Introduction
Payment Reconciliation is the process of comparing bank statements against your accounting to make sure the amounts match each other. For small firms where their clients and cash flows are from fewer sources and banks, reconciliation may be fairly straightforward.
However, as a company scales, and cash inflows and outflows become more diverse, this process will soon become daunting and labor-intensive, exponentially increasing the probability of error. Therefore, numerous automated methods were proposed to alleviate the process from human efforts.
This article introduces the step-by-step process of payment reconciliation, a brief history as well as the importance of checking across payments, challenges, and how deep learning-based approaches could help automate and accelerate parts of the process efficiently.
Types of Reconciliation
Reconciliation needs can vary from individuals (personal requirements) to large corporations. Let’s look at two main types of reconciliation: Personal and Business.
Personal Reconciliation
Personal reconciliation is fairly straightforward — it is the process of comparing personal account statements with that of receipts. By doing so, it allows us to investigate and discover whenever a fraud occurs. In addition, such methods would also ensure that the financial transactions of an individual did not contain any errors.
Although personal reconciliation is a fairly simple task that is being performed daily without our notice, the process can indeed be expedited with automated methods. Recent technologies have developed mobile applications that allow users to scan in receipts and automatically perform data extraction and recordings on the products and corresponding prices. Along with credit card and banking apps that offer pop up notifications of money being withdrawn or spent, one can easily perform top-level reconciliations in a blink of an eye.
Business Reconciliation
Business reconciliation, on the other hand, is a much bigger and more complex task. Companies must consult their multiple accounts across each sector against all the purchase records to prevent any unintended cash flows. The scale of such transfers are much bigger and may go through multiple accounts making the process more risky and difficult to organize. Therefore, the reconciliation process is much more difficult to handle and requires multiple steps before finally the results are out (as described in later sections). These reconciliations are usually performed either monthly or quarterly and are often assisted by other third parties such as accounting firms.
In addition to making sure that the revenues and costs are currently recorded within the company, business reconciliation is also very important for taxes. Taxes of companies are based on several important factors such as assets and sales which would only be accurate if reconciliations were performed properly to ensure the correctness of the taxes imposed.
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The Need for Payment Reconciliation
Payment reconciliation is crucial for any company, be it large or small, as it is an opportunity to perform any analysis to gather insights and detect any fraudulent activity. Such a process has to be repeated multiple times a year usually monthly or once a quarter. The following section discusses some of the important applications for Payment Reconciliation.
Cashflow Visibility and Compliance
Corporations and companies are required to provide clear and visible cash flows to their investors as required by the financial regulators and accounting standards to avoid any fraudulent activities. These financial reports have to be balanced in terms of the assets and liabilities and therefore reconciliation is required to ensure that all details are correct.
Business and Financial Planning
After a proper reconciliation process, these records can also be used for further financial planning. Purchases or costs that are too big can be reduced and transactions that require much higher transaction fees may be investigated to reduce any unnecessary charges.
The Process of Payment Reconciliation
Pipeline
The figure above shows a pipeline of the reconciliation process. This process can be segmented into 4 main sub-modules: document data extraction, matching, reconciliation, and finalization.
- Document Data Extraction — This is the first stage of performing payment reconciliation which is the process of retrieval of bank statements and document data. As different bank statements and text documents may be in various forms, whether printed or as PDFs, manually performing data collection and extraction may be highly prone to errors. Therefore, it would be necessary to adopt Optical Character Recognition (OCR) approaches for extraction and insertions into databases for reconciliation (OCR is essentially the process of converting handwritten/printed texts into machine-encoded texts. For more information please see: nnt.ai/cloud-vision). This is also where a rough check of totals across bank statements take place. Any noticeable discrepancies could be noted here for further investigation in the later stages.
- Matching — The second stage of reconciliation is the matching process, which is essentially the process of comparing statements and diagrams at a transaction level. For transactions that are clearly matched, they are deleted from the reconciling database. Matching is typically done through excel VBA programs if datasets are fairly simple and straightforward but machine learning and AI methods may come in handy when datasets are related in a complex manner. Any mismatches will then be sent into the reconciliation stage.
