer

Channel preference is a modelled property of the customer, not a policy default. Customers who resolve through a self-serve link should never occupy an agent’s time. Customers who ignore digital entirely should reach voice contact sooner rather than after four weeks of unread messages.

Reserve field capacity for where it changes the outcome

Field visits are treated as the scarce, expensive resource they are. Allocation is driven by expected recovery value against cost-to-serve, and routing is optimised across the day’s visit set rather than account by account.

Design for the regulator as well as the P&L

Collections strategy operates under conduct obligations, and rightly so. Vulnerability signals are treated as a routing input, not as an exception handled after a complaint. Treatment decisions are recorded with the reason attached, which is what makes the strategy explainable to a regulator rather than merely effective.

Where the value lands

  • Recovery rate. More of the book resolves, because the treatment fits the customer instead of the bucket.
  • Cost-to-collect. Field and agent capacity concentrate where they change the outcome, and stop being spent on accounts that would have self-resolved.
  • Customer outcomes. Customers in genuine difficulty reach a sustainable arrangement earlier, which is both the required outcome and the one that actually recovers more over time.
  • Explainability. Every treatment decision carries its rationale, which matters when the strategy has to be defended rather than just run.

Working with us on this

Collections work is engagement-based and shaped around the book, the channel mix and the regulatory environment it operates in. Start a conversation if this is a live problem for you.

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