In 2025, to what extent do you think fintechs will invest more in data analytics capabilities to gain deeper customer insights, improve risk management and increase operational efficiency?
Keren Ben Zvi, Chief Data Officer, PayU GPO
In 2025, fintechs will likely significantly increase their investments in data analytics. As competition intensifies, the need to understand customer behavior and preferences will drive fintechs to leverage more advanced analytics tools. Deeper customer knowledge will enable better personalization of financial services, which is crucial for retention and growth. At the same time, improving risk management capabilities will become essential as fintechs continue to face regulatory challenges and growing customer expectations for security. Operational efficiency will be enhanced 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 turn to outsourcing due to resource constraints, while larger companies might prefer to collect 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 data analytics, and several key factors will fuel this trend:
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Personalization of services – Fintechs use data analytics to offer more personalized financial products and services to increase user acquisition and maintain user engagement. By investing in advanced analytics, fintechs will be able to create more personalized financial solutions, provide proactive recommendations, and improve the overall user experience. Further developments could focus on areas such as budgeting tools and investment strategies tailored to an individual’s risk profile, where deploying deeper data analytics can drive innovation and personalization . When it comes to the category of data they need, fintechs will focus on customer spending habits, financial history, including information on the financial health and creditworthiness of their customers, as well as trends of the market.
- Risk management – Investment in AI and Big Data tools will be crucial to improve risk modeling and predict market volatility. But even if fintechs invest in fraud detection during transactions, much of their investment in data will likely be used to improve KYC processes. For what? 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 ensure that only trustworthy people or businesses are allowed in, fintech companies need a robust fraud prevention system. But it’s not just about blocking fraud. Fintechs also want to make the verification process for new users as smooth as possible. So they need as much data as possible around the user to create different paths for KYC checks. If a user seems trustworthy, they are guided to a simpler process. And only users who appear at risk or need additional controls are sent to the more detailed processes.
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Embedded finance and open banking – With the continued development of embedded finance and open banking, fintechs will have access to richer data sets from multiple sources, including banks, payment providers and e-commerce platforms. Thanks to the rise of AI, fintechs can better integrate financial products directly into their services. AI helps sort all data from all available sources with options that exactly match the customer’s needs.
Nicolas Miachon, Product Director, Bank Marketing Manager at SBS
Banks and fintechs have no shortage of data, but have historically lacked the systems and processes necessary to leverage that data effectively. In the coming year, we will see them invest heavily in data analytics to turn the tide. As this happens, data analysis will move from a tedious manual process to a highly effective business practice that will drive new operational efficiencies across the organization.
With the implementation of modern data warehouses, financial organizations will be able to take a more structured approach to extracting customer insights from their data, in a fraction of the time it previously took. This is an area where we will continue to see some of the largest investments in data analytics, as fintechs continue to seek new digital and AI-based tools that will help them turn these customer insights into new products and services.
In the risk management space, fintechs have been leveraging AI and machine learning-based data analytics for anti-money laundering (AML), fraud detection, credit risk reporting and much more. We expect these investments to continue over the next year, but more through upgrades to current data analytics systems, rather than investments in entirely new systems.
Jamie Hutton, co-founder and CTO at Quantexa
The recurring problem for financial institutions (FIs) is creating a unified, integrated view of their data across business units, locations and systems. Hundreds of billions of dollars are invested each year in everything from financial crime compliance and risk analysis to customer service.
Yet many financial institutions still have too many manual processes; data silos; ever-increasing and complex data and an inability to effectively integrate this data to make intelligent decisions. Overcoming these issues and creating a unified view of an organization’s data is called Decision Intelligence. To harness the full power of artificial and generative intelligence applications, it is essential that all of an organization’s data is unified and made available. A solid database is THE an essential enabler for all digital transformation projects.
