Invoice-level payment matching that cuts reconciliation time by 67% — automated posting, not month-end marathons.
Match payments to invoices at the line-item level using AI-powered matching, apply entity-specific posting rules, and route exceptions to a prioritized queue — from 40+ hours of manual reconciliation to under 4 hours monthly.
The Problem This Solves
Before PaymentHub
Manual reconciliation consumes 40+ hours monthly. Partial payments, overpayments, and multi-source payments create cascading matching errors that compound through the month. Month-end close extends by days while AR staff manually match payment batches to invoice records. Multiple payment sources — card processor statements, ACH files, wire notifications, check deposits — each require separate reconciliation workflows.
With PaymentHub
Payments match to invoices automatically at the line-item level. Posting rules apply per entity without manual intervention. Exceptions route to a prioritized queue with AI-suggested resolution. Monthly reconciliation drops from 40+ hours to under 4 hours. Month-end close accelerates by days. Finance teams see real-time cash position without waiting for batch reconciliation.
How It Works
AI-Powered Invoice-Level Matching
The matching engine uses amount, reference number, customer identifier, date, and historical payment patterns to match incoming payments to specific invoices. For straightforward matches (exact amount to single open invoice), matching is automatic and immediate. For complex scenarios — partial payments, overpayments, payments against multiple invoices, or payments without clear reference data — the AI engine analyzes the customer's payment history, typical payment patterns, and open invoice profile to propose the most likely match. Match confidence scores determine whether the payment auto-posts or routes to the exception queue for human review. Average auto-match rates exceed 87% for organizations with standard B2B payment patterns.
Entity-Specific GL Posting
Posting rules are configured per entity using PaymentHub's upgrade-safe hook architecture. Each entity can define distinct GL account mappings, posting batch timing, fee allocation rules, and journal entry formats. When a payment is matched and ready to post, the posting engine applies the entity-specific rules to generate the correct GL entries — cash account debit, AR account credit, fee expense entries, and any entity-specific adjustments. Posting can operate in real-time mode (entries post immediately upon match) or batch mode (entries queue and post on a scheduled cadence). Multi-entity operations with intercompany payment scenarios are supported with configurable intercompany transfer posting.
Exception Queue with AI-Suggested Resolution
Payments that cannot be auto-matched or that fail posting validation route to a prioritized exception queue. Each exception includes the reason for escalation, the AI's best-guess match with confidence score, suggested resolution actions, and links to the relevant customer account and invoice records. Exceptions are prioritized by amount, age, and customer tier. The queue tracks resolution time and provides metrics on exception volume by type, enabling teams to identify and fix the root causes of recurring exceptions. Common exception types include: partial payment, overpayment, duplicate payment, unidentified customer, and posting rule conflict.
Multi-Source Payment Consolidation
Card processor settlements, ACH files, wire notifications, and check deposits are all ingested into a single reconciliation workflow. Each payment source has a configurable import adapter that normalizes the data into PaymentHub's standard payment format. The reconciliation engine processes all sources through the same matching and posting pipeline, eliminating the need for source-specific reconciliation workflows. Consolidated reporting shows payment volume, match rates, and exception metrics across all sources — giving finance teams a unified cash position view rather than source-by-source reporting.
Reconciliation Health Monitoring
Continuous monitoring tracks reconciliation accuracy, match rates, exception aging, and posting integrity. Dashboard alerts notify AR management when match rates drop below configured thresholds, when exception queues grow beyond capacity, or when posting discrepancies are detected. Monthly reconciliation health reports summarize performance trends and identify areas for improvement. The monitoring system detects drift — gradual degradation in match rates that might indicate ERP data quality issues, new payment patterns, or configuration changes needed.
Architecture & Integration Notes
The reconciliation engine operates as an event-driven pipeline: payment events arrive from gateway settlements, ACH files, or manual entry; the matching engine evaluates each payment against open invoices; matched payments route to the posting engine; and exceptions route to the queue. Before/after hooks on both the matching and posting steps allow injection of custom logic — for example, a pre-matching hook that applies customer-specific remittance parsing rules, or a pre-posting hook that validates GL account availability before entry creation. The entire pipeline logs every decision (match attempts, confidence scores, posting entries, exception reasons) for audit and troubleshooting.
AI Copilot for Automated Reconciliation
The AI copilot proposes initial GL mapping configurations from your chart of accounts structure and posting patterns. It generates test scenarios covering every edge case — partial payments, overpayments, credit memo applications, multi-entity cross-payments, and surcharge splits — to validate reconciliation accuracy before go-live. In production, the copilot monitors reconciliation health, detects drift in match rates, suggests corrective actions for recurring exception types, and recommends configuration adjustments to improve auto-match rates. For new payment sources or ERP changes, the copilot generates updated mapping rules and validates them against historical transactions.
AI Copilot — Available on Growth & Enterprise Plans
AI Copilot reduces implementation time for automated reconciliation by automatically generating field mappings, test datasets, and validation scripts based on your ERP schema — so your team can ship faster without writing repetitive configuration code.
Ready to see Automated Reconciliation in action?
Book a Payments Blueprint call and get a live walkthrough tailored to your ERP and payment requirements.
Before & After PaymentHub
| Area | Before | After PaymentHub |
|---|---|---|
| Area 1 | 40+ hours of manual matching and posting | Under 4 hours — 67% reduction through automated matching |
| Area 2 | Manual lookup by amount, date, and customer with frequent errors | AI-powered matching with 87%+ auto-match rate and confidence scoring |
| Area 3 | Close extended 3-5 days for payment reconciliation | Continuous reconciliation — no month-end backlog |
| Area 4 | Separate reconciliation per payment source with spreadsheet consolidation | Unified reconciliation view across all payment sources in real time |
Related Features
AR Customer Portal
Give your customers a branded payment portal with real-time invoice balances, multi-method payment acceptance, autopay enrollment, and payment history — every transaction posts automatically to your general ledger with invoice-level matching.
Gateway Routing Engine
PaymentHub's gateway orchestration routes payments to any processor based on configurable business rules, supports automatic failover, and lets you add or swap gateways without disrupting ERP posting workflows.
Level 2/3 Data Optimization
PaymentHub automatically extracts tax amounts, customer codes, and line-item detail from ERP invoices and submits them with every B2B card transaction — qualifying for the lowest interchange rates without manual data entry.
Frequently Asked Questions — Automated Reconciliation
Get your tailored implementation plan.
Our Payments Blueprint call delivers a written implementation roadmap specific to your ERP, your team, and your timeline.