Potential of Machine Learning for Transforming Fintech

With a host of new companies and partnerships rising to the challenge, there is one area of innovation that holds vast potential for the fintech sector.

FREMONT, CA: Due to the explosive development of e-commerce data volumes, fintech is confronted with unprecedented hurdles in handling, analysing, and reconciling this data in a timely and efficient manner. There is one area of technology that holds tremendous potential for the fintech sector, with dozens of new startups and collaborations rising to the occasion. By 2025, the amount of data on the entire globe is predicted to reach 175 zettabytes, up from an estimated 44 zettabytes in 2020. Many UK fintech has already embraced solutions to improve accuracy, efficiency, and adaptability within data reporting and reconciliation procedures, given the sector handles vast amounts of data and has seen tremendous expansion in recent years. Simply put, these essential business operations may make or break a company's development and efficiency: inaccurate data reporting and reconciliation can cost a lot of money, waste resources, and result in a lack of regulatory compliance. Data reporting and reconciliation must not only be automated but also incorporated into as many data formats and sources as feasible, because typical Excel spreadsheets leave a lot to be desired, with minimal audit trail and a lot of possibility for human mistakes in manual operations. Given the growing amount of transaction data generated by a growing number of payment channels, devices, and touchpoints, the need for intelligent automation and enhanced reconciliation has never been greater. As per recent research from the Global Fintech Series, two-thirds of financial service organisations (66 per cent) expect solutions to automate manual processes to be one of their top investment priorities over the next three years, and 68 per cent expect to have fully automated their reconciliation within the next five years. Fintechs can improve the accuracy of their decision-making by automating these procedures as much as feasible.

Payments and fintech firms sometimes have several processors, card schemes, and issuing partnerships, making them liable for vast amounts of data emanating from a variety of sources and in various forms. However, with fast increasing data volumes and the fintech sector's growing needs and expectations for innovative ways to handle the intensive demands of gathering, analysing, and reconciling data, even automated procedures must evolve to keep up. As the year 2022 approaches and the UK fintech sector pushes for even more innovation, expansion, and investment, certain trends are expected to further disrupt data reporting and reconciliation to keep up with demand. Traditional payment providers will engage with fintech and technology providers as one of their key sources of innovation in the future, according to 86 per cent of respondents in PWC's Payments 2025 & Beyond research. The possibilities for the sector are tremendous.

In 2022 and beyond, the demand for improved business operations, driven by skyrocketing data volumes and high levels of remote working, will be a defining strategic focus for fintech firms. Machine learning will be the next level of automation innovation for fintech across the UK, based on the requirement to further optimise data reporting, handling, and reconciliation. Using machine learning in any industry, whether entirely or partially, has the primary goal of removing the need for human verification, thus enhancing accuracy and reducing the risk of human error. Machine learning has already been identified as a key business technology trend for the years 2022 and beyond: According to Analytics Insight, machine learning will generate USD 80.3 billion in income by 2023, a figure that will only rise as machine learning's use cases in the finance sector expand.

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