By Dion Appel, CEO & Managing Director at Opypro
Organisations with a large and growing number of trade accounts are facing an exponential rise in the volume and complexity of data. Despite the varied sources and formats of information in the payment allocation and reconciliation process, there is still an expectation that accounts receivable (AR) teams will deliver faster settlement cycles, often while relying on outdated manual methods.
Navigating spreadsheets and documents across multiple systems introduces significant delays for AR teams. Time spent manually piecing together data incurs high costs for organisations, a challenge compounded when bulk payments are involved. Human error, inconsistent processes, and time-lagged reporting further impact efficiency, compliance, and growth. PwC reports that finance teams spend around 30% of their time on manual reconciliation. Even in top-quartile companies, analysts are dedicating around 40% of their time gathering data rather than analysing it (PwC, 2024).
This piece explores the impact of manual reconciliation in trade account environments and outlines how automation, artificial intelligence (AI), and machine learning (ML) can enhance accuracy and speed to drive scalable growth.
The impact of manual reconciliation
Manual reconciliation demands substantial effort from AR teams to match transactions and other financial documents, such as invoices and remittance advice, with bank statements and payment data. For some enterprise-level organisations, this commonly involves thousands of reconciliations across multiple business units and brands. AR teams spend considerable time gathering data, chasing mismatches and manually resolving breaks. The process can be time-consuming and prone to errors, and these inefficiencies can have a direct impact on team morale.
A McKinsey report estimates that over 25% of teams experience recurring errors due to manual reconciliation (KlearStack, 2025). Even minor line-item mismatches can trigger major cash flow disruptions and unnecessary disputes, while financial metrics begin to suffer. High Days Sales Outstanding (DSO) and increased aged and bad debt rates are multiplied by the number of trade accounts and data sources. Unallocated payments are often left unresolved, as working through details and exceptions is both time and cost-prohibitive. Siloed teams and disconnected systems impact the accuracy and timeliness of financial reports. Time lags caused by manual consolidation limit visibility into real-time data, which organisations rely on to support strategic assessments, actionable insights and cash flow management decisions.
How automation transforms the payment allocation process
Technology solutions enable organisations to centralise all data sources into a single place, either via an API-driven approach or a non-integrated method using secure data transfer files.
Manual processes and tasks are transformed into automated workflows that seamlessly operate the payment allocation process with high accuracy and speed, while boosting compliance.
Leveraging artificial intelligence (AI) and machine learning (ML) capabilities, high volumes of data can be analysed in real time. Algorithms work to identify payments automatically, extract data from documents and accurately match them to outstanding invoices and remittance advice, accelerating payment processing and improving cash flow management.
Key steps involved in automating the payment allocation process:
- Centralise all data sources An automated solution connects and consolidates key information, including: - Bank feeds or payment processors: for access to payment data
- Transaction data: to identify purchase information
- Invoice documents: to match payment data automatically
- Remittance documents: to support automated payment allocation
- Account information: to determine the source of each payment
- Automate payment matching and allocation Leveraging a centralised data source, automated workflows seamlessly match payment data to invoices, remittance documents, and even trade account bank details. Thousands of transactions are processed in minutes, improving processing time and accuracy. Pilot benchmarks have shown up to 97% accuracy (Open Ledger, 2025). Standardised methods are applied consistently, reducing errors and improving compliance.
For organisations that require a non-integrated solution, extracted data files can be returned to core systems, such as the ERP, to maintain up-to-date information. 3. Identify and action unallocated payments With automation handling the majority of payment allocations, having a tool that automatically flags any unallocated payments can be highly valuable. Some platforms offer a portal, providing team members a way to action tasks and reconcile payments directly within the system. 4. Access reports in real-time Centralised, accurate reports are easily accessed by AR teams in real-time for prompt analysis and decision-making. Comprehensive financial reports provide a full view of the entire trade account portfolio, down to granular insights by business units, brands and at an account level. This visibility empowers data-driven decisioning at every tier of the organisation.
Choosing the right solution
Selecting a technology solution that integrates all data streams into a single platform, allows automation to operate seamlessly and efficiently handles growing data volumes is essential to deliver growth. Flexibility is key: the solution must be adaptable to evolving trade data requirements and support sustainable, scalable operations as your business grows.
Automated reconciliation is the new standard for efficiency
Automation transforms how AR teams operate from day one. Teams can work smarter and faster, unlocking immediate benefits as soon as the automated reconciliation system goes live:
Faster cash flow: accurate payment matching accelerates settlement cycles.
Reduced errors: minimal human intervention lowers mistakes and time spent handling discrepancies.
Time savings: automation significantly reduces team workload, freeing up valuable time for growth activities.
Centralised visibility: multiple data sources are streamlined into a single, accessible view.
Improved compliance: standardised workflows ensure consistency and adherence to policies.
Real-time decisioning: accurate, up-to-date reports empower timely decisions.
Scalable growth: organisations can grow efficiently without a proportional headcount increase.
Enhanced team morale: automating repetitive tasks enables team members to focus on more meaningful, higher-value activities.
Conclusion
Manual reconciliation may still work, but it’s no longer sustainable. Rising data volumes, stricter compliance requirements and growing operational demands make the case for automation undeniable. With AI and machine learning-powered automation, organisations can achieve a highly efficient, fast and accurate reconciliation process that empowers teams and supports a broader growth agenda.
Dion Appel is CEO & Managing Director at Opypro__, a cloud-based credit management platform that simplifies and automates accounts receivable processes. With end-to-end automation, Opypro streamlines operations, reduces costs and drives growth.
Opypro standalone modules offer the flexibility to start with the Payment Reconciliation module and scale to the full platform over time, building a stronger, more connected accounts receivable operation that scales.
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