Industry
Capital Markets
Unlocking Exponential Future Growth in Capital Market Firms
Banking
Banking Services Transformation: Unlocking Exponential Value
Solution
TCS BaNCS™
TCS BaNCS™: Enabling Transformation in Banks and Financial Institutions
Highlights
- AI is rapidly reshaping customer engagement in financial services and financial institutions are now leveraging AI to personalize interactions and build trust.
- Financial institutions are now using AI decisioning, Agentic AI, and GenAI to deliver transparent, empathetic, and ethical customer engagements.
- With examples from BFSI, the article highlights how AI technologies empower financial institutions to build trust and drive sustainable growth through human-centric AI applications.
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Overview
Raw data to meaningful engagement
While intelligence is abundant in today’s digitized financial services landscape, the challenge lies in applying intelligence to drive customer trust, business value, and sustainable growth. Artificial Intelligence powers credit approvals, automated investment advice, and personalized insurance coverage. But without purpose, it risks adding complexity or eroding trust. While customers appreciate speed and convenience, they expect accuracy, transparency, and empathy from financial institutions. Used purposefully, AI can meet and exceed these expectations.
Raw data to meaningful engagement: Financial institutions on modern platforms handle immense volumes of transactional, behavioral, and market data. Retail banks track deposits, lending, and digital interactions; broker-dealers manage trades across asset classes; insurers record policy status, claims activity, and risk scores. The paradox? They are data-rich but insight-limited — with valuable signals often trapped in system silos or locked in performance reports.
To move from volume to value, institutions must pivot from:
- Fragmented analytics to connected intelligence ecosystems.
- Reactive to predictive engagement by anticipating needs and guiding action.
- Broad segmentation to truly 1:1 personalization and delivering relevant offers at the right time
Purpose-driven AI pillars
Leverage AI to build trust at scale.
- Empathy at scale.
Purpose-driven AI goes beyond transactional convenience to serve human needs. AI can help anticipate customer needs, such as alerting a customer to an upcoming loan repayment shortfall or recommending a tax-efficient investment near fiscal year-end.
Some examples of how leading brands use AI to drive empathy at scale: A leading banking firm from India has started using AI to automatically detect real-life events like salary crediting from a new employer. and uses this information to suggest offers on home and personal loans. One of the top insurance providers in the United States actively uses AI to spot patterns in telematics and health data and offers wellness rewards. However, it is important to remember that brand communication and product recommendations need to be framed keeping the customer’s life in context, not just financial metrics. - Transparency and explainability.
Trust increases when customers understand why an AI-driven decision happened. Whether it’s explaining loan eligibility or why a transaction was flagged, AI, used in the right manner, turns potential friction into trust-building moments. - Adaptive journeys.
AI can move beyond linear customer journey maps to create dynamic, decisioning-led pathways. This involves continuously adjusting the “next-best action” based on moment-to-moment behavior, channel preference, and even emotional sentiment detected in communications.
A top Fintech mobile app in the Middle East created an AI-led workflow automation that skips redundant steps based on applicant behavior, such as when data they’ve shared is already validated via open banking APIs. This adaptive customer journey helps their customers convert faster. - Ethical and regulatory alignment.
Purpose means balancing personalization with privacy. AI engagement must follow strict governance in line with data protection laws and ethical principles, making customer-interest the north star alongside business aims.
Engaging tomorrow’s customers
Leveraging AI across the customer lifecycle.
- Onboarding and activation.
The early stage is critical for lifetime value. AI can identify early drop-off points in application flows and push assistive interventions, such as real-time chat handoffs or how-to microvideos. For digital KYC, intelligent document classification paired with fraud risk scoring speeds up onboarding while ensuring security compliance. This ensures higher completion rates, reduced onboarding costs, and stronger first impressions. - Deepening active engagement.
Over 70% of customers prefer lifecycle-relevant communication. The WEF report predicts that by 2035, contextual immediacy will become the baseline, with BFSI players competing more on how timely and seamlessly they embed value into customer life events than on traditional product features.
