Discover top fintech news and events!
Subscribe to FinTech Weekly's newsletter
Read by executives at JP Morgan, Coinbase, Blackrock, Klarna and more
“Revenues in the fintech industry are expected to grow almost three times faster than those in the traditional banking sector between 2022 and 2028” – McKinsey, Oct24, 2023.
"The global fintech market is projected to be worth $394.88 billion in 2025 and reach $1,126.64 billion by 2032” – Fortune business insights, June 09, 2025
Customer engagement is one of the key differentiators between traditional banking & financial services institutions and fintech. Starting from a seamless customer onboarding to validations to executing transactions to subsequent servicing and grievance redressal, fintech excel traditional financial institutions. Over time, fintech have tried to bridge the gap and excel in customer engagement. Research shows this is the single most important factor, that leads to bottom line improvement.
Despite the developments in digital technologies and banks’ efforts, customer service still continues to be one of the major areas of improvement. “Personalization” and “Speed of customer service” are still rated low in the satisfactions surveys1, providing ample opportunities for banks and financial services organizations to improve the quality. The gap further widens for wealth management customers, where the need for personalization and specialized knowledge matter the most, building trust and loyalty. This is where AI Agents empowered with specialized domain knowledge can drive engaging and intelligent customer interaction. Customer service being at the forefront of business interaction, that drives just not satisfaction level, but also long-term loyalty and lifetime business value.
An Agentic AI mesh with multiple specialized agents can perform activities simultaneously, such as pulling customer interaction histories, sentiment analysis, life events, analyzing competitive landscape on products and fees, analyzing market trends etc., and providing informative guidance to customers. Using NLP and voice enabled technologies, the interaction can be made intuitively matching customer’s preferred style, language agnostic and omni channel enabled. The benefits of GenAI are real and some recent implementations by banks are showing positive outcomes. Improvements in experience are one of the major benefactors.
AI-Human collaboration is one of the most mutually beneficial outcomes of recent technological developments. Artificial intelligence systems demonstrate exceptional proficiency in the processing of enormous volumes of data, identifying trends and patterns with accuracy and velocity.
Generative AI further advances this capability, by generating recommendations for human agents that enhance customer experience and engagement. Personal Financial Advisors, once a privilege of ultra-high net worth customers, can now be democratized by AI Agents and be made available to a wider customer base.
Banks, being privy to a host of customer’s personal information and transaction history, can provide a concierge of services, from tax planning to investment advisory, even acting as a personal assistant. By this gradual enablement of AI Agents to handle complex and personal tasks, banks and financial services organizations can provide superior customer experience leading to enhanced loyalty and lifetime value.
Agentic AI & the hype around it
Gartner technology trend 2025 placed Agentic AI as the top trend in 2025. MITSMR 2025 AI & Data Leadership Executive benchmark Survey also forecasted similar outcome.
What is Agentic AI? It refers to “AI systems and models that can act autonomously to achieve goals without the need of constant human guidance, says HBR. It understands the goals and objectives of the user and the context of the problem they are trying to solve”. It’s a self- learning system that uses sophisticated reasoning and creative abilities of GenAI models to solve multi-step complex problems. An Agentic mess is a team of multiple agents, who can perform tasks simultaneously aligned with a single objective.
“Agentic AI Systems promise to transform many aspects of human-machine collaboration with their supercharged reasoning and execution capabilities. They can plan and make decisions independently, offering greater productivity, innovation, and insights for the human workforce”
– HBR, Dec 2024
A Sample representation of an Agentic AI Customer service system
All these agents perform their tasks concurrently and report to the manager agent, who in intern responds to customer queries. Curated domain knowledge and training makes these agents an expert in their area. The vast organizational library of wealth management research and data points are resources, which can be leveraged to train the AI Agents.
Some of the key use cases in customer service are:
- Virtual financial advisor
- Customer profiling
- Realtime fraud monitoring
- Executing routine tasks
- Reporting
Customer Profiling, which is the first step to knowing a customer, is another key use case that drives customer engagement. The better a bank knows its customers, the better it can serve and build a lasting relationship. It’s an arduous process. Despite the progress in technology, it is still time consuming and has lots of scope for improvement. Over years, OCR Technologies and varied levels of automation at different stages have vastly improved the process of capturing, processing and utilizing customer information. Autonomous AI Agents offer lots of hope and possibilities to further transform the process, making it seamless and performing multiple concurrent activities.
AI Agents, using its ecosystem of AI powered tools such as biometric validation, facial recognition, API enabled document verification etc. can perform simultaneous validations in parallel while capturing the data.
As evidence shows, the current process is susceptible to fraudulent actors, who could bypass validation mechanisms such as liveliness test etc. AI Agents have the capability to make this process robust, by analyzing contextual signals such as the device angle, or running of any unauthorized software in the background etc. Additionally, AI Agents ability to process unstructured data combined with sentiment analysis can lead to a robust risk profiling of the customer creating more accurate persona. This deeper level of scrutiny combined with real time simultaneous validations enhances security level and helps prevent sophisticated fraud attempts by unscrupulous elements, making the system safe. This leads to increased trust, enhanced customer engagement and loyalty.
Learnings:
- A typical customer interaction may involve multiple enquiries—like recent transactions, product recommendations, and billing errors—all in a single conversation.
- Traditional chatbots often fail to handle such multifaceted interactions and may lose context.
- Traditional chatbots can’t churn customer portfolios by executing investment transactions on wealth management products
- Agentic AI operates at a more advanced level, functioning like digital team members with:
Autonomy to act without constant human intervention.
Goal-oriented intelligence to pursue and achieve specific outcomes.
Real-time reasoning capabilities for dynamic decision-making.
- These systems can:
Understand nuanced and natural human language.
Maintain contextual coherence across long and complex dialogues.
Integrate and orchestrate tasks using tools like CRM, ERP, and internal knowledge bases.
- In customer engagement, Agentic AI delivers:
24/7 supports those mimics human interaction.
Scalable handling of complex and layered customer issues.
Personalized, fluid conversations enabled by a network of micro-agents, each specializing in a specific customer need.
- The approach moves beyond basic query resolution—it ensures full problem ownership and end-to-end resolution.
Call to Actions for Industry Leaders:
Now comes the strategic question; what should industry leaders do to not just experiment but operationalize agentic AI for transformative gains? First, they must move beyond pilot fatigue and select high-impact customer engagement use cases to test in “copilot mode”.
That is augmenting human agents, not replacing them. Second, invest in training frontline teams to work alongside AI, not around it. AI should be their partner, not a parallel process. Third, shift budgeting models from per-seat software to outcome-based service-as-a-software contracts; pay per resolution, not per license. Fourth, leaders must integrate data across silos like marketing, service, operations, to feed these systems with the context they thrive on.
And finally, lead with trust; deploy ethical guardrails, measure performance transparently, and let customers know that while machines may handle queries, humans are always in the loop. In this new era, winning isn’t about building the tech, it’s about enabling people and processes to amplify its impact.
References:
- https://www.salesforce.com/resources/research-reports/financial-services-report/
- https://www.sas.com/en_us/news/press-releases/2024/october/generativeai-banking.html
- https://www.gartner.com/en/articles/top-technology-trends-2025
- https://sloanreview.mit.edu/article/five-trends-in-ai-and-data-science-for-2025/
- The future of fintech growth | McKinsey
- https://www.fortunebusinessinsights.com/ - FinTech Market Overview with Size, Share, Value | Growth [2032]
