Technology
The real-time payments rush highlights the need for data standardisation
Global Banking and Finance Review
Company
By Aaron Holmes, founder and CEO of global data reconciliation pioneer
By Aaron Holmes, founder and CEO of global data reconciliation pioneer Kani Payments
Speak to any payments or fintech business about their immediate priorities and you’ll find that most, if not all, point to digital transformation, rising regulatory scrutiny and the constant hunt for actionable business intelligence insights. But data quality – which is critical to meet all these needs and more – is getting worse. Without clean, standardised data, payments firms are left fumbling in the dark, unable to find the insights they need to improve and grow. Even worse, they risk falling foul of regulators, breaching compliance obligations and being put out of business altogether.
As the worldwide shift towards real-time payments gains momentum, with 38 countries having launched faster payment systems and more in the pipeline, payment firms urgently need to improve the cleanliness and quality of their data. While we can all agree instant payments are fantastic for consumers and businesses, skyrocketing transaction volumes mean that the reconciliation processes designed for batch-scheduled payments are struggling to adjust to increasing real-time payment flows. And that means data quality is weakening by the day.
Reporting, reconciliation and compliance are mission-critical processes for payment companies, as they need to ensure timely and accurate scheme settlement, interchange fee and foreign exchange calculations, show evidence of customer fund safeguarding and account segregation, efficient exception management, merchant settlement and chargeback/dispute management. But all too often, businesses are relying on legacy tech or Excel spreadsheets that simply can’t keep up with the evolving mix of payment methods, rails and data formats. Even as new data messaging formats like ISO 20022 are rolled out to build something like a universal data language, far too many payments businesses are relying on data files and formats that can’t speak to each other and are difficult to interpret manually. That spells trouble when it comes to accurate reconciling and reporting – and could seriously harm future growth prospects too.
With fintech funding dropping dramatically, it’s getting harder for start-ups to raise the funds they need to expand. Increasingly, investors are looking for proof of a clear path to profitability. Scaling businesses are also facing fiercer competition. Being able to mine data for actionable growth insights requires speed and accuracy, which is why it’s so important to get data quality right as soon as possible.
Confusing transaction terminology can harm performance
Payment companies receive raw transaction data from their payment processors, amounting to vast datasets which they then must use to match reconciliations and generate reports. But with many companies dealing with multiple processors, what often happens is that one payment processor might send clients a CSV file, another might send an XML type file, another might send a text file. Often many data fields are incomplete, which leads to inaccurate or delayed reconciliation and reporting.
Errors or reconciliation breaks will often require in-house finance teams to perform manual investigations, hampering productivity. Many in-house finance teams simply don’t have the time or depth of knowledge needed to clean up their data, fill in missing fields and meta data in their output files, rectify anomalies and match reconciliations. And identifying and resolving reconciliation anomalies will become even harder as transaction volumes and sources grow.
Compounding the problem is that payment transactions are often cloaked with confusing terminology, leading to frequent misunderstandings with different labels referring to the same process or data field. It’s a problem that affects even long-established and experienced financial institutions. For example, after speaking to many payment businesses, it’s surprising to me how many mistake clearing and settlement as being the same thing when they’re not.
Even when data standards exist, they are not always universally followed, and companies can become overwhelmed by the sheer size complexity of their datasets. Too many processors and data reconciliation providers are built on old legacy tech, reliant on rigid on-premises infrastructure and can’t standardise data in the way their clients deserve.
Automated reporting and reconciliation is a business game-changer
A payment company’s success depends on having strong financial controls and the right tools to ensure timely and accurate reporting and reconciliations, especially as regulators ramp up scrutiny. For example, recently introduced rules on customer fund safeguarding require companies to prove that segregated funds are not mixed with funds belonging to other parties. If a business can’t ensure funds are being held where they’re supposed to be and not co-mingled with other accounts, regulators will come down hard on them.
While some companies attempt to mitigate these challenges by building their own platforms to handle reporting and reconciliation, all too often DIY platform builds go way over budget and schedule, causing the business even more financial pressure and poor performance. And this doesn’t take into account the updates, on-going regulatory changes and payments-data knowledge needed to maintain an internal system.
A platform built by experts inherently familiar with the nuances of transaction data can help companies save vast amounts of time and costs, simplify complex and fragmented data and standardise it into a common language that can be understood by everyone.
Kani Payments’ platform helps BIN sponsors, challenger banks, acquirers and other fintechs by ingesting their vast, complex datasets to complete weeks of transaction reporting and reconciliation work in under 30 seconds, with full transaction audit history trails. And, due to Kani Payments’ auto-data feature that parses and normalises data automatically when it is ingested, ensuring that it is in a common view for accurate reporting and reconciliation, the platform provides users with complete assurance of standardised data.
Automated reports can be configured to existing formats or bespoke requirements, can be scheduled or produced ad-hoc, and any errors or exceptions are instantly identified with alerts sent to relevant teams for manual investigation if needed. Another game-changing benefit is that these platforms can be automatically updated when any payment scheme or regulatory changes occur, ensuring firms stay in full compliance.
Ultimately, as real-time payment volumes surge, data standardisation is the key to maximising the value companies can extract from their data, ensuring fintechs and payment firms can get to the actionable insights they need to grow and strengthen their bottom line.
Data standardisation is the process of ensuring that data is consistent and uniform across different systems and formats, which is crucial for accurate reporting and reconciliation in financial services.
Compliance refers to the adherence to laws, regulations, and guidelines that govern financial institutions, ensuring they operate within legal frameworks and maintain ethical standards.
Automated reporting is the use of technology to generate reports without manual intervention, improving accuracy and efficiency in financial data management.
Reconciliation is the process of comparing two sets of records to ensure they are in agreement, often used to verify transactions and account balances in financial operations.
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