How AI Improves Customer Experience in Finance

What CTOs in Banking Should Do with AI for Customer Experience A few months ago, I sat with the CTO of a major bank who shared a familiar frustration: “We’ve invested millions in AI, but our customer experience hasn’t improved the way we expected.” I asked a simple question: “Are you using AI to solve real customer pain points, or are you using it because it’s expected?” That conversation led us down a path that many banking leaders are navigating today—leveraging AI not just for efficiency, but to truly enhance customer relationships. AI and the Future of Banking Customer Experience The global AI in banking market is expected to reach $130 billion by 2030, growing at a CAGR of 32% (Allied Market Research). This isn’t just about chatbots or fraud detection anymore; AI is redefining how banks engage with customers at every touchpoint. McKinsey reports that banks effectively using AI can increase customer satisfaction by 35% while reducing operational costs by up to 25%. The challenge, however, is execution—CTOs must ensure AI is seamlessly integrated into both digital and human interactions. How Leading CTOs Use AI for Customer Experience 1- Hyper-Personalization Example: JPMorgan Chase uses AI to analyze customer behavior and provide real-time loan and investment suggestions, increasing engagement by 40%. 2- AI-Powered Virtual Assistants Example: Bank of America’s Erica, an AI-powered assistant, has handled over 1.5 billion interactions, offering personalized financial insights. 3- Predictive Analytics for Proactive Engagement Example: A European bank using AI-driven insights reduced customer churn by 22% by proactively addressing financial concerns. 4- AI-Enhanced Fraud Detection Example: Mastercard’s AI-based fraud prevention has reduced false declines by 50%, improving trust and security. A Real-World Impact: AI in Action One of our banking clients struggled with high customer complaints about slow loan approvals. By integrating AI-driven document verification and risk assessment, approval times dropped from 5 days to 5 minutes. The result? A 30% increase in loan applications and a significant boost in customer satisfaction. The Human-AI Balance in Banking Despite AI’s capabilities, customers still value human interaction. 88% of banking customers want a mix of AI-powered convenience and human support when dealing with financial decisions (PwC). The key for CTOs is to balance automation with empathy—ensuring AI enhances, rather than replaces, the personal touch. The Road Ahead AI is no longer a futuristic concept in banking—it’s a strategic necessity. CTOs who embrace AI for customer experience, not just efficiency, will lead the industry forward. At Devsinc, we believe the future of banking isn’t just digital—it’s intelligent, personalized, and deeply customer-centric. The question is, are we using AI to replace transactions, or to build trust? Because in banking, trust isn’t just a feature—it’s the foundation.

AI isn’t just helping banks work faster, it’s helping them show up better for clients in moments of volatility. At JPMorganChase, their "Coach AI" is enabling wealth advisers to respond to client needs faster, anticipate queries, and tailor advice in real time. During recent market turbulence, these tools helped teams scale service without sacrificing quality. According to Mary Callahan Erdoes, AI has allowed JPMorganChase advisers to find information 95% faster and engage more meaningfully. The firm expects its advisers to grow their client base by 50% over the next three to five years, driven by these gains in speed and efficiency. This is a strong signal of where I see our industry heading: intelligent tools that support human relationships, especially when clients need them most. Read the Reuters story for more: https://lnkd.in/eewAtaBC #AI #WealthManagement #ClientExperience #BankingInnovation #FinancialServices #JPMorgan

Learnings from transforming CX with Gen AI for a Financial Services giant in APAC 🚀 One of the largest Financial Services players in the APAC recently leveraged Verloop to transform its contact center. The outcomes? Transformational change in customer support experience which not only drove CSAT up but also helped them bring efficiency into their CX Ops. Here is a snapshot of outcomes and learnings Outcomes -------------- 1. About 30% increase in Customer Satisfaction score 2. 43% fewer tickets assigned to their support desk 3. 70% Reduction in Average Response Time 4. 30% Cost Savings by CX efficiency Learnings -------------- 1. Effort - Easier said than done; most models are great for building demos but a nightmare when implementing large complex scenarios 2. Focus - Niche-trained LLMs work better than a large model 3. Latency - Latency in response especially in audio calls is a deal breaker. 4. RAG + LLM - Balancing when to refer to RAG vs when should LLM handle the task takes a while 5. Cost - Models cost significant amount of money to run; attach and focus on business outcomes 6. Data Quality - Investing time in data cleansing and organization pays off massively 7. AI + Human - AI handles the repetitive tasks, while AI-assisted human agents are required for empathy and complex problem-solving 8. Keep Building - Continuous improvements and training of flows is critical more so in the first few months of launch Implementing Guardrails --------------------------- 1. Focus on Ethical AI usage with strict guidelines to ensure AI operates within ethical boundaries, maintaining transparency and customer trust. 2. Adhere to rigorous data privacy regulations to protect customer information. Protecto works like a charm! 3. A key trait of any such implementation is AI knowing when to hand over Launch Experience -------------------- 1. Collaborative Approach - Everyone is learning in this journey; engage early and frequently with all stakeholders 2. Stay Agile - Launch iteratively and keep improving instead of one big bang launch 3. Human training - Focus on training all stakeholders; things are different vs structured data We started Verloop with the idea that the future of contact centers is AI-first, human-assisted. These engagements help us stay on the course and keep building towards our vision. We are already living in the future and it is slowly spreading everywhere! 🌟 #contactcenter #GenAI #CXTransformation #transformation Verloop.io CA. Ankit Sarawagi Melisa Vaz Nikhil Gupta Urvashi Singh Kiran Prabhu Ravi Petlur Kumar Gaurav