- Reconciliation — This is the main stage of investigating reconciling items. Any errors will be reported to companies or individuals for fixing followed by reviews and approvals. This stage is very labor-intensive as the errors are unsolved by automated approaches. Therefore, it is crucial to optimize the previous 2 stages to ensure that the least amount of work is required during this phase.
- Finalization — After all the work is checked and errors are fixed, we can then update the checking lists and entries accordingly. This stage is fairly straightforward and thus can be done purely via computer-aided programs for Enterprise Resource Planning (ERP) / Robotic Process Automation (RPA) (Further addressed in later sections).
As the entire reconciliation process involves multiple phases and tasks, selecting and incorporating multiple programs and automated approaches is important to ensure the process runs smoothly.
Documents Involved
For large companies, numerous documents are involved to perform reconciliation. Bank statements, receipts, invoices, as well as all billing paperwork are required for detailed cross-check to bookkeep the record.
Internal and External Reconciliation
When an error occurs, it is also important for the company to figure out whether the fault is internal or of the banks’. It may be a result of a breach if the error took place externally, which happens rarely but still possible.
A Short History — The Traditional Approach
Nowadays, payment reconciliation is usually done through accounting software and deep learning assisted tools. However, this wasn’t always the case. Before the widespread use of computers, companies still had to perform cross-checks and bookkeeping. This section describes the traditional approach as well as the potential risks involved in each stage with reference to the aforementioned pipeline.
Use of Papers/Manual Entries
Before the digital era, all receipts and bank statements were in handwritten texts with no universal formats, not to mention there was no OCR available at all. Therefore, the document data extraction process could only be done with pen and paper, slowly listing out transactions of each process. Instead of online databases, the records had to be kept in a large number of books that constantly require an update when new entries come in.
Manual entries are extremely costly especially when a company’s size is large. It would also be important to ensure that transactions were recorded in a similar manner between each recorder to ease up the difficulty of comparison between different records.
Complex Logic
When a company is small, with single or limited sources of income, comparing data is fairly easy. However, when high volume of transactions take place in a many-to-many relationship with other data, along with the limited time given to workers, it may be possible for workers to write off or simplify omit some of the transactions.
The stakes are high as more and more transactions are dealt carelessly whether intentionally or unintentionally, as the small write-offs could build up and lead to more significant errors.
Manual Checks and Communication
Despite the difficulty in manual entries, the most challenging aspect of reconciliation in the traditional approach is the manual checks, especially when errors are observed.
During the matching and reconciliation stages, it would be necessary to compare against transaction records from multiple documents which causes huge problems when an error is spotted. Since transactions are everywhere with no actual database to keep track of, any error would have to be checked through communication between different functions of the company that record different parts of the cashflow.
Communication causes a set of risks on top of the originally mistake ridden records further creating difficulties in the reconciling process. All the efforts put into cross-checking becomes another great yet inevitable expenditure to companies.
Difficult to add insights
Finally, when all the efforts are spent on extracting, comparing, and communicating, it would be challenging to focus on areas that are more risky or to analyze trends. This limits the time these workers can actually spend on investigating parts of the transactions that could be simplified or improved as all the time is used on repetitive procedures like manual comparison.
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An Emerging Trend — Workflow Automation
Recent reconciliation processes have already incorporated semi-automated procedures such as ERP, accounting software, RPA, and the universalization of cloud computing. The following sections describe each of the programs and how they facilitate the process of automating reconciliation.
ERP
ERP or Enterprise Resource Planning software plans and manages all the core processes of an organization including the supply chain, manufacturing, and financials. They could be used for individual activity planning. Each application within the ERP may be regarded as a service and the entire system combined allows operations to be communicated and noted efficiently much simpler than traditional manual entries and verbal communication.
Accounting Software
Accounting software are set of application software that help process a given transaction through traditional accounting modules such as account receivables, account payables, inventory, etc. One of the most notable software in this category is Quickbooks, developed by Intuit. The software allows small or medium sized firms to process transactions, both remotely and locally, and further perform reconciliation by looking at the transaction records, modules, and comparing it with bank accounts to ensure the right amount of inflow and outflow took place.
