Richard Winston is a Managing Director and Global Head of Financial Services at Slalom.
We’ve all faced the frustration of being trapped in a seemingly endless virtual loop—stuck in a voice response system or conversing with a chatbot that seems to repeat endlessly, asking the same questions and delivering irrelevant, sometimes even off-topic, responses.
The financial services sector, eager to embrace innovative technologies to enhance customer experiences and streamline operations, often falls into this trap. Many financial institutions have adopted chatbots and rules-based systems, promising improved customer interactions and reduced operational costs. Yet, these solutions frequently miss the mark, offering little more than a glorified FAQ database. Customers find themselves navigating rigid decision trees, repeatedly providing the same information, and eventually resorting to calling a human representative—or worse, abandoning ship for a more progressive competitor.
This outdated approach not only fails to meet the rising expectations of today’s tech-savvy consumers but also neglects the vast potential of new tools and data to foster deeper customer insights and engagement.
Making Relevance Matter
By focusing on “relevance” (over single-minded efficiency) and harnessing the power of data-powered hybrid AI approaches—an advanced fusion of machine learning, generative and conversational AI and natural language processing—financial institutions can transcend the limitations of outdated rigid solutions. By combining the strengths of different AI methodologies, hybrid AI can address complex challenges more effectively than any single AI type alone.
A hybrid AI approach enables financial institutions to deliver highly personalized and anticipatory customer experiences by leveraging machine learning for predictive analytics, generative AI for dynamic content creation and NLP for nuanced understanding and interaction. This synergy enhances the accuracy and relevance of responses and creates a seamless and intuitive user experience. Through this approach, financial institutions can transform their customer service and advisory processes, leading to higher satisfaction and loyalty.
To understand how financial institutions can elevate relevance, it is useful to look at how companies such as Netflix, Spotify and Amazon have mastered the art of personalization. Netflix suggests content based on a variety of factors including viewing history and preferences, creating a highly personalized viewing experience. Spotify curates playlists and recommends songs by analyzing listening habits and social trends. Amazon anticipates needs and preferences, offering product recommendations that often feel intuitive and timely. These companies succeed by focusing on relevance—using data-driven insights to anticipate user needs and provide tailored suggestions.
Financial institutions can adopt a similar approach to transform their conversational AI. Doing so can improve customer satisfaction as delivering timely, accurate and personalized responses enhances the overall customer experience. And by automating routine inquiries and transactions, financial institutions can free up human agents to focus on more complex tasks. Conversational AI also generates valuable data on customer preferences and behaviors, which can inform strategic decision making and drive innovation.
Additionally, organizations that leverage advanced AI to provide superior customer experiences can stand out in a crowded market, attracting and retaining more customers.
From Reactive To Relevant
To deliver on relevant responsiveness, financial institutions need to embrace the next generation of AI-driven customer experiences. This means moving beyond older practices and response models to create truly relevant, context-aware engagement. Here’s how that might look in practice:
Customer Service
• Reactive: Static, scripted responses with limited customization.
• Relevant: Proactive, personalized responses using recent customer history and behavioral analysis.
Financial Advisory
• Reactive: Generic advice based on limited customer data and periodic reviews.
• Relevant: Real-time, AI-driven financial advice tailored to individual goals, risk profiles and investment activity.
Fraud Detection
• Reactive: Manual review and periodic checks leading to delayed responses.
• Relevant: AI/ML-driven continuous monitoring and instant alerts with contextual explanations and preventive actions.
Loan Processing
• Reactive: Lengthy application processes with standardized options and not leveraging existing customer data.
• Relevant: Insight-led, streamlined application process with predictive analysis for approval and personalized loan options.
Insurance Claims
• Reactive: Manual claim processing with prolonged wait times and limited updates.
• Relevant: Efficient claim filing with real-time status updates and AI-guided assistance and verification.
Wealth Management
• Reactive: Quarterly or annual reviews with static portfolio adjustments.
• Relevant: Dynamic portfolio recommendations and adjustments based on market trends and customer preferences.
Customer Onboarding
• Reactive: Paper- or form-based, time-consuming onboarding with generic product offerings.
• Relevant: Seamless, AI-assisted onboarding with personalized product suggestions.
Investment Research
• Reactive: Periodic, one-size-fits-all research reports.
• Relevant: Real-time, AI-curated research reports and insights personalized to customer interests.
Risk Assessment
• Reactive: Annual or semiannual risk assessments with broad recommendations.
• Relevant: Continuous, AI-driven risk assessment with personalized risk mitigation strategies.
Customer Engagement
• Reactive: Engagement based on customer-initiated contact.
• Relevant: Predictive engagement based on customer behavior, preferences and life cycle events.
Relevance matters. Customers will tolerate, even anticipate, nonhuman interactions if those deliver expected positive experiences and outcomes. To achieve this level of relevance, financial institutions must move beyond rule-based interactions to systems capable of natural language processing and deep learning. They need to leverage big data to integrate information from various sources to create a holistic view of each customer’s financial life and prioritize user experiences that are intuitive, engaging and truly conversational.
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