To drive higher engagement, an online mobile app in Singapore implemented AI to detect “micromoments” like spikes in trading activity during market swings and then triggered timely and contextually relevant messages instead of generic alerts, leading to higher mobile app traffic from their customers. - Retention and advocacy.
Churn prediction is common, but the purpose requires acting on those predictions. If risk of lapse is rising for a policyholder, an intelligent engagement system can prescribe exactly which outreach approach has historically improved retention for similar profiles — whether a call, an incentive, or a policy review session. Similarly, customers showing advocacy potential can be nurtured for referrals or brand ambassador roles, closing the engagement loop. This results in lower churn, better Net Promoter Scores, and improved lifetime value. It’s clear that the future of customer engagement in the BFSI sector is AI-driven.
Road ahead
Predictive models, AI decisioning, Agentic AI and GenAI.
AI decisioning engines.
AI decisioning moves beyond static journeys to create truly 1:1 personalized communications that adapt to each customer’s real-time context.
- In banking: Deciding in milliseconds whether to serve a fraud alert before a promotional offer.
- In insurance: Choosing whether to send seasonal safety tips or renewal reminders based on policyholder engagement. These decisioning engines combine predictive AI models with business rules and compliance guardrails, ensuring every “next-best action” is profitable, timely, and aligned with customer trust.
- Offer decisioning for hyper-personalization: The latest generation of AI in customer engagement includes offer decisioning, which autonomously identifies and decides the best possible offer for every individual customer — not just at the segment level.
For BFSI, this means:
- In retail banking: Determining whether to offer a top-up loan, credit card upgrade, or fee waiver based on current balance patterns and recent spending.
- In insurance: Selecting the optimal retention offer — such as a premium discount, coverage extension, or loyalty reward — within predefined eligibility, frequency, and expiration constraints
- Offer decisioning gives marketers tight control via guardrails (eligibility rules, frequency caps, offer usage tracking), ensuring personalization operates within both profitability and compliance boundaries.
Agentic AI
From automation to true autonomy.
Agentic AI represents the next stage of AI maturity. Unlike traditional automation or standalone GenAI, agents understand context and reason through complex trade-offs and autonomously execute campaigns while learning from results.
A leading financial institution in India has successfully implemented AI agents to:
- Optimize onboarding flows in real time as soon as friction is detected by automatically switching the customer to a faster KYC validation path.
- Dynamically sequence different lending, investment, or insurance offers based on market conditions and predicted customer intent.
- Monitor live performance metrics and recalibrate campaigns to meet revenue and customer satisfaction KPIs.
Generative AI
Hyper-localized and tailored communication.
GenAI can craft compliant, hyper-localized, and context-appropriate messaging for any lifecycle stage, such as drafting policy or contract summaries in the customer’s preferred language and tone or producing personalized investment advice narratives based on portfolio performance. When paired with offer decisioning and Agentic AI, GenAI ensures the right message is delivered at the right moment — multiplying campaign impact.
From prediction to prescription: While predictive AI flags churn risk, fraud likelihood, or portfolio exposure, the leap forward is prescriptive AI, which, powered by offer decisioning and Agentic AI, immediately acts on those predictions with targeted, compliant, and tested interventions.
This allows BFSI enterprises to operate with agility, responding to market shifts, customer micro-signals, and regulatory demands in near real-time.
Conclusion
Sustainable value through AI and purpose.
In financial services, the true power of AI is not in faster decisions, but in more meaningful ones. When intelligence is applied with empathy, transparency, and integrity, every engagement becomes an opportunity to deepen trust. The leaders of tomorrow will be those who fuse technology with a human touch — using AI not just to predict outcomes, but to shape lasting relationships and sustainable growth.
References
- BFSI SOCCM Report 2025: https://www.moengage.com/wp-content/uploads/C_FINAL_DIGITAL_BFSI_SOCCM_report_compressed-3.pdf
- Artificial Intelligence in Customer Engagement: https://www.moengage.com/capabilities/ artificial-intelligence