By Aaron Holmes, CEO, Kani Payments
As every tech firm knows, data is its most valuable asset – but worthless unless used efficiently and continually learned from. Fintechs must grapple with so many disparate seams of data in order to function smoothly and it’s not easy.
Raw data is ugly, it’s complicated and it’s scattered across many file sources, languages and formats. What’s more, big data is getting bigger and more complicated every day.
We’ve all heard of kilobytes, megabytes, gigabytes, and even terabytes. But even these terms can’t keep up with the pace of data growth. How much data is generated every day? Some estimates put it at 1.145 trillion megabytes per day. The amount of data globally in 2020 was estimated to be 44 zettabytes (one zettabyte is equal to a thousand exabytes, a billion terabytes, or a trillion gigabytes). By 2025, it’s set to reach 175 zettabytes, generated by over 55 billion devices that will be connected to the Internet-of-Things.
Trying to make sense of all this data can seem like a daunting task, especially for small fintechs or start-ups which lack the resources of their bigger competitors. According to Market Data Forecast, the global fintech market is expected to reach a value of around $324 billion by 2026, and competition will only get tougher over the next few years.
For fintech start-ups wanting to scale up, the earlier they unlock the power of their data will become a vital competitive differentiator. But growing data complexities could overwhelm them before they’ve had a chance to show the market what they can offer.
Without accurate and transparent data reporting and reconciliation, companies can face fines due to non-compliance with regulations and inaccurate audits, which could threaten the survival of the business.
Growing data complexity put more demands on fintechs
Compliance obligations require fintechs dealing with card transactions to send reports to the payment schemes like Visa and MasterCard regularly. These are huge reports that often take days or even weeks to do. Other outputs need to go to supervisory conduct authorities to show evidence of safeguards like minimum funding levels or capital requirements, with costly penalties for failure to comply, not to mention the reputational damage caused.
Fragmented, inaccessible data can create logistical and operational nightmares for a company, and trust deficits in clients when serviced ineffectively. Now more than ever before, having greater awareness and transparency in data is vital to navigate the increasingly complex legal and regulatory landscape for payments and financial services. But the rapid shift to digitalisation, especially over the last two years, has outgrown the capability of old methods to handle new data.
The tried and trusted Excel spreadsheet is arguably the most familiar and widely used business application in the world. But legacy data applications like these were built for a different time and are simply not agile nor intelligent enough to cope with the sheer scale of today’s data volumes, let alone optimize and analyze data at a niche, granular level. Without that deep drilling capability, businesses won’t get the actionable insights they need to scale successfully, and service clients effectively.
In the fintech space, third-party data comes from multiple processor relationships, card scheme relationships and bank account relationships, often spanning different countries. New services like open banking also add new complexities to data layers, forging infrastructure connections between different entities, portals and APIs. While these services allow transactions to move much faster, every new touchpoint is a new data source that needs to be parsed, analyzed and generated into accessible outputs.
Typically, the broad content of a data file may be the same, but specifications may be different, such as different field names, text files, CSV and subscript text as examples of the different variabilities in file formats and the files themselves. Raw data like this is extremely difficult for a human to read and understand.
Finding the nuances in these reams of data, and translating them into actionable insights – such as identifying where your cardholders are spending, or how many cards are being issued and used – can determine the difference between success and failure.
Old systems can’t keep pace with new data
For some emerging fintechs, they might find the systems they’re using aren’t capable of ingesting data from different sources, let alone unravelling the sheer complexity of what it’s trying to tell them. What’s more, many don’t have an established finance department in place with the specific payments knowledge required to resolve payments data queries.
Even when a company’s existing system can generate aggregated data, they may not be able to drill down into individual and underlying transactions causing disconnects. It can often take days or weeks for in-house finance teams to analyse and reconcile data, with any errors or gaps making the workload of manual investigation even heavier and more time-consuming.
Of course, companies – especially those that still have the innovative capabilities of a start-up or sizable capital – could decide to manage this by building their own back-office system to handle their payments data. But this is expensive, reliant on in-depth knowledge of payments, time-consuming, and difficult to integrate with third-party systems.
New fintechs or challenger banks already have so many hurdles to overcome just to get up and running. With such exponential growth of data volumes flowing through so many different pipelines, how can businesses extract the maximum value from their data in the most tangible and transparent way? Often, the simplest way is the quickest. By embracing automated data reporting and reconciliation, new fintech firms can use an out-the-box solution that ticks all their data management boxes.
Simplified data can help fintechs navigate the new landscape
Innovative reporting and reconciliation solutions can shoulder the burden of data management and demystify even the most complex transaction data, making it accessible and understandable. With automated, data-agnostic solutions like those from Kani, complex data reporting and reconciliation can be done in just minutes. Any errors or reconciliation breaks can be instantly identified, investigated and rectified, saving many hours of manual work.
Streamlined and speedy reconciliation assures that all payments have been settled in the right amount of money to the bank account number, or how much to remit to clients to cover settlement positions. This in turn helps to produce items like invoices much more accurately, minimising errors from data being incorrectly parsed. Businesses can leverage the full power of the data, with the assurance of being in full compliance with reporting regulations.
True data transparency comes when all of that data can normalised and presented in a clear and understandable dashboard, which can be customised or filtered to the business’s requirements. Essentially, these solutions do all the heavy lifting for businesses, which means they can spend more time focusing on building exceptional products and services for their customers.
Data never stands still, and market conditions are constantly changing. Every fintech wants to be a pioneer, and to disrupt the market. To do that, they need speed and agility – and they can only get that when they have a clear view of the data flowing into them.
When freed from the labour-intensive demands of data handling, ambitious businesses can focus their efforts on expanding. The more data you have, and the more accessible you can make it, the better decisions you can make. With full data transparency, fintechs can generate real-time insights, with accurate, reliable audit trails, giving them game-changing advantages in a fiercely competitive market.