Generative AI Reshapes Banking: Hyper-Personalization & Efficiency Gains

The banking sector in 2025 faces immense pressure, with institutions ranging from global titans to regional players navigating a complex landscape of outdated processes, escalating cyber threats, and ever-increasing customer demands for personalized experiences. These daily challenges often threaten the very survival of many financial entities. It is precisely into this environment that generative artificial intelligence (GenAI) is making a profound impact, not only streamlining manual workflows and delivering hyper-personalization at scale but also fortifying defenses against cyber threats, facilitating adaptation to regulatory changes, and rebuilding customer trust. According to McKinsey, GenAI could inject an impressive $200 billion to $340 billion annually into the banking industry, potentially boosting profitability by as much as 15%.

This transformative shift is not a distant prospect; it is already unfolding. Forward-thinking banks such as Boost, Tyme, and UNO Digital Bank are leveraging generative AI to redefine banking, offering services that are faster and more tailored than those of traditional banks, which often struggle under the weight of legacy systems and slow-moving regulations.

Generative AI, powered by sophisticated machine learning and large language models, is fundamentally reshaping how banks approach automation, decision-making, and customer engagement. Historically, many banking processes, from onboarding new clients to detecting fraud, were labor-intensive, fragmented, and notoriously slow. GenAI is rapidly changing this paradigm, enabling institutions to complete tasks in minutes that once required days or even weeks. Its applications are broad and expanding, from powering more human-like conversations through advanced chatbots and analyzing vast datasets for market forecasting to tailoring financial products to individual customer profiles. As D.K. Sharma, President & COO at Kore.ai, notes, GenAI is already streamlining previously manual tasks like managing FAQs, detecting fraud, and facilitating onboarding by harnessing the speed, consistency, and scale of large language models that traditional methods simply cannot match.

The practical applications of generative AI are diverse and impactful. Many traditional banks grapple with the significant challenge of legacy systems—cumbersome infrastructures that are costly and time-consuming to update, hindering innovation. Generative AI offers a powerful solution, allowing established institutions to inject agility, efficiency, and innovation into their operations without the need for a complete system overhaul. Boost, operating across Southeast Asia, exemplifies this by seamlessly integrating generative AI into customer interactions via platforms like WhatsApp, creating a frictionless experience for everything from onboarding to loan applications. Karthik Bhaskaran, Boost’s CTO, highlights how GenAI allows them to scale customer support and service efficiently, unburdened by legacy technological constraints.

Beyond operational agility, GenAI excels at delivering personalization at scale, which has evolved from a desirable feature to a non-negotiable customer expectation. Digital-first banks are leveraging AI to hyper-personalize services, proactively shaping offerings around individual habits and needs. Tyme Bank, for instance, analyzes customer behavior in real-time to customize loan offers based on spending patterns, ensuring every interaction is timely and relevant. UNO Digital Bank takes this further by employing AI-driven underwriting, using alternative data sources like device information and bank statements to extend credit to individuals traditionally excluded from conventional banking systems. Kalidas Ghose, UNO’s chairman, emphasizes that generative AI allows them to move beyond narrow credit scoring models, fostering more inclusive and personalized services.

Generative AI also ushers in a new era of operational excellence through enhanced efficiency and automation. Digital-first banks are not merely refining old processes; they are reinventing them for an AI-first world, automating repetitive, time-consuming tasks to free up human resources for higher-value services while maintaining lean cost structures. Tyme Bank, for example, automates the handling of subpoenas and legal requests—a once tedious process—enabling employees to focus on strategic tasks and customer service. Similarly, UNO Digital Bank utilizes predictive analytics to optimize credit risk assessments and decision-making, allowing real-time lending decisions and significantly accelerating their entire operation.

In customer acquisition, AI-driven strategies provide digital-first banks with a significant edge. They employ AI-powered marketing to conduct sophisticated, real-time A/B tests and optimize customer engagement with unprecedented agility. Tyme Bank uses AI-driven marketing campaigns to understand customer preferences and tailor messages, refining their approach in real-time. Boost leverages conversational AI on platforms like WhatsApp to guide potential customers through sign-up processes and answer queries without human intervention, leading to a lower cost per acquisition and rapid expansion without the overheads associated with traditional banks. These approaches shift the focus from merely pushing products to building enduring customer relationships.

The future of banking is intrinsically linked to the transformative power of generative AI, which is poised to become the backbone of financial services. Its ability to analyze vast data, generate actionable insights, automate complex tasks, offer tailored financial advice, and detect fraudulent activities with precision is setting new standards for operational excellence and customer engagement. Banks are already exploring innovative domains such as decentralized finance and predictive analytics, and this momentum is set to increase. For traditional banks, embracing generative AI is no longer optional; those that fail to integrate and leverage it risk falling behind in a rapidly evolving financial landscape. McKinsey reports that banks implementing AI for Know Your Customer (KYC) agentic workflows can realize productivity improvements ranging from 200% to 2,000%, as a single human can supervise over 20 AI agent workers.

However, generative AI is not a panacea and comes with its own set of challenges and risks. The handling of sensitive customer data is a primary concern, necessitating strict data privacy and security measures, anonymization where required, and compliance with regulations like GDPR or CCPA. Ensuring transparency and fairness in AI-driven processes remains an ongoing challenge, requiring robust governance frameworks and explainability measures to audit and understand AI decisions. A significant limitation is the potential for “AI hallucinations,” where models produce inaccurate or misleading outputs due to incomplete or flawed training data, posing a real risk in critical financial decisions like credit assessments or fraud detection. Consequently, maintaining high-quality, up-to-date data is paramount. The current best practice positions generative AI as a powerful assistant rather than the ultimate decision-maker; critical decisions, especially those affecting customer finances, should remain under human oversight, with AI performing the heavy lifting of data analysis and process automation.

The banking revolution is a clear glimpse into the future of finance. For traditional banks, it serves as a call to embrace agility, innovation, and customer-centric strategies powered by AI. By strategically investing in AI-driven solutions, banks can modernize operations, enhance customer experiences, and gain a competitive edge. As D.K. Sharma aptly states, “The financial institutions that will thrive aren’t the ones that lean entirely on automation or stubbornly resist it. They’ll be the ones that blend both—harnessing AI for speed and scale, while doubling down on the uniquely human elements that build relationships.” The AI era is not just arriving; it is already here, and those banks that embrace generative AI today will lead the financial landscape tomorrow.