An AI collections agent scores every open invoice for payment risk, puts the right accounts at the top of each collector's list, and sends reminders that match the customer's history and channel. Collectors spend their calls on disputes and relationships.
An AI collections agent is software that predicts which customers are likely to pay late, prioritises accounts receivable follow-up, and runs personalised dunning across email, SMS and WhatsApp. It learns from payment history, disputes, credit data and customer replies. It outputs a ranked daily worklist for collectors, sends approved reminders automatically, logs promises to pay, and flags disputes to the right owner.
Most AR teams work down an ageing report and send the same reminder to everyone. Reliable payers get nagged, slipping accounts get noticed too late, and disputes sit untouched in an inbox.
of US credit-based B2B sales are overdue according to the Atradius Payment Practices Barometer, US 2025.[1]
of US businesses saw DSO get longer over the past 12 months, against 45-day average terms (Atradius, 2025).[1]
DSO gap between top and median performers among the 1,000 largest US non-financial public companies (The Hackett Group, 2025).[2]
Disputes hide inside overdue balances and no reminder will collect an invoice the customer believes is wrong.
Scores each open invoice on the likelihood and likely days of delay, retrained on your own customers.
Each collector starts the day with accounts ranked by cash at risk, with the suggested action and talking points.
Reminders written for the account's situation and tone, sent at the time and channel that customer answers.
We connect read access to your ERP AR ledger and agree one metric, such as DSO or percentage collected by due date, on a chosen customer segment.
The model learns from 12 to 36 months of payment history. We show you which signals drive late payment in your customer base.
Collectors use the ranked list. Low-risk reminders go out automatically on approved templates; escalations wait for a person.
Results are compared with a control group of similar accounts worked the old way, so the effect is measured, not assumed.
| Criterion | Manual process | Off-the-shelf tool | Stratgik build + run |
|---|---|---|---|
| Prioritisation | Oldest and largest first | Rules or vendor-trained scoring | Model trained on your customers' behaviour |
| Channels | Email and phone | Email, sometimes SMS | Email, SMS and WhatsApp with reply handling |
| Disputes | Found on the call | Tagged manually | Detected in replies and routed to owners |
| ERP fit | Exports to spreadsheets | Strong on supported ERPs | NetSuite, SAP, Dynamics, Sage or custom |
| Proof of impact | Anecdotal | Vendor benchmarks | Control-group comparison on your ledger |
| Best fit | Few, large customers | Standard B2B terms on a supported ERP | Many accounts, mixed channels, US/UK/UAE customers |
Across the US, UK and Middle East, receivables are getting slower or staying slow. That makes collection effort one of the cheapest sources of cash a finance team controls.
Enter your credit sales and current DSO. The days reduced is an assumption; the pilot measures it against a control group on your own ledger.
Illustrative estimate using your inputs and stated assumptions, not a quote or guarantee. The pilot measures the real figure against your baseline.
21-day pilot on one entity or customer segment
per month, up to 2,000 active accounts
per month, multiple entities and WhatsApp
Usage (model tokens, SMS and WhatsApp message fees, cloud hosting) billed at cost; outreach follows your contact rules and applicable regulations; taxes excluded. GBP and AED prices are indicative conversions from USD.
An AI collections agent reduces DSO by acting earlier on the invoices most likely to be paid late. It predicts delay before the due date, moves those accounts to the top of the worklist, sends reminders that fit each customer, and surfaces disputes quickly so they are fixed rather than aged. Reliable payers get fewer contacts, so collector time goes where it changes the payment date.
Payment-risk prediction is usually strongest for customers with several months of payment history, and weaker for new accounts, where credit data and order patterns carry more weight. We measure accuracy on your own ledger by scoring past invoices the model never saw and comparing with what actually happened. Every score shows its main reasons so collectors can override it.
Business reminders by WhatsApp and SMS are widely used for B2B invoices, but rules differ by country and channel. WhatsApp requires opt-in and approved message templates; SMS rules vary between the US, UK and UAE. We configure consent tracking, contact hours and frequency caps to your policy, and your legal team approves the templates before anything is sent.
Our AI collections agent starts with a fixed 14,000 USD, 21-day pilot on one entity or segment. Ongoing Run plans start around 3,500 USD a month for up to 2,000 active accounts, with message fees and model usage billed at cost. We do not charge a percentage of cash collected, because the work is your team's with better tools.
Badly timed, generic dunning can; targeted reminders usually do the opposite. The agent contacts reliable payers less, writes messages in a tone suited to the account, never chases a disputed invoice, and hands sensitive accounts to a named collector. Key accounts can be set to draft-only, so a person reviews every message before it goes out.
It works with the AR ledger in NetSuite, SAP S/4HANA, Microsoft Dynamics 365, Sage Intacct, Xero and QuickBooks, and alongside AR tools you already use. Customer contacts can come from Salesforce or HubSpot. For bank remittances we connect through your bank's file exports or an aggregator. The pilot needs read access only.
A 30-minute call to pick the segment and metric, then a fixed-price 21-day pilot measured against a control group.