How banks use AI to improve customer experiences
With most banking interactions now taking place in-app, the need for sophisticated, hyperpersonalized experiences is rapidly growing. Customers expect timely advice, proactive support, and intelligent fraud prevention, seamlessly integrated into their everyday journeys.
This is where AI in banking is playing a transformative role. By analyzing vast volumes of structured and unstructured data, AI in banking and finance enables institutions to move beyond reactive service models and deliver real-time, contextually tailored experiences for individual needs. Increasingly, agentic AI and conversational AI in banking are enhancing this further, acting as an always-on relationship manager that adapts dynamically and supports personalized interactions at scale. Supported by next-generation core banking platforms that centralize data and unlock real-time insights, banks can deliver more intelligent engagement models that strengthen relationships while driving efficiency, revenue, and loyalty.
What personalization means in the age of AI
Personalized banking leverages real-time data insights to tailor financial services, products, and communications to customers’ specific goals - going beyond simple name addressing or broad segmentation.
Generative AI enables banks to analyze vast volumes of customer data while simultaneously generating relevant, contextual interactions at scale. These capabilities empower banks to deliver intuitive, proactive experiences that anticipate customer needs.
The overall business impact is significant. According to EY research, 61% of banking respondents have already reported substantial impacts from their generative AI deployments.1 This is no surprise considering the latest reports from the Global Skill Development Council reveal that generative AI in banking & finance had a market size of around USD 1.6 billion last year, and is projected to grow up to USD 26.34 billion by 2035.2
Instead of offering generic lending products, for instance, a bank can present a tailored loan option precisely when a customer shows signs of needing financing, such as increased spending on home improvements.
Similarly, advanced fraud detection using AI can trigger proactive alerts when unusual account activity suggests a customer may need support, enhancing both service and trust. This is especially important given the increase in fraudulent activity as a result of greater AI integration. In fact, Fortinet reports over 121.99 billion exploitation attempts, specifically driven by greater availability and automation.3 The overall result is a shift from transactional banking to relationship-driven engagement.
The business case for AI-driven customer experiences
As digital-first experiences redefine expectations across industries, financial institutions (FIs) must evolve to remain relevant. AI in banking is now a strategic catalyst for revenue growth and maintaining a competitive edge. The economic potential of AI-driven engagement is significant. A 2024 Citibank study found that 93% of FIs expect AI to bolster profits over the next five years, with Citi forecasting a 9% uplift in global banking profits, equivalent to $170 billion, by 2028. By orchestrating the right product at the right moment, banks can increase adoption, optimize cross-sell opportunities, and unlock new revenue streams.
The competitive bar continues to rise as digital-native banks and fintechs set new standards for intuitive, seamless engagement. Institutions that fail to adapt risk losing market share to more agile players capable of delivering highly relevant interactions. Beyond financial performance, personalization is central to customer satisfaction. Tailored experiences build trust, deepen engagement, and support long-term loyalty. Conversely, banks that do not embrace AI-driven personalization risk declining engagement and ineffective outreach, as customers increasingly expect frictionless journeys.
Practical ways banks can use AI today
Banks can achieve hyperpersonalized banking through a pragmatic, low-complexity execution strategy. They can start by focusing on high-impact use cases that deliver measurable value quickly. A pragmatic approach begins with identifying areas where personalization can drive immediate outcomes, such as lending, customer onboarding, or financial wellness insights. By embedding AI capabilities directly into existing systems, banks avoid the risks and costs of a wholesale transformation.
Next-generation core banking systems play a key role by centralizing data and enabling banks to draw real-time insights. These insights, combined with proactive partnership and automated assistance, allow AI to enhance the customer experience in meaningful ways, such as delivering timely product recommendations, anticipating customer needs, or guiding financial decisions. Modern banking platforms support this approach through modular and scalable architectures, allowing banks to introduce AI incrementally and integrate new capabilities without disrupting core operations.
Banks can accelerate adoption while managing complexity and risk by taking a phased and strategic approach. Key considerations for successful implementation include:
- Prioritizing high-impact use cases to demonstrate early ROI as well as flagging potential pain points before adoption.
- Integrating AI into existing banking ecosystems for seamless delivery, particularly by adopting a symbiotic model.
- Leveraging modular, cloud-native architecture to support growth and flexibility without operational downtime or interruption to customers.
- Ensuring robust data quality, governance, and security frameworks are implemented and updated throughout the process.
The benefits of AI-powered customer experiences
The impact of AI-driven personalization extends across both business performance and customer outcomes, highlighting the significant benefits of AI in banking. For banks, these benefits are tangible and measurable; according to Deloitte’s 2025 analysis of AI ‘pioneers’, 74% of leading banks are already seeing an ROI of over 10% on their most advanced generative AI initiatives. Personalized recommendations drive higher conversion rates, increasing revenue through more effective cross-selling and upselling. Automation of customer interactions and decision-making processes further reduces operational costs while freeing up resources for higher-value activities.
For customers, the experience becomes more intuitive, relevant, and supportive. Financial products and advice better align with individual needs, improving satisfaction and fostering long-term loyalty. This shift is substantiated by industry pioneers who cite strengthened client relationships as a primary driver of their AI success, moving beyond simple efficiency to deep engagement. Ultimately, AI-powered customer experiences enable banks to move from product-centric models to truly customer-centric strategies.
As banks seek to scale AI-driven personalization, core technology plays a pivotal role. Our solution, Finastra Essence, helps FIs turn data into actionable insights and deliver richer customer experiences. By integrating with existing core systems, it breaks down silos and provides a unified view of each customer. This increased visibility enables banks to identify needs, tailor products and services, and deliver personalized interactions across channels. With tools for campaign management, performance tracking, and predictive insights into behavior and risk, banks can move from reactive service to proactive, insight-led engagement.
The result is a more intuitive, connected, and customer-centric baking experience that drives growth and enables personalization at scale. Contact us for more information.
Sources:
1EY (September 2025). How AI in banking can result in major transformative benefits. Available at: https://www.ey.com/en_us/insights/banking-capital-markets/ai-in-banking-ey-parthenon-genai-survey-insights
2Global Skill Development Council. Generative AI in Finance and Banking: Market Growth & Trends. Available at: https://www.gsdcouncil.org/blogs/generative-ai-in-finance-and-banking-market-growth-trends
3Fortinet. 2026 Fortinet Global Threat Landscape Report. Available at: https://www.fortinet.com/resources/reports/threat-landscape-report?utm_source=Paid-Search&utm_medium=Google&utm_campaign=AI-DrivenSecOps-GLOBAL-Global&utm_content=AR-SOC-TLR-G&utm_term=fortinet%20fortigate&lsci=701Hr000002S3wXIAS&meeting_offer__c=&UID=ft
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