Case study
AI-augmented collections for a finance team
How we built a focused collections platform for a mid-size professional services firm: prioritized accounts, suggested next actions, clear invoice visibility, designed for the finance team that runs receivables.
Sector
Professional services
Team
Mid-size finance team
Engagement
Pilot to production build
Duration
6 to 10 weeks
The challenge
Manual prioritization and fragmented visibility were eating finance team hours
Before the engagement, the team handled collections out of spreadsheets and an ERP report that listed every overdue invoice equally. Deciding which account to chase first depended on memory and gut. Context for each dossier (last contact, escalation history, payment promises) lived scattered across notes, emails and CRM threads. The result: high-priority accounts slipped, low-priority accounts got over-pursued, and the team spent more time hunting context than acting on it.
The approach
Four steps, no surprises
We mapped the actual workflow, defined the platform's surface, shipped iteratively, then stayed in operation.
- 1Week 1
Review
Mapped how collections actually happen: who acts when, on what signal, with what context. Surfaced the prioritization and visibility gaps that the platform would close.
- 2Week 2
Blueprint
Defined the platform's surface: a prioritized queue, a per-dossier view, next-action suggestions, and the data model that supports them. Locked the scope before any code.
- 3Weeks 3 to 8
Build
Shipped iteratively. Queue and dossier view first, so the team could replace the spreadsheet. Then the AI-augmented prioritization, then the next-action suggestions.
- 4Ongoing
Operate
Monitor the platform in production, ship enhancements based on team feedback, tune the prioritization signals against actual collection outcomes.
What we built
A focused platform, not a generic dunning tool
Four surfaces the finance team uses every day. Each one replaced a chunk of context-hunting with a direct action.
Prioritized account queue
The team opens the platform and sees the accounts that need attention today, ranked by signals like overdue amount, age, broken payment promises and escalation context. Not every invoice equally. The most-leverage ones first.
Next-action suggestions
For each dossier, the platform suggests what to do next: send a reminder, escalate, schedule a call, log a payment promise. The team accepts, edits or rejects. AI-augmented, not AI-decided.
Per-dossier visibility
One view per account brings together overdue invoices, payment history, last contact, agreed payment promises and escalation level. The context the team used to hunt across systems is now one click away.
Live overdue dashboard
Aggregate view across all accounts: total overdue, breakdown by age bucket, top accounts, weekly trend. Finance leadership reads it on their own, instead of asking for a status report.
Outcomes
What changed in practice
Directional outcomes, observed after the platform went live and the team adopted it as their primary collections workspace.
Chase time per dossier shifted from hours of context-gathering to minutes of acting.
Overdue receivables over 60 days reduced meaningfully within the first two months of operation.
Finance leadership stopped asking for weekly status reports. The dashboard answered it.
Frequently asked
What teams usually want to know after reading this.
How long did the project take?
Six to ten weeks from review to production-ready. The first useful version of the queue and dossier view shipped inside the first month. The AI-augmented prioritization and next-action suggestions followed in the next iteration.
Is this an off-the-shelf platform or a custom build?
A custom build. Off-the-shelf collections tools either fit large-enterprise dunning workflows and overcomplicate things for a mid-size finance team, or fit retail consumer credit and miss B2B context. The platform was designed around the team's actual workflow.
Can Morsof build something similar for my finance team?
Yes. Whether the right answer is the same platform, a different platform with the same approach, or an extension of your existing ERP and CRM, we figure that out in the 30-minute review. You leave with a 1-page recommendation tailored to your collections workflow, even if you don't engage us.
Why is the client anonymized? Can you share more under NDA?
We keep client names off public case studies by default. Under NDA we can share a high-level overview of the architecture and the kind of outcomes the system produced. Anything deeper belongs to a later step, once we know what's actually relevant to your situation.
Want something similar for your team?
Book a 30-minute review. You leave with a 1-page recommendation tailored to your collections workflow, even if you don't engage us.