Future of Global Fintech Second Edition 2025

Specific applications of AI To further understand how AI is being used within application areas, the survey asked respondents about their specific application interests for each area where they reported current or planned adoption interests (Figure 27). While there were variations, responses revealed that priorities spanned nearly all individual applications or use cases studied. Respondents were also asked how they implemented their applications of AI – e.g. built in-house, through partnerships with banks or outsourced. Customer service use cases were a major focus, with fintechs reporting that they prioritized enhanced communication channels (64%) and real-time services (39%). In-house development dominated these areas (56% and 62%, respectively), though outsourcing also played a key role, covering 43% of communication channels and 37% of real-time services. Within the process reengineering and automation application area, automation of administrative tasks was the leading use case with a 46% implementation rate, followed by automated reporting at 44% and chatbots and virtual assistants at 35%. Compliance automation remained lower at 22%, with higher outsourcing rates (particularly in the APAC, LAC and MENA regions, where over 30% of these tasks were handled externally).In risk management, AI was primarily used in fraud detection, with 46% of fintechs reporting that they were implementing it for this purpose. Notably, 37% outsourced this function, relying on external expertise for specialized capabilities. Preventative pattern analysis saw even higher outsourcing rates (65% in digital payments and 30% in digital capital raising). Regional differences were notable. Europe, for instance, favoured in-house development, with 76% of preventive pattern analysis managed internally. Similarly, while AI-enabled conduct risk management in digital payments and digital capital raising was outsourced by 61% and 33% of fintechs globally, Europe stood out with 84% in-house development. For AI-driven generation of a new revenue stream, fintechs primarily developed decision-making and data analytics solutions in-house. Yet, the outsourcing of decision-making solutions was more common in LAC (27%) and SSA (32%). Wealthtech and digital capital raising also leaned on external providers, outsourcing over 40% of decision-making solutions. Digital capital raising relied on external expertise for 47% of its data analytics solutions. In the customer acquisition domain, AI helped fintechs expand their market presence and service offerings. About 35% of fintechs reported using AI-enabled market services, while 39% employed AI for add-on services. Notably, 79% of these add-on services were developed in-house. Digital account opening solutions, adopted by 25% of fintechs, also reflected this trend, with 71% of fintechs developing internally and only 23% outsourcing. Specific applications of AI overall FIGURE 27 AI-enabled customer communication channels Customer service Process reengineering and automation AI-enabled risk management Generation of new revenue Customer acquisition64% AI-enabled real-time service adjustments to clients' needs 39% Personalized risk exposure analysis 16% Automated compliance 22% Automated reporting 44% Automation of administrative tasks and processes 46% Chatbot and virtual assistant to streamline work 35% AI-enabled conduct risk management 32% Fraud detection 46% Preventive pattern analysis to find potential exploits 31% AI-enabled data analytics 41% AI-enabled informed decision-making 41% AI-enabled access to add-on services/products 39% AI-enabled marketing 35% Digital account opening solutions 25% In the customer acquisition domain, AI helped fintechs expand their market presence and service offerings. The Future of Global Fintech: From Rapid Expansion to Sustainable Growth 39