Achieving financial growth through payments data analytics

by Julia Morozova, solution architect, DataArt

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The critical role of data analytics as a cornerstone in the evolving payments landscape cannot be overstated. The volume of electronic transactions is not just growing; it’s tripling the growth rate of overall payments revenue over five years, as shown in the 2023 McKinsey Global Payments Report. Thanks to ongoing technological advancements, this significant shift means less reliance on cash and a stronger focus on immediate payment methods. Over the last year, we’ve seen an increase in payment activities both within countries and internationally.

Accumulating data from these transactions becomes a crucial strategic asset, giving businesses a competitive edge. Leaders in the field, like Stripe and Adyen, are proving the value of utilising data analytics by offering enhanced services beyond processing payments. Open banking is also transforming the financial landscape by enabling partnerships between banks and fintech companies, using payment data to improve tools for budgeting, investing, and financial products like loans and insurance.

Yet, modernisation is key for businesses to truly benefit from these advancements. It has the potential to cut operational costs by about 30%, reduce the time it takes to bring new products to market, and open doors to new opportunities. Innovation, which includes embracing artificial intelligence (AI), leveraging big data, introducing new services, and moving to cloud-based systems, may require managing strategic commitments and forming smart partnerships to stay ahead.

Addressing data analytics challenges in payments

For businesses awash in data, establishing a strategic plan is vital to leveraging this wealth of information. An effective data strategy aligns with an organisation’s broader business objectives, streamlining data handling to enhance decision-making processes and secure a market edge.

Despite understanding the value of data analytics, the payments industry encounters significant obstacles. Data is often scattered across different locations and systems, complicating creating a cohesive strategy. The challenge is to improve the quality of this data and overcome issues related to its distribution and centralised management. Some organisations are making headway with approaches like modern data management, which help them organise and manage data more effectively.

Predictive analytics, which uses machine learning and statistics to forecast trends, faces integration issues. Financial companies must carefully blend vast amounts of payment and marketing data to enhance risk management and informed decision-making. Techniques such as predictive modelling are critical in anticipating financial and operational risks, but their implementation can be complex and challenging for many.

Reporting, essential for transparency, also poses its set of challenges. Financial institutions need clear reports on key metrics, such as revenue from fees, the number of chargebacks, and overall transaction volumes. They require analytics solutions that can provide both compliance support and strategic insights. AI advancements have led to developing tools that can generate reports more intuitively, yet adopting these technologies is challenging. They demand investment, training, and adjustment to existing systems.

Finally, AI and machine learning are transforming labour-intensive compliance tasks, aiming for high accuracy and cost savings. However, setting up these systems for tasks such as monitoring communications and analysing contracts for legal and fraud risks is a complex challenge that the industry strives to address. Despite these hurdles, the sector recognises the significant advantages of embracing these technologies.

Navigating security and fraud prevention issues

A strong security strategy is crucial to protect against fraud and keep operations running smoothly. However, it’s concerning that, according to OWASP Top 10 2021, a staggering 94% of global applications have issues with access control. This is just the beginning; there are widespread problems with encryption, code injections, insecure software designs, and incorrect system setups. Staying ahead of these issues requires improved DevOps workflows, better use of cloud technology, and updated security practices. These are particularly vital for those handling financial transactions and payment data.

Thanks to technological advancements, we’ve moved from spotting fraud after it happens to try to stop it before it occurs. A deeper understanding of how users behave and their backgrounds and activity is needed to spot and stop threats quickly. AI and machine learning are making it easier to identify fraud patterns, monitor account activity, and intervene accurately and rapidly, reducing risks without getting in the way of genuine users.

Handling fraud well means constantly comparing your practices to the industry norm. Looking closely at fraud cases—when they happened, what kind, how they were carried out, and which accounts were involved—gives businesses valuable information to prevent and manage fraud more proactively. 

Even though regulations are constantly changing and the community is there to help, processing payments is complex and requires detailed knowledge of various areas, including compliance with laws, data sovereignty, disaster recovery, business continuity, security, networking, and how data is structured. The scattered nature of data can lead to quality and integrity problems. Separate data sets make it hard for systems to work together, and inflexible systems can make adding new features or data sources slow.


For financial companies, analysing data is more than just helpful—it’s a crucial part of staying ahead in the market because of the valuable insights it provides for creating extra services. There are a lot of opportunities out there, with AI and machine learning leading to a better grasp of customer behaviours, spotting trends, and improving how we report information. As fraud attempts become more complex, solving these issues faster is essential to protect money, keep profits safe, and reduce overall risks. The challenges are significant, but the benefits of updating our technology are even bigger. These new developments can become strategic advantages with the right skills and tools.

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