How AI is elevating marketing in financial services

Insights

• AI in financial services has evolved from simple tools like chatbots to more complex systems enhancing customer experience and retention.
• Generative AI tools are fueling AI expansion in financial services, with widespread adoption of agentic AI expected by 2027.
• AI enables financial services companies to deploy personalized marketing strategies, resulting in increased customer engagement and satisfaction.
• Virtual financial assistants like Bank of America’s Erica and improvements in AI chatbots are significantly enhancing customer support and satisfaction across large financial institutions.
• Agentic AI employs adaptive, autonomous decision-making, bridging the gap between automation and human-like reasoning.

The financial services industry has seen the potential and the value in artificial intelligence (AI) for decades — from assisting customers via simple chatbots to assessing creditworthiness and detecting fraud. Even with the industry’s significant regulatory barriers, AI has proven valuable as an efficiency tool for an industry awash in data.

However, businesses are now moving past the easiest AI efficiency hacks to focus more on enterprisewide AI adoption and discovering ways for the chief marketing office (CMO) to increase customer acquisition, satisfaction, and retention. Much of this AI expansion is fueled by generative AI tools and will be helped along by the emergence of AI agents that can act autonomously to achieve goals with little or no human intervention.

In the financial services sector, a majority of marketing leaders are already deploying AI at scale across various functions, such as content creation, e-commerce personalization, and advertising management, according to the Infosys CMO Radar report. And these AI initiatives are frequently generating business value — often through personalized nudges and A/B testing (Figure 1).

Figure 1. How marketers are using AI in financial services

Source: Infosys Knowledge Institute

Dubai-based Mashreq Bank faced high dropout rates from its new mobile app and low adoption of its debit card and loyalty program. Using MoEngage’s AI-driven customer engagement platform, the bank built omnichannel workflows and plugged key dropout points, resulting in a 16% increase in debit card activations. The platform enabled customized offers, improving click-through rates by 50%, which in turn led to better cross-selling and upselling.

AI assistants

AI first proved its value in financial services through algorithmic trading, fraud detection, predictive analytics, and other behind-the-scenes uses. Customers often remained unaware of AI’s integration into the industry until early chatbots emerged to ease pressure on call centers. Research from US federal regulators in 2022 estimated that 37% of the country’s population had interacted with a bank chatbot that year.

As AI has grown more sophisticated, these chatbots are delivering greater value, supporting more complex interactions, and improving customer satisfaction. Bank of America’s Erica virtual financial assistant was introduced in 2018 and since then has interacted with customers more than 2.5 billion times. The services range from finding transactions to providing insights into spending patterns.

More recently, UK-based NatWest has partnered with OpenAI to enhance its digital assistants and customer support. The bank reported that the AI upgrades have already delivered a 150% increase in customer satisfaction. Virtually all large financial institutions have AI chatbots or virtual advisors and are continuously improving their performance in parallel with AI’s growing capabilities.

In addition, payment processor Klarna and several e-commerce platforms have embedded large language model (LLM)–based chatbots and virtual assistants into their applications. These tools personalize the online shopping experience with curated product recommendations and prompt advice. Generative AI also boosts sales productivity by suggesting personalized product and pricing options or drafting documents needed to respond to requests for proposals from key accounts.

Not all AI interactions involve direct customer engagement. Banks now view AI assistants as digital coworkers that enhance productivity and help manage rising workloads. In financial services, these agents add value in investigations, customer due diligence, internal operations, and front-office activities. They generate client-facing summaries, unearth valuable insights, and support decision-making. By managing routine inquiries, AI agents also improve the customer experience with faster resolution times and more personalized service.

Goldman Sachs announced in early 2025 that it was providing a generative AI assistant to its bankers, traders, and asset managers. The GS AI Assistant was designed initially for routine tasks, such as writing emails and summarizing documents. However, in the future, the assistant might “actually reason more and become more like the way a Goldman employee would think,” according to Goldman Sachs chief information officer Marco Argenti. Other major institutions like JPMorgan Chase and Morgan Stanley have also deployed similar AI tools.

AI as innovator

Companies are still discovering how AI can deliver value. Increasingly, it drives innovation by enabling faster experimentation, deeper insights, and the development of new products and services. Rather than relying solely on historical data and fixed rules, AI tools can adapt to real-time inputs and shifting market dynamics. This adaptability allows businesses to test multiple strategies simultaneously, simulate outcomes, and optimize decisions at scale.

Sales teams, for example, can compress timelines that once took weeks or months. Generative AI analyzes client conversations to find patterns and suggest targeted product recommendations, revealing opportunities that might otherwise go unnoticed.

HSBC created an AI strategy for its credit card business using FICO’s Decision Optimizer to build predictive models that simulated customer behavior. It evaluated more than 40 scenarios to refine credit line offers. This proved particularly valuable during the Covid-19 pandemic when economic conditions were changing rapidly and unpredictably. The results were significant: Monthly credit card spending increased by 15% and customer engagement improved, even as bad debt levels remained stable.

CMO as AI leader

Marketers are already active in generative AI experimentation and adoption. This is particularly true in the banking sector, where they are more likely to use the technology than their peers in IT, sales, finance, or any other department, according to a report from software company SAS and Coleman Parkes Research.

