Payment Reconciliation is the process of comparing bank statements against your accounting to make sure the amounts match each other. This is important because it ensures that businesses are able to identify any unusual transactions caused by fraud or accounting errors.
Small firms with clients and cash flows from fewer sources and banks may find reconciliation to be quite simple. However, as a company grows and its cash inflows and outflows become more diverse, this process becomes more difficult and time-consuming, increasing the likelihood of error exponentially. Furthermore, as a result of the various methods for collecting money and the disparities in how payments and associated remittance details are submitted, reconciliations have become increasingly complex. Missing or incorrect reference numbers, invoices in different languages and currencies, and a variety of other factors can all make payment confirmation difficult, resulting in unnecessary delays in posting and using a company’s incoming funds.
This is where automation can help to free up human resources. Artificial intelligence (AI) and machine learning (ML) have the potential to significantly reduce manual interventions and increase straight-through reconciliation rates, thereby reducing the risk of human error. Having innovative reporting tools gives businesses complete visibility of their payment data, allowing them to make informed, strategic decisions while ensuring that they are informed by accurate data.
Payment reconciliation – what is entailed and why is it necessary?
By discovering errors, discrepancies, or fraud, reconciliation and reporting ensure that odd transactions and behaviours are documented. It’s a crucial process that keeps organisations safe, compliant, and in control of their finances and cash flows.
After a thorough reconciliation, these records can be used for future financial planning. Excessive purchases or expenditures can be cut, and transactions requiring significantly higher transaction fees can be checked to eliminate any unnecessary charges. This process can be categorised in three essential steps prior to finalisation: data extraction, matching, and reconciliation.
- Data Extraction – The process of retrieving bank statements and document data is the first stage of performing payment reconciliation. Because bank statements and text documents can take many different forms, manually collecting and extracting data can be extremely error-prone. As a result of this, businesses can adopt solutions like Optical Character Recognition (OCR), which is essentially the process of converting printed documents into machine-encoded texts. At this stage, any visible errors can be noted for further investigation during later stages.
- Matching – Transactions that are clearly matched are deleted from the reconciling database; machine learning is then used to fine-tune the automation process. ML can recognise familiar actions and patterns from previous manual interventions using pre-programmed rules and algorithms. Based on these discoveries, the system can resolve future unmatched items on its own, resulting in a significant reduction in the number of unmatched items reported.
- Reconciliation – This is the main stage of reconciling item investigation. Any mistakes will be reported to companies or individuals for correction, which will be followed by reviews and approvals. Because this particular process cannot be solved using automated methods, this stage is extremely labour-intensive. As a result, optimising the previous two stages is critical to ensuring that the least amount of labour and time is required during this phase.
Adopting AI for automated reconciliation
Reconciliation is an imperative process. For growing businesses particularly, there can be an added pressure to ensure its funds are properly reported and fraudulent activities are found at early stages. For companies looking for higher levels of performance within their own reporting and reconciliation processes, now is the time to ensure that innovation is at its core.
Businesses should partner with a payment provider that is constantly enhancing their AI and data insights capabilities. The right payment service provider will alleviate the tedious process of extracting and analysing data, by investing into new products and features that enable and automate these capabilities. PayU for example, offers a fully flexible and customisable payment processing engine which also includes reporting as a functionality. Solutions such as this one are enabling businesses to visualise available data to uncover hidden insights with instant access to interactive dashboards and detailed reports.
By adopting these solutions, a business’ financial health can be easily evaluated and red flags can be discovered faster. This in turn, will enhance security levels, improve efficiency and decrease overall risks.
