Finance and performance
Collections Prioritization
Each delinquent account matched to the right treatment, inside the contact rules, measured in net recovery in your own ledger.
The problem
Collections often works every account in a delinquency bucket the same way. Many early-stage accounts would pay without contact, so uniform outreach costs money and turns payers into complaints, while the accounts that need a conversation wait.
Who owns it
Collections strategy, who own the treatment ladder. Operations works the daily queues, and compliance is a named approver on every contact policy.
What it learns from
- Core banking or the receivables ledger
- Payments and promises to pay
- Contact events in both directions
- Hardship, dispute, and complaint records
How it works
Predict, decide, act, measure
The same four steps in every solution, in this order, each one visible to the team that owns it.
- 01
Predicts roll and response
Models predict which accounts will roll to the next stage of delinquency and how each is likely to respond to each treatment, trained on point-in-time balances. Deceased, bankruptcy, hardship, dispute, and legal-hold accounts are excluded before any model sees them.
- 02
Decides the treatment through a policy
A cost-tiered ladder assigns each account a treatment for the cycle: watch with no contact, a digital nudge with a self-serve payment link, a call, a hardship or restructuring offer, a settlement within authority bands, or agency placement. Contact-frequency caps are enforced by the policy, never by the model, and a vulnerability signal always routes to a specialist.
- 03
Acts in the collections platform
Queue order and treatment codes land in the collections platform and dialer, and nudges go out through the self-serve portal. Calls and hardship conversations always stay with people.
- 04
Measures with champion and challenger cells
Randomized strategy cells compare net recovery, roll rates, promises kept, and complaints against the current strategy. The policy and the dialer reconcile contact events every day.
What it moves
The business levers
Cash recovered
Working capital released as more delinquent balances are recovered and fewer roll forward.
Cost to collect
A digital-first mix for the accounts that respond to it, with calls kept for those that need one.
Complaints
Fewer contacts with people who were going to pay, which is the measure a regulator sees.
Customer harm
Hardship and vulnerability are detected generously and handled by people.
Proof
How the return is proven
The return is net recovery, what was collected minus what the treatment cost, in the challenger cells over the champion, stated against the return model agreed up front and measured in your own ledger. Contact-cap adherence is reported as complete, or as an incident.
What you see: the ROI readout
Every solution produces the same readout, assembled from the system's own records rather than built by hand.
Correctness
The checks the pipeline passed on your own data: leakage, point-in-time, and entity resolution.
Incrementality
The incremental effect against the holdout or equivalent design, with its confidence interval.
Return
The measured return against the investment, set beside the return model agreed before work started.
Guardrails
Complaints, cost, margin, fairness, and contact limits, each shown against its bounds.
Audit extract
Every score, action, and outcome, with the model and policy versions behind it.
Governance
What is governed
Access, approvals, and the audit trail are part of the solution, not a review step added at the end.
Identity-scoped access
Account and treatment views are scoped by role through your single sign-on.
Approval gates
Contact policies are co-signed by compliance, and settlements beyond the authority band need approval.
Audit trail
Every contact decision is logged with its reason, in a form an examiner can read.
FDCPA and Regulation F
Call-frequency caps and contact rules under FDCPA and Regulation F, and their equivalents elsewhere, are hard constraints in the policy. In a bank, the model documentation supports model-risk review.
Why it holds up
Trained on your data, framed by the outcome
This solution trains models on your own data, checks every stage from data to decision, and is measured against the outcome it was built for. Because its predictive power is measured, it also shows the drivers behind the metric, so your team can fix causes as well as act on scores.
Why solutions hold upStart with one decision
Bring the problem and the systems your team works in. We scope the first solution with you, agree how its return will be measured, and run it in production with your people.