Behind The Scenes: How Machine Learning Is Driving Fintech Data Reporting Beyond Old Boundaries

From retail to banking, the current process of rapid digitalisation is also having a big impact on the fintech and payments companies who support and process the skyrocketing data volumes it produces. One innovative solution is being developed to help fintech and payments companies around the world do just that: machine learning, writes AARON HOLMES.

By the end of 2021, over 2.14 billion people globally bought goods and services online. Other services are also increasingly taking place in the virtual world, with two-thirds of financial transactions now made online. Despite the uncertainty caused by the global pandemic, it’s now clear that this rapid digitalisation is good news for the economy, merchants and the fintechs who support them.

However, there is one element of rapid digitalisation that remains challenging for many fintechs: the skyrocketing data volumes that drive this trend, and the changing ways to handle, analyse and reconcile this data. As a sector that handles large amounts of data and has seen rapid growth in recent years, many UK fintechs have already adopted solutions to tackle the 175 zettabytes of global data that is expected to exist by 2025.

Fintech and payments companies around the world are finding that these new demands on their traditional data reporting and reconciliation processes are leading to a need for increased accuracy, efficiency and adaptability. With traditional Excel spreadsheets leaving much to be desired, and considerable room for human error in manual processes, data reporting and reconciliation not only needs to be automated; it also needs to be integrated with as many data formats and sources as possible. Given that transaction data is springing up from an ever-increasing array of payment channels, devices and touchpoints, the quest for intelligent automation and enhanced reconciliation has never been more urgent.

According to recent research by the Global Fintech Series, two-thirds (66%) of financial service organisations expect solutions that automate manual processes to be one of their top investment focuses over the next three years, whilst 68 per cent plan to have fully automated their reconciliations within the next five years. By automating these processes as much as possible, fintechs can accelerate their decision-making with much greater accuracy, and streamline their operations to be leaner and stronger.

The current challenges facing fintechs in data reporting

Payments and fintech companies often have several processor relationships, card scheme relationships and issuing and acquiring relationships, leaving them in receipt of large amounts of data originating from multiple third parties and in different formats. But with rapidly escalating data volumes, and the increasing needs and expectations of fintechs demanding new ways to shoulder the intensive demands of collating, analysing and reconciling data, even automated processes need to advance to keep up with the pace of change.

As the UK fintech sector strives for further innovation, expansion and investment, certain trends are set to disrupt data reporting and reconciliation even further to match demand. With 86% of respondents in PWC’s Payments 2025 & Beyond report agreeing that traditional payments providers will collaborate with fintechs and technology providers as one of their main sources of innovation in the future, the possibilities (and expectations) are huge for companies spanning the entire payment ecosystem.

With a clear need for new services and partnerships to support the complex demands of the ever-changing fintech sector, a new raft of companies is stepping in with solutions to match – the fintechs for fintechs. Kani Payments is one such company: after years of experiencing these problems first-hand, whilst working in other fintechs, we launched a reconciliation and reporting SaaS platform specifically designed to reduce complexity for financial services businesses. Whether it be other ambitious fintechs, challenger banks, acquirers or payments companies, the conditions are ideal for companies like ours to partner with others and enable them to scale faster.

New possibilities for intelligent data reconciliation

The need for improved business operations, driven by soaring data volumes, and high levels of remote working, will be a defining strategic priority for fintech businesses now and over the next few years. Building on the need to further optimise data reporting, handling and reconciliation, the next level of automation innovation for fintechs is building products and features using machine learning.

Either completely or in part, utilising machine learning within any sector has the primary objective of eliminating a need for human processing, thus increasing accuracy and removing room for manual error. In fact, machine learning has already been pegged as a major business technology trend for 2022 and beyond: Analytics Insight estimates machine learning to reach US$80.3 billion in revenue by the year 2023, a figure that’s only going to grow massively as machine learning expands in usage cases within the fintech sector.

Even before the pandemic, payments businesses struggled to manage complex data reconciliations which involved time-consuming manual processes. Now that the pandemic-driven shift to digital payments worldwide has led fintechs everywhere to scramble for more clarity from their data, it’s innovations such as machine learning that can help them keep up with demand.

For the data reconciliation process, machine learning can help businesses to make increasingly accurate decisions at lightning speed, allowing more space for informing business strategies, directing new service developments with quicker go-to-market times, and helping to meet stringent regulatory reporting and audit trail requirements.

Innovation from the UK’s alternative fintech hub in the North East

Having already reconciled over €10 billion(US$10.7billion) in processed payments volume to-date with our automated platform, Kani Payments is committed to supporting and accelerating even greater innovation in data reporting and reconciliation, with new geographies on our roster and a suite of services designed to take fintechs to the next level.

Recognising that accurate and verifiable reconciliation and reporting of payments data is vital for payments and fintech companies to mine valuable business insights, and to scale up to meet customer demand, Kani Payments has recently invested in new AI and machine learning functionality.

Currently unique in the fintech market, our investment was driven by a partnership project with Newcastle University’s Mathematics Department and the National Innovation Centre for Data, which explored how to solidify and build on our machine learning record-matching solutions. Having yielded positive results, we are excited to see how our work can continue to help UK fintechs thrive in 2022 and beyond.

Named as an emerging fintech hub in the 2021 Kalifa Review of UK Fintech, Newcastle is fast becoming one of the most exciting and appealing locations for dynamic financial services and fintech players, a place Kani Payments is proud to call home. Our investment and research into machine learning for the fintech data reconciliation process will not only help solidify Newcastle and the North East as a thriving data science and fintech hub, but will also empower fintechs themselves to be global tech pioneers in a fast-moving sector.

• Holmes is CEO and Founder of Kani Payments.