AI agents automate credit control end-to-end: personalised multi-channel collections, billing query resolution, dispute and deduction management, supplier portal updates, and credit risk assessment. Legacy and SaaS AR platforms automate reminders. AI agents handle the conversation.
Credit control has always been a conversation-heavy function. Chasing overdue invoices, resolving billing queries, managing disputes, negotiating payment plans, updating supplier portals, assessing credit risk. Every one of these tasks involves back-and-forth communication with customers, internal stakeholders, or both.
The tools built for credit control over the past two decades automated a fraction of this: outbound payment reminders on a schedule. The rest stayed manual. AR teams still spend their days in shared inboxes, reading customer replies, pulling up account data, drafting responses, logging into supplier portals, and chasing approvals internally. The reminder goes out automatically. Everything that happens after it does not.
AI agents change this. They handle the full credit control conversation across collections, billing queries, disputes, deductions, supplier portals, and credit assessment. They read incoming emails, access live account data, understand conversation context, respond where appropriate, and route to a human where necessary. This is what credit control automation looks like when it covers the complete workflow, not just the first outbound message.
How AI Agents Handle Collections Differently from Legacy Software
Every AR platform on the market sends payment reminders. The customer receives a templated email at a fixed interval. If they do not pay, another email follows on the same schedule.
This is where legacy and SaaS platforms stop. The real work of collections begins where they leave off.
When a customer replies to a reminder, someone on the AR team has to read the email, understand what is being asked, pull up the account, check the invoice history, draft a response, and send it. If the customer replies again, the cycle repeats. If the customer does not reply at all, the same templated reminder fires again on schedule, regardless of what has happened in the meantime.
AI agents handle the end-to-end collections conversation. Paraglide's Collections Agent personalises every outbound message based on the customer's payment history, outstanding balance, open queries, credit notes, and prior conversation threads. A customer who consistently pays within 45 days and has a clean history receives a different message from one who has disputed three invoices in the past quarter. This is not template selection from a library of variants. The agent generates each message based on the full context of the account.
When the customer replies, the agent reads the response, identifies what is being communicated (payment confirmation, a billing query, a dispute, a request for a payment plan), and acts on it. If the reply is a billing query, the agent resolves it. If it is a dispute, the agent captures details and routes it. If it is a promise to pay, the agent logs it and schedules the appropriate follow-up. The conversation continues naturally, using the context of every prior exchange, the way a skilled credit controller would handle it.
AI agents also work across multiple channels. A single overdue invoice may need outreach via email first, then SMS if there is no response, then voice for high-value or aged balances. The agent selects the appropriate channel based on invoice ageing, customer responsiveness, and account risk, and escalates when lower-urgency methods produce no engagement. Every interaction across every channel is logged in a single audit trail per invoice.
Collections Capability | Legacy and SaaS Platforms | Paraglide AI Agent |
|---|
First outbound reminder | Templated, scheduled | AI-personalised based on account context |
Subsequent follow-ups | Same template at fixed intervals | AI-generated based on conversation state |
Handle customer replies | Manual | Automatic: reads, triages, responds or routes |
Continue multi-turn threads | No thread awareness | Full conversation context maintained |
Adjust tone based on risk | Limited segmentation | Dynamic per customer and per invoice |
Escalate across channels | Email only (most platforms) | Email, SMS, voice based on ageing and risk |
Log all activity per invoice | Outbound only | Full lifecycle across all channels |
How AI Agents Automate the Finance Inbox and Billing Queries
The most common reason a payment is delayed is not that the customer forgot. It is that the customer has a question, a missing document, or an unresolved issue and has emailed the AR team about it. That email is sitting in a shared inbox, waiting for someone to get to it.
Billing queries are the single largest source of payment delays that AR teams can control. Customers cannot process invoices with incorrect PO numbers. They will not pay amounts they are querying. They need statements to reconcile before approving payment. Every one of these issues requires a response before cash moves.
Paraglide's Billing Support Agent operates directly in the finance inbox. It reads every incoming email, identifies the query type (invoice copy request, PO mismatch, amount discrepancy, payment status, statement request, credit note query), retrieves the relevant data from live billing systems, reads the full conversation thread for context, and responds. Standard queries are resolved end-to-end without human involvement. Complex or sensitive queries are routed to the AR team with full context assembled: the customer's question, the account history, the prior thread, and a draft response.
The difference between this and the templated auto-responses offered by some platforms (HighRadius, Esker) is fundamental. Templates pattern-match on incoming messages and fire a pre-written reply. They have no access to conversation history, cannot handle follow-up queries, and fail on any message that does not match a predefined pattern. AI agents read, understand, and respond to what the customer actually wrote.
How AI Agents Manage Disputes in Credit Control
Disputes block payment for longer than any other query type. A customer who disputes an invoice will not pay it until the dispute is resolved. In many cases, the customer will also hold payment on other invoices from the same supplier until they are satisfied the dispute process is working.
Dispute management in credit control involves receiving the dispute notification, capturing the details, cross-referencing the invoice and account data, investigating the root cause, coordinating with internal teams (sales, operations, fulfilment), and resolving or escalating the case. In most AR teams, this is handled manually in spreadsheets or email, with no structured workflow.
AI agents automate the intake and triage stages of dispute management. When a customer emails to dispute an invoice, the agent reads the email, identifies it as a dispute, extracts the relevant details (invoice number, amount, reason), cross-references the account data, and creates a structured case. For disputes that can be resolved with data (pricing discrepancies where the invoice matches the contract, delivery queries where proof of delivery exists), the agent resolves them. For disputes requiring human approval or investigation, the agent routes the case to the appropriate AR specialist with a complete brief.
