IDC: Top 10 Predictions for Global Retail Banking in 2025.

IDC recently released "IDC FutureScape: Global Retail Banking 2025 Forecast - Insights from China." It mentioned that GenAI provides a meaningful evolutionary path for wealth management. By 2027, given that the personalized insights and tailored recommendations of generative AI can better meet the needs of customers, the customer retention rate of personal finance advisory providers will increase by 45%. Prediction One Generative AI Wealth Assistant (GenAI Hybrid Wealth Advisory) By 2027, given that the personalized insights and tailored recommendations of generative AI can better meet the needs of customers, the customer retention rate of personal finance advisory providers will increase by 45%. GenAI provides a meaningful evolutionary path for wealth management. By applying GenAI to this field, it can effectively enhance the level of personalized customer analysis and financial planning, helping financial institutions provide highly personalized wealth management services. For example, in terms of automated investment strategies, through GenAI-generated financial plans, GenAI-driven scenarios and stress tests, and GenAI-driven market forecasts, financial institutions can offer high levels of customer service at an acceptable cost, as well as provide AI-driven financial knowledge services for clients with limited financial knowledge, attracting and retaining a wide range of customers. Prediction Two Non-AUM Platform Services By the end of 2029, due to the advanced analytics and digital engagement tools provided by AI, 25% of digital financial advisory platforms will generate more than 50% of their revenue from subscription fee models and provide value independent of asset under management (AUM) scale. The application of artificial intelligence in wealth management allows financial institutions to provide advanced personalized analysis and digital engagement tools, aiding in the transition of the industry business model towards subscription-based services and moving away from traditional fee structures based on AUM percentages. Subscription models can provide customers with predictable cost structures and are generally considered fairer than other fee structures. Additionally, highly accessible, on-demand, and digitally delivered services enable financial institutions to provide GenAI-driven wealth management services more conveniently to users, catering to differentiating needs and increasing user AUM. In China, revenue sharing for product providers is still mainstream. The subscription fee model is relatively immature, with significant room for future exploration. Prediction Three Synthetic Fraud Loan Applications By 2028, due to the availability of GenAI and personal data on the dark web, criminals will find it easier to use generative AI to create synthetic identities, leading to a 70% increase in fraudulent loan applications. With the rapid development of AI technology in China and an increasing number of loan applications moving online, fraudsters have more opportunities to exploit technology to conceal their actions and bypass traditional risk control measures, such as remote identity authentication. Criminals can use stolen personal information to create highly realistic synthetic identities, supported by forged identification documents, AI-generated videos, and other falsified evidence to engage in criminal activities, making it increasingly difficult for traditional fraud detection systems to identify them. Advanced technology and widespread data leaks create fertile ground for complex fraud schemes. Low-cost commercial-grade GenAI can greatly reduce the barriers to entry for fraudsters generating synthetic identities. Prediction Four Responsible AI Regulation By 2029, 40% of C20 banks in China will establish dedicated AI compliance functions within their governance and risk compliance (GRC) departments, adopting a comprehensive approach to address increasingly stringent responsible AI compliance. IDC defines Responsible AI (RAI) as ensuring fairness, reliability, security, privacy, inclusivity, transparency, and accountability in the design, development, and deployment of AI. RAI emphasizes the development and use of AI solutions that align with user expectations, organizational values, and societal laws and norms. Responsible AI plays a critical role in the financial services industry in ensuring privacy and security, building customer trust, and promoting fairness. Responsible AI compliance requires financial institutions to use AI responsibly in a practical, scalable, and adaptive manner. This not only pertains to how financial institutions successfully implement AI technology, but also to how they ensure compliance and fulfill their obligations to users and society. Prediction Five Payments Through Aggregator Platforms By 2027, banks' data aggregation platforms using open banking or open APIs will hold a 30% market share of online consumer-to-business payments in China. Many existing bank payment solutions use domestic interbank payment channels, resulting in low payment processing efficiency and potentially requiring a cumbersome consumer registration process. However, by combining data aggregation with the increasingly popular real-time payment systems, it is expected that making payments directly through banks (bypassing traditional third-party payment services) will become simpler, faster, and more user-friendly. The data access mechanisms of open banking offer an opportunity to streamline consumer-to-business payments. For example, it can provide real-time account verification, direct payments between accounts (bypassing traditional payment networks), and more transparent transaction records. These advantages may lead to more businesses adopting this payment method. Currently, some banks are designing applications to streamline processes, using open banking