Business function
The most documented AI use cases for collections and recovery are collections and hardship agent, outbound notice drafting and litigation and recovery triage, across 24 public deployments by 23 organizations. Reminding, restructuring and recovering overdue payments with care for vulnerable customers.
Ranked by the number of organizations with a public deployment, as of 11 November 2026.
A voice and messaging agent that contacts customers in early arrears to collect payments.
AI for outbound notice drafting writes the letters and notices operations must send at scale, such as arrears notices or decline letters.
AI for litigation and recovery triage scores open insurance claims and overdue accounts for the risk that they end up in litigation or formal legal recovery.
Propensity to pay scoring is a machine learning model that estimates how likely each debtor is to pay, or to miss an upcoming payment, so collectors can pick the cases and contact approach.
AI subrogation detection is AI that reads open and closed claims to find cases where a third party is wholly or partly liable.
AI loan restructuring recommendations are an assistant that proposes the best hardship arrangement for a stressed borrower, for a person to approve.
23 organizations in 7 countries run these use cases in public. 63% of those 24 deployments disclose a measurable outcome.
A voice and messaging agent that contacts customers in early arrears to collect payments. It answers their inbound calls too, takes payments and sets up payment arrangements within preapproved rules, and recognizes signs of hardship or vulnerability so those customers go straight to a trained person.
AI for outbound notice drafting writes the letters and notices operations must send at scale, such as arrears notices or decline letters. It also covers complaint responses, servicing confirmations and product change notices, from case data and approved templates and clauses, in the customer's language, for a person to approve where the notice is regulated.
AI for litigation and recovery triage scores open insurance claims and overdue accounts for the risk that they end up in litigation or formal legal recovery. It also ranks the attorneys, law firms or recovery agencies likely to get the best result, so a claims handler, legal panel manager or collections specialist can act early instead of after the case has already escalated.
AI subrogation detection is AI that reads open and closed claims to find cases where a third party is wholly or partly liable. It estimates liability and the recoverable amount under the applicable negligence and recovery rules, and sends scored recovery opportunities with their reasons to the subrogation team.
Propensity to pay scoring is a machine learning model that estimates how likely each debtor is to pay, or to miss an upcoming payment, so collectors can pick the cases and contact approach. The score orders worklists and selects the letters, calls or support a debtor receives, while people still decide on payment plans, enforcement and write offs.
AI loan restructuring recommendations are an assistant that proposes the best hardship arrangement for a stressed borrower, for a person to approve. It assembles the borrower's position and tests options such as a term extension, rate relief, payment holiday or due date change against policy and affordability, then recommends the best fit with a written rationale.
Computed from the 24 public deployments behind these use cases. Numbers update with every checked record.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Cost reduction | 63% | 16% to 75% | 3 | 1 organization, 2 vendor |
