Forrester’s annual report on emerging technologies in banking highlights three key areas: low-code/no-code development platforms, natural language processing, and the metaverse and Web3. Despite tighter budgets, the report emphasizes that banks should prioritize transforming their core systems for long-term competitiveness. Low-code/no-code platforms can deliver a positive ROI within 12 months, but banks need to establish governance structures to avoid potential downsides. Natural language processing has potential applications in compliance and customer service, and Forrester expects increasing investments in this area. However, the firm does not recommend investing in the metaverse and Web3 at this time, as their potential payoffs for banks remain uncertain.
Traditional banks face an increasing fragmentation of banking relationships, with customers turning to digital-only banks and other financial service providers. Customer loyalty in banking continues to decline, with only 29% of customers claiming loyalty to their primary bank. To combat silent attrition, financial institutions need to monitor customer activity, improve services, and use data analytics and machine learning to identify and retain customers at risk of leaving. Personalization, frictionless experiences, and efficient digital account opening processes are essential for customer retention. Banks and credit unions must address challenges in delivering campaigns and communications at digital speed to reduce fragmentation and maintain customer relationships.
The global digital banking market is expected to reach $30bn by 2026, with digital-only banks continuing to gain traction. These challenger banks offer more personalization, attractive pricing, and real-time notifications, making them increasingly appealing to customers. Traditional banks have the advantage of secure digital channels, unparalleled customer service support, and inherent trust. However, they struggle with providing a streamlined digital experience due to complex back-office legacy systems. To compete with digital banks, traditional banks must strategically analyze customer-impacting applications and identify opportunities to simplify operations and infrastructure. This will allow them to offer the hyper-personalized services that younger customers expect.
Banks have a unique opportunity to lead in sustainable finance, with $130 trillion earmarked for climate change and sustainable development initiatives. But many banks are slow to respond, focusing on regulatory and compliance aspects. Banks risk missing out on financial opportunities, tarnishing their reputations, and losing ground to competitors in the green economy. To capitalize on this opportunity, banks must urgently prioritize sustainability, overhaul incentives, build genuine green products, and educate their workforce on the importance of sustainability. By acting now, banks can contribute to a greener economy and a better future for all.
Payment systems play a critical role in the digital transformation journey. A payment hub consolidates different payment types into a single platform, simplifying the customer experience and enabling financial institutions to manage holistic payment risk. It also lowers the total cost of ownership by reducing maintenance, support, and operational resource requirements. Payment hubs are important as they allow financial institutions to adapt to the growing adoption of instant payments and enhance their fight against fraud. By implementing a payment hub, banks can modernize their payment systems, improve efficiencies, and potentially create new revenue streams.
AutoGPT is a semi-autonomous, open-source application built on top of ChatGPT. Users give AutoGPT a goal, and it creates a task list and executes tasks until the goal is completed. It can complete up to five goals in a single ask, connect to various internet-based applications, and process tasks linearly or in parallel. AutoGPT is also database-enabled and self-learning. The technology has the potential to transform banking in various aspects, including marketing, sales, customer service, finance, operations, and strategy. Banks should consider investing in these generative AI and large language models, as they are likely to significantly impact the banking industry.