The result is faster resolution cycles, fewer disputes left untracked, and AR specialists who spend their time resolving complex cases rather than logging and triaging them.
How AI Agents Process Deductions
Deductions occur when a customer pays less than the invoiced amount and provides a reason: a promotional allowance, a volume rebate, a shipping discrepancy, damaged goods, or a pricing disagreement. In industries like FMCG, wholesale distribution, and manufacturing, deductions are a daily occurrence and a significant drain on AR team capacity.
Processing a deduction requires identifying the short payment, matching it to the customer's stated reason, validating the reason against contracts, purchase orders, and delivery records, and either accepting the deduction or challenging it. In most finance operations, this is manual, slow, and often backlogged.
AI agents read the remittance advice or customer email, identify the deduction, extract the reason code and amount, cross-reference it against the relevant source documents, and route it for approval or challenge. Valid deductions (where the contract or promotion matches the deduction amount) can be approved automatically within defined thresholds. Invalid or unclear deductions are flagged for the AR team with the supporting data already assembled.
Enterprise customers increasingly require suppliers to submit invoices, track payment status, and manage billing documentation through procurement portals: Coupa, SAP Ariba, Tradeshift, SAP Fieldglass, Oracle Supplier Cloud, and others. For AR teams on the supplier side, this means logging into dozens of different portals, navigating different interfaces, uploading invoices in different formats, entering required metadata, and checking back to track status and resolve rejections.
RPA was the first attempt to automate this. It failed for a structural reason: every portal has different form fields, different file format requirements, and different rejection logic, and portals update their interfaces without notice. A rigid script that works on Monday breaks on Tuesday when a form field moves.
AI agents succeed where RPA fails because they reason about what they see rather than following a fixed script. They handle the variation across portals, manage exceptions (missing fields, format mismatches, rejections), and adapt when interfaces change. Portal activity is connected to the collections workflow, so a rejection in Coupa pauses the collections reminder for that invoice, and a successful resubmission updates the account status across the full AR cycle.
How AI Agents Assess Credit Risk
Credit risk assessment determines how much credit to extend to each customer and on what terms. It is the upstream decision that shapes the entire credit control process. Get it wrong, and the AR team spends its time chasing payments from customers who were always high-risk.
Traditional credit management relies on periodic manual reviews: pulling a credit report, checking payment history, reviewing the ageing report, and making a judgement call. This works at low volume but does not scale. In high-volume finance operations, credit reviews fall behind, limits go unreviewed, and risk accumulates.
AI agents monitor credit risk continuously. Paraglide's Credit Agent tracks payment behaviour, ageing trends, dispute frequency, and external credit signals across the customer base and flags accounts where risk is increasing before it becomes a collections problem. Credit limit recommendations, hold decisions, and escalation to the credit committee are informed by live data rather than quarterly reviews.
Why Legacy and SaaS AR Platforms Only Solve Part of Credit Control
The AR software market has developed in three generations. The first generation (Esker, founded 1985; HighRadius, founded 2006; Sidetrade, founded 2000) built large platforms for enterprise AR, focused on outbound automation, cash application, and deduction management. The second generation (Kolleno, Upflow, Chaser, Gaviti, Tesorio) built SaaS tools for mid-market companies, primarily automating payment reminder sequences. Both generations share a fundamental limitation: they automate the outbound side of credit control and leave the inbound side to the AR team.
Credit Control Function | Legacy (Esker, HighRadius, Sidetrade) | Gen 2 SaaS | AI-Native (Paraglide) |
|---|
Outbound payment reminders | ✅ Rule-based, templated | ✅ Rule-based, templated | ✅ AI-personalised |
Handle inbound replies | ❌ Manual | ❌ Manual | ✅ AI agent reads, responds, routes |
Billing query resolution | ❌ Manual | ❌ Manual | ✅ End-to-end for standard queries |
Dispute management | ⚠️ Workflow tools, manual intake | Partially Automated | ✅ Automated intake and triage |
Deduction processing | ⚠️ Available (enterprise tier) | Partial | ✅ Automated validation and routing |
Supplier portal updates | ❌ Not available | ❌ Not available | ✅ AI agent manages portal submissions |
Credit risk assessment | ⚠️ Periodic, report-based | ❌ Not available | ✅ Continuous monitoring |
Multi-channel outreach | ⚠️ Limited | ⚠️ Email-focused | ✅ Email, SMS, voice |
Conversation thread context | ❌ No thread awareness | ❌ No thread awareness | ✅ Full thread context |
The Full Credit Control Conversation, Not Just the First Reminder
Credit control is a conversation. Customers reply, query, dispute, request, and follow up. The AR team's job is to manage those conversations across hundreds or thousands of accounts, resolve what can be resolved, escalate what cannot, and keep cash moving.
For two decades, AR software automated the opening line of that conversation and nothing else. AI agents handle the rest: the replies, the follow-ups, the billing queries, the disputes, the deductions, the portal submissions, and the credit decisions that determine whether an account is worth chasing at all.
Paraglide is the only AR platform built for the full credit control conversation. Its Billing Support Agent, Collections Agent, and Credit Agent work across the complete order-to-cash cycle, handling both outbound and inbound, across email, SMS, and voice, with full conversation context and a complete audit trail for every invoice.