What Do You Forecast for Fintech Investments in Data Analytics by 2025?
Keren Ben Zvi, Chief Data Officer at PayU GPO
By 2025, it’s expected that fintech companies will significantly ramp up their investments in data analytics. As competition heats up, the demand for understanding customer behaviors and preferences will push these companies to leverage advanced analytics tools. Gaining deeper insights into customers will allow for more tailored financial services, which is essential for maintaining customer loyalty and achieving growth. Furthermore, enhanced risk management capabilities will be increasingly vital as fintechs navigate regulatory hurdles and rising customer expectations concerning security. Data-driven decision-making will also enhance operational efficiency, helping companies streamline their processes and lower expenses.
Maciej Pitucha, VP of Data at Mangopay
It’s likely that fintech startups will lean towards outsourcing their data analytics due to limited resources, whereas larger firms may choose to gather data internally to maintain control and align with long-term data strategies. Sometimes, a hybrid approach that combines both outsourcing and internal collection can be the most effective strategy, depending on specific data types and requirements.
In 2025, we anticipate significant financial commitments from fintechs in data analysis, driven by several key factors:
-
Service Personalization – Fintechs are increasingly adopting data analytics to deliver personalized financial products and services to enhance user acquisition and maintain engagement. Investments in advanced analytics will empower fintechs to craft more customized solutions, make proactive recommendations, and enrich the overall user experience. Innovations may focus on features like budgeting tools and investment strategies tailored to individual risk profiles. Fintechs will prioritize gathering data on customer spending habits, financial histories, as well as insights into customers’ financial health and creditworthiness, along with market trends.
-
Risk Management – Investing in AI and big data technologies will be vital for refining risk models and anticipating market fluctuations. While fintechs are directing investment towards fraud detection during transactions, a significant portion will likely focus on improving Know Your Customer (KYC) processes. With a competitive landscape for user acquisition, optimizing KYC systems becomes crucial for making a favorable initial impression. To ensure that only trustworthy individuals and businesses are onboarded, fintech companies must establish robust fraud prevention systems. However, it’s not solely about preventing fraud; fintechs also aim to simplify the user verification process. Access to comprehensive data will allow companies to create streamlined KYC pathways, enabling trustworthy users to navigate an easier verification process while flagging those needing additional scrutiny.
-
Embedded Finance and Open Banking – As embedded finance and open banking continue to evolve, fintechs will have the advantage of enriched datasets from multiple sources such as banks, payment processing platforms, and e-commerce businesses. The rise of AI will further enable fintechs to seamlessly integrate these financial products into their existing services. AI aids in filtering through the plethora of data available, ensuring that the solutions provided align perfectly with customer needs.
Nicolas Miachon, Product Director and Head of Marketing for Banks at SBS
Financial institutions and fintechs have ample data but have historically struggled to utilize it effectively due to lack of suitable systems and processes. In the upcoming year, a significant investment in data analytics is anticipated for turning this around. As a result, data analytics will evolve from a cumbersome, manual endeavor to a streamlined business practice, boosting operational efficiencies across the organization.
With modern data warehouses being established, financial institutions will adopt a more structured methodology for extracting customer insights from their datasets, significantly cutting down the time required for this process. We expect substantial investments to continue in data analytics, particularly as fintechs explore new digital tools and AI-driven solutions to convert customer insights into innovative products and services.
In the realm of risk management, fintechs have harnessed AI and machine learning to enhance data analytics for anti-money laundering (AML), fraud detection, credit risk assessment, and other applications. We foresee ongoing investments in these areas over the coming year, primarily focused on upgrading existing data analytic systems rather than acquiring entirely new technologies.
Jamie Hutton, Co-founder and CTO at Quantexa
A persistent challenge for financial institutions is constructing a unified and integrated understanding of their data across various business units, locations, and systems. Each year, hundreds of billions of dollars are funneled into addressing issues ranging from financial crime compliance and risk assessment to customer service enhancement.
Nonetheless, many financial institutions still face obstacles such as reliance on manual processes, data silos, and an overwhelming influx of data that can’t be effectively integrated for informed decision-making. Resolving these challenges and achieving a cohesive view of organizational data is termed Decision Intelligence. Unlocking the full potential of artificial and generative intelligence applications necessitates a comprehensive unification and availability of organizational data. A robust data foundation is key to successful digital transformation initiatives.
