Data Analytics: Empowering Fintech and Regtech Capabilities

In 2025, how much do you expect to see fintechs invest more heavily in data analytics capabilities to gain deeper customer insights, improve risk management and enhance operational efficiency?

Keren Ben Zvi, Chief Data Officer, PayU GPO

In 2025, fintechs are likely to increase their investments in data analytics substantially. As the competition intensifies, the need to understand customer behavior and preferences will drive fintechs to harness more advanced analytics tools. Deeper customer insights will enable better personalisation of financial services, which is crucial for retention and growth. Simultaneously, improved risk management capabilities will become essential as fintechs continue to face regulatory challenges and growing customer expectations around security. Operational efficiency will be boosted by data-driven decision-making, enabling fintechs to streamline processes and reduce costs.

Maciej Pitucha, VP of Data at Mangopay

Most likely, fintech startups might lean towards outsourcing due to resource constraints, while larger companies might prefer collecting data in-house to ensure control and align with long-term data strategy. Additionally, a combination of both approaches can sometimes be the best strategy, depending on the type of data and specific use cases.

In 2025, we can expect to see fintechs investing even more money and effort into analysing data, and several key factors will drive this trend: 

• Personalisation of services – Fintechs are using data analytics to offer more personalised financial products and services to increase user acquisition and keep user engagement. By investing in advanced analytics, fintechs will be able to create more tailored financial solutions, provide proactive recommendations, and improve the overall user experience. More developments might focus on areas like budgeting tools and investment strategies tuned to an individual’s risk profile, where the deployment of more extensive data analytics can drive innovation and customisation. When it comes to the category of data they need, fintechs will focus on customer spending patterns, financial history, including insights into the financial health and creditworthiness of their customers, and market trends.

• Risk management – Investment in AI and big data tools will be crucial for improving risk modelling and predicting market volatility. But while fintechs are putting money into detecting fraud during transactions, a big chunk of their investment in data will likely be used to improve the KYC processes. Why? Because there is a race for user acquisition among fintechs, and KYC screens are one of the key steps to making a good impression. To make sure only trustworthy individuals or businesses are allowed in, fintech companies need a strong fraud prevention system. But it’s not just about blocking fraud. Fintechs also want to make the process of checking new users as smooth as possible. So, they need as much data as possible around the user to create different paths for the KYC checks. If a user seems trustworthy, they are taken through an easier process. And only the users who seem risky or need more checks are sent through the more detailed processes.

• Embedded finance and open banking – With the continued development of embedded finance and open banking, fintechs will have access to richer datasets from multiple sources, including banks, payment providers, and e-commerce platforms. Thanks to the rise of AI, fintechs can better blend financial products right into their services. AI helps sort through all the data that comes up from all the available sources with options that exactly fit what the customer needs. 

Nicolas Miachon, Product Director, Head of Marketing for Banks at SBS

Banks and fintechs have no shortage of data but historically haven’t had the systems and processes to leverage this data effectively. In the coming year, we’ll see them investing heavily in data analytics to turn this around. As this happens, data analytics will go from a cumbersome, manual process to a highly efficient business practice that drives new operational efficiencies organisation-wide.

With modern data warehouses in place, financial organisations will be able to take a more structured approach to deriving customer insights from their data—in a fraction of the time that has previously been required to do so. This is one area where we’ll continue to see some of the biggest investments in data analytics, as fintechs continue looking to new digital and AI-powered tools that will help them transform these customer insights into new products and services. 

In risk management, fintechs have been leveraging AI and machine learning-powered data analytics for some time now across anti-money laundering (AML), fraud detection, credit risk reporting and more. We expect to see these investments continue over the next year, but more so through upgrades to current data analytics systems, rather than investments in entirely new systems.

Jamie Hutton, Co-founder and CTO at Quantexa

The perennial issue for financial institutions (FIs) is creating a unified and integrated view of their data across business units, locations and systems. Hundreds of billions of dollars a year are being invested in all affected areas from financial crime compliance and risk analysis to customer service.

Yet many FIs still have too many manual processes; data silos; ever increasing and compounding data and an inability to effectively integrate that data to make intelligent decisions. Overcoming these issues and creating a unified view of an organisation’s data is called Decision Intelligence. For the full power of artificial and generative intelligence applications to be unlocked, it’s essential that all an organisation’s data be unified and made available. A strong data foundation is the critical enabler for all digital transformation projects.