RPA
Robotic Process Automation (RPA), on the other hand, is the set of software programs that utilize machine learning to mimic workers. Instead of creating a set of processes/programs to use, RPA learns from human behaviors in using the GUIs in order to replicate the same set of tasks. This ultimately reduces human effort in repeatedly typing in entries and further minimizes errors that are caused by potential fatigue.
Cloud Computing
Cloud computing and storage are services that are remotely kept together. Similar to traditional methods of bookkeeping that require all the books to be kept at one place for access each time, cloud computing allows all the data to be kept on the ‘cloud’ available at any time. As the database is identical for access at different places, it is easier to perform any checks in between and make the entire process less error-prone. Cloud computing ultimately makes all the transactions created via RPA and ERP easier with tracking.
Nevertheless, the former processes still serve only partial tasks throughout reconciliation even with the utilization of cloud computing. The accuracy of deep learning has recently created more potential segments that could be automated.
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Deep Learning and OCR — Fully Automated Data Extraction
The two processes mentioned in the previous sections are constrained to repeated tasks (RPA), or post-facto operations after all entries are entered (ERP). Therefore, the process of detecting data from various formats and documents still remains an open area to be replaced by software — this is where OCR takes the main role.
Bank statements and receipts are often given in printed even handwritten formats. Even when we can or take photographs of them to upload into our drives, they are merely colored pixels with no real contextual meanings to machines. The process of OCR is thus applicable to convert them into structured data so that we could perform the following:
- Statistical analysis of numerical data, pointing out which number represents which aspects of the transaction, which could be performed based on the semantic meanings of titles within each block through natural language processing (NLP).
- Checking handwritten texts or other texts that are at a higher risk of error and perform anomaly detection via deep learning or traditional machine learning approaches. Report any part that may be worth a review. If the accuracy is fairly high, companies can quickly spot mistakes at an early stage without having to look through all the details.
Potential OCR Services
A number of companies provide highly accurate OCR services. Notable companies such as Google, Microsoft, and Amazon all provide APIs for OCR and other vision tasks. However, their OCRs are more focused on providing the highest accuracy across images-in-the-wild and are less specialized in terms of document reading and data interpretations from documents with numerical data. Let’s look at some alternatives of OCR services from other companies that are specialized for different aspects:
- ABBYY — ABBYY FineReader PDF is an OCR developed by ABBYY. The software has friendly UIs used for PDF reading. However, with its non-engineering nature, it would be more difficult to incorporate it into other programs to make an entirely automated reconciliation process.
- Kofax — Similar to ABBYY, Kofax is a friendly PDF reader. The price is fixed for individual usage, with discounts for large corporations. 24/7 assistance is also available in case of any technical difficulties.
- Nanonets — Nanonets OCR is specialized in particular types of documents such as receipts, statements or invoices. As their deep learning models have specific targets, they perform extremely well in specialized tasks, which comes in handy for the reconciliation process. APIs are available for any further tuning and combination with other automated software throughout the entire pipeline making it one of the more viable options.
Things to Keep in Mind
With a grasp of the concepts regarding payment reconciliation, there are still processes in the middle to keep in mind when designing a fully automated pipeline for payment reconciliation.
Best Practices
One must consider the best practices both in terms of efficiency and ethical standards. For efficiency, we have to make sure that all individual parts of the payment reconciliation set up provide accuracies that outperform traditional approaches while bringing the cost down or at least it’s kept at similar levels as that of the traditional approach.
Ethically, one must also ensure the development of the reconciliation to be fair and equal and not be biased or allow illegal profiteering by any of the stakeholders. Checks across various departments must be conducted before fully deploying the system.
The Future — Seamless, Frictionless, and A Breeze with Tech
With improvements from both hardware and software standpoint, the future of payment reconciliation is very optimistic. It will not be surprising to one day have a payment reconciliation pipeline that merely requires humans to take a photo of the statement at his/her hand and let programs and AI perform the rest.
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Source: https://nanonets.com/blog/payment-reconciliation/