and open API platforms to enhance the user experience for consumers and clients. Especially in the digital finance landscape.In the market of wallets, making payments faster and easier.Prediction Six Rail-Agnostic Processor Platforms By 2029, 20% of new bank payment systems in China will deploy capabilities to support interbank/cross-card payment processing and cross-border payment processing. Bank payment integration platforms or payment centers have been in existence for over a decade, providing a single system to handle multiple types of interbank payments. However, card payment processing (including debit cards, credit cards, and prepaid cards) may be more fragmented, with most banks providing separate operating platforms for each type of card, gradually reducing payment processing efficiency. Some payment rail providers also recognize the importance of the ability to process payment messages for improving payment efficiency. For example, the ISO 20022 standard provides rich data elements and structures that enable payment information to be more comprehensively and accurately described and transmitted business information. It can also reduce costs, improve authorization efficiency, settlement efficiency, and enhance risk management levels. For example, in cross-border payments, using ISO 20022-based message formats can simplify payment management, increase automation, and reduce errors. Prediction Seven Intelligent Integration for CX By 2029, 30% of financial institutions will integrate GenAI capabilities in front, middle, back office systems, and operational systems to prevent revenue loss and improve customer loyalty. IDC research shows that successful implementation of GenAI in organizations requires creating a more integrated organization through activities such as data sharing and operational practices to ensure the integrity of Large Language Models (LLM) development data and establish company-wide guidelines to monitor the use of GenAI data and models. To provide advanced CX services such as personalization and predictive behavioral analysis, enterprises need to create a unified, accessible, and secure customer data platform composed of internal and external data and structured and unstructured data. Breaking down customer data silos and the ability to view this data throughout the organization is the starting point for achieving an enterprise-wide CX perspective. Meanwhile, the customer data platform will become a core element for financial institutions to fully apply GenAI. Prediction Eight Personalized Product Pricing Platforms By 2030, 40% of C100 bank institutions will modernize their product development, pricing, and management platforms to achieve new product innovation. Product creation and pricing is both an art and a science, often involving data-driven advanced analytics that help organizations respond quickly to products, maximizing customer value and institutional revenue. Companies can use data to identify market opportunities, create potential new products and services, and test the viability of new products, including product simulations, customer segmentation, and cost analysis to help companies estimate the potential of new products. Therefore, financial institutions' ability to support product pricing through real-time analysis of market data and customer behavior is essential. Dynamic pricing models will be a key competitive advantage. With more flexible pricing and optimized product portfolios, banks can more effectively capture market opportunities, respond to customer needs more quickly, and provide highly personalized product service capabilities. Prediction Nine Finance and Planning Modernization By 2029, 20% of financial institutions will embed AI/ML and GenAI capabilities in finance, ERP, and planning software to improve the efficiency of financial planning departments. With the continuous transformation of the financial services industry under market competition and geopolitical pressures, financial institutions face challenges in monitoring their financial situation and the return on investment in technology. Financial planning is often completed using older systems and incomplete data analysis, resulting in financial planning that does not align with market conditions and trends. Embedding GenAI in modern financial and ERP systems can also enhance the quality and efficiency of financial planning, significantly reduce the workforce needed for reviews, internal and external oversight, and writing audit reports. Chinese financial institutions have also actively applied generative AI in internal functions departments such as human resources/finance to improve operational efficiency and reduce operating costs. Prediction Ten RegTech Reporting Automation By 2026, 25% of Chinese banks will use RegTech embedded with GenAI to support banks' daily risk management or regulatory compliance processes to streamline workflows and increase reporting efficiency. Currently, the importance of risk management for financial institutions is increasing. Compliance with regulatory requirements has become one of the top priorities for financial institutions. The fluctuation of financial assets such as currency and stocks has increased the market risk of tradable assets. The current compliance operations and processes of many financial institutions are still manual, making it easy to make process errors. Most importantly, current financial companies also face pressure to reduce the cost of the three lines of defense, and they are actively exploring ways to solve this pain point. For example, using tools such as AI (ML) to efficiently monitor large amounts of data from logs to identify normal or typical customer behavior patterns and detect suspicious activities or money laundering risks in transaction systems, thereby continuously simplifying compliance processes and enhancing the level of regulatory compliance automation.