Banks and other financial institutions already have granular data about their customer spending habits, but that’s not always enough. Financial services companies are turning to AI to sharpen their marketing and sales strategies. By analyzing customer behavior, preferences, and demographics, AI can segment audiences into smaller and smaller groups — then rank them with predictive scoring. This level of precision enables highly targeted acquisition campaigns, ensuring the right offer reaches the right person at the right time.

But the work doesn’t stop once a customer is acquired. AI can sometimes predict when a client might leave, well in advance of that decision. These churn prediction models draw on signals such as sentiment and customer usage patterns. Equipped with these insights, businesses can intervene early with personalized offers or support to boost retention. Research by Paddle calculated that 60% to 80% of churn is voluntary, meaning the customers actively decided to cancel credit cards or close accounts.

Underpinning these efforts is a layer of predictive analytics. Using supervised and unsupervised learning, AI can anticipate customer needs and actions — from life events to product interest — and tailor the experience accordingly and create insights that lead to upselling or cross-selling. Recommendation engines suggest the best next step, the right channel for communication, and the ideal moment to reach out with customized offers. The result: Deeper relationships, increased conversion, and a more responsive customer experience.

However, as AI adoption increases, CMOs must work closely with the rest of the C-suite to align marketing initiatives with enterprise strategy, ensure responsible data use, and integrate customer insights into product development and business model innovation. This cross-functional collaboration not only enhances operational efficiency and customer trust but also positions marketing as a strategic growth engine — critical in a sector where differentiation depends on customer relationships and regulatory compliance.

Responsible AI

Despite AI’s potential, financial services companies face significant challenges in its adoption and implementation. Government compliance, from the EU AI Act and General Data Protection Regulation to the US’s various regulatory agencies and rules, presents significant hurdles. In addition, financial institutions manage vast amounts of sensitive customer data, making them prime targets for cyberattacks. Nearly half of the marketers surveyed in the CMO Radar report identified data privacy or security as major concerns (Figure 2), just ahead of return on investment (ROI).

Figure 2. Barriers to AI integration for marketers in financial services

Source: Infosys Knowledge Institute

The ethical deployment of AI is essential for building trust with customers and stakeholders. CMOs must ensure AI solutions are transparent, fair, and free from bias, requiring well-defined ethics protocols backed by governance frameworks and key performance indicators.

However, only 42% of financial services respondents have comprehensive risk strategies in place, underscoring a critical gap in responsible AI adoption. Addressing this shortfall is crucial to mitigating risks, ensuring compliance, and fostering long-term confidence in AI-driven initiatives.

With these concerns in mind, Infosys created its Responsible AI Office framework to guide the ethical use of AI — building trust and promoting innovation. It defines the governance framework, including policies, procedures, and decision-making standards for AI initiatives. Also, the framework ensures AI systems comply with global regulations and reflect core principles such as transparency, fairness, and accountability.

“Using AI to provide advice or offers opens up a whole can of worms around AI ethics,” Stephen Greer, a banking industry consultant at software company SAS told payments and commerce news site PYMNTS. Greer said there is a concern that chatbots in particular could lead to an “overall erosion in consumer trust” in financial services.

How CMOs are successfully managing AI

To overcome these complications, Infosys research shows that CMOs must adopt a comprehensive approach to AI integration — rather than treating AI as just another technology tool. The following factors can lead to an increased chance of generating business value from AI initiatives:

Business processes: Successful AI deployment requires embedding AI solutions into business processes at scale. Nearly two-thirds of marketers in leading companies are able to rapidly integrate AI into their workflows, driving business value from AI deployments. Financial services businesses benefit by prioritizing seamless integration of AI with existing systems, such as customer relationship management and anti-money laundering systems, to create systematic governance and provide real-time insights to users.

Strategy: A coherent and dynamic AI strategy that aligns with broader business goals is crucial for maximizing AI's potential. CMOs should actively implement AI with a holistic view of priority use cases across the marketing value chain, focusing on value, feasibility, and risk. This approach enhances decision-making, operational efficiency, and customer engagement.

Risk management: Effective risk management is essential for responsible AI deployment. Financial services organizations should develop comprehensive risk management strategies, including dedicated risk functions and policies that integrate risk management into AI implementation. Structured plans should address domain-specific risks, such as misinformation or unintended outputs, particularly concerning generative AI.

MarTech: A strong technology foundation doesn’t guarantee success with AI, but it is a critical enabler. Companies that generate value from AI typically operate advanced, cloud-native, and highly scalable MarTech stacks. To stay competitive, organizations need a flexible architecture that can adapt to AI’s rapid evolution.

Aligning AI and marketing

Integrating AI into financial services marketing presents vast opportunities and complex challenges. AI can play a pivotal role in reshaping the financial services industry, driving a more efficient, responsive, and customer-centric future.

However, CMOs must align AI strategies with broader business goals while ensuring responsible and ethical deployment to fully capitalize on AI’s potential. CMOs can build trust while using AI to enhance employee support, streamline operations, and improve customer engagement. Beyond the bottom line, ethical AI practices not only drive efficiency and productivity but also foster stronger relationships with customers and stakeholders.