Came back from vacation Monday. Inbox? On fire.🔥 Buried in the chaos: a customer story that stopped me in my tracks (and made me so happy). A Customer Support leader at a fast-growing financial services company used AI to transform his team - in just a few weeks. This leader works for a financial services company that’s in high-growth mode. Great news, right? Yes! For everyone except his Customer Support team… As the business grew faster, they were bombarded with repetitive questions about simple things like loan statuses and document requirements. Reps were overwhelmed. Customers faced longer response times. The company has been a HubSpot customer for nearly 10 years. They turned to Customer Agent, HubSpot’s AI Agent, and got to work: - Connected it to their knowledge base → accurate, fast answers - Set smart handoff rules → AI handles the simple, reps handle the complex - Customized the tone → sounds like them, not a generic bot (you know the type) In a short space of time, things changed dramatically: - Customer Agent now resolves more tickets than any rep - 94.9% of customers report being happy with the experience - For the first time, the team can prioritize complex issues and provide proactive support to high-value customers It’s exciting to see leaders using Customer Agent to not just respond to more tickets, but to increase CSAT and empower their teams to drive more impact. 2025 is the year of AI transformed Customer Support. I am stunned by how quickly that transformation is playing out!

Voice data holds the key to better customer experiences. Every day, businesses have thousands of conversations with customers. Inside those interactions are insights that can strengthen relationships, improve service, and drive better outcomes. The challenge has always been finding a way to access that data without breaking the bank. Now, AI is making that possible. Businesses of all sizes can use AI to analyze voice data to spot trends, understand customer needs more clearly, and make decisions that create a real impact. From improving response times to personalizing interactions and identifying common pain points, this data provides a roadmap for meaningful change. The answers CX teams need are already in the conversations they’re having. Now is the time to start putting them to work.

How SAP is Using AI to Enhance Customer Experience SAP leverages AI to significantly improve customer experience through a variety of innovative approaches and tools. Here are the key ways SAP is enhancing customer interactions: 1. Personalized Interactions - Customer Profiles: AI-generated customer profiles powered by real-time data from the SAP Customer Data Platform enable businesses to deliver tailored and relevant experiences. This includes personalized recommendations and targeted marketing content. - Predictive Engagement: AI's predictive analysis allows businesses to anticipate customer needs and offer proactive solutions, enhancing engagement and satisfaction. 2. Automation of Repetitive Tasks - Role-Based AI Tools: SAP provides job-specific AI tools to automate time-consuming tasks for service, sales, and commerce teams. This includes generating content, summarizing customer issues, and suggesting solutions, which frees up teams to focus on more value-adding activities. - Catalog Management: AI assists in product discovery by automatically extracting and enriching product attributes from images and text, generating product descriptions, and improving search capabilities, which enhances the shopping experience 3. Enhanced Customer Support - Proactive AI Responses: AI models in SAP's Customer Experience portfolio detect questions and suggest responses in natural language, derived from business data. This proactive approach helps resolve customer queries faster and more accurately. - Self-Service Options: AI-powered self-service tools and chatbots provide 24/7 customer support, reducing response times and improving availability. 4. Integration with Business Processes - Embedded AI Features: SAP integrates AI capabilities directly into its products, such as SAP Sales Cloud, SAP Service Cloud, and SAP Commerce Cloud. These embedded features help in generating personalized content, automating responses, and providing real-time insights. - Holistic Data Utilization: SAP's AI solutions leverage data from various sources, including ERP systems, to provide comprehensive insights and enable more informed decision-making, leading to better customer experiences. 5. Generative AI Innovations - Joule AI Assistant: SAP introduced Joule, a generative AI assistant that helps streamline customer service and marketing tasks by providing contextual insights and automating routine processes. This enhances productivity and ensures more personalized customer interactions. 6. Predictive and Preventative Support - Predictive Analytics: AI-driven predictive analytics in SAP solutions help businesses forecast demand, optimize inventory, and plan more effectively. This ensures that customer needs are met promptly and efficiently. By embedding AI across its customer experience solutions, SAP aims to deliver more personalized, efficient, and proactive customer interactions, ultimately driving higher satisfaction and loyalty. #SAP #AI #ZaranTech