Supporting Financially Vulnerable Customers: Advanced Segmentation Strategies for Inclusive Banking

The Imperative for Inclusive Banking

Economic uncertainty, rising living costs, and evolving regulatory expectations—such as the UK’s Consumer Duty—have placed a renewed focus on the need for banks to proactively identify and support financially vulnerable customers. Traditional segmentation models, which rely on basic demographics like age, income, or location, are no longer sufficient. To truly put customer interests at the heart of banking, institutions must embrace advanced segmentation strategies that integrate behavioral and psychographic data, powered by AI and machine learning. This approach not only drives compliance but also delivers meaningful, personalized support to those who need it most.

Why Traditional Segmentation Falls Short

Conventional 1D or 2D segmentation models provide a limited view of customer needs. Two customers with similar incomes may have vastly different financial habits, risk tolerances, or values. Relying solely on basic data can result in generic offerings, missed opportunities for early intervention, and even regulatory risk if vulnerable customers are not adequately identified and supported. The UK’s Consumer Duty regulation, for example, requires banks to demonstrate a comprehensive understanding of customer vulnerability and to act in their best interests at all times.

The Power of 3D Segmentation and Psychographic Data

3D segmentation maps go beyond demographics and transaction history to include psychographic data—insights into customers’ values, attitudes, motivations, and lifestyle characteristics. By leveraging advanced analytics and machine learning, banks can cluster customers into more meaningful segments, revealing:

Who is at risk of financial stress or vulnerability (e.g., based on spending patterns, missed payments, or life events)
What motivates different segments (e.g., risk aversion, sustainability values, digital savviness)
How best to engage and support each group (e.g., preferred channels, content, and interventions)

This richer, multi-dimensional view enables banks to:

Identify early warning signs of distress and intervene proactively
Personalize products, communications, and support to each segment’s needs
Demonstrate compliance with regulatory requirements through transparent, data-driven decision-making

Integrating Behavioral and Psychographic Data: Practical Steps

Validate Segments with Multiple Data Sources
- Use transaction history, customer feedback, social media, and support center data to ensure segments reflect real behaviors and needs.
- Continuously update models to reflect changing circumstances and new insights.
Incorporate Psychographic Data
- Gather insights on values, personality traits, interests, and lifestyle through surveys, digital interactions, and social listening.
- Use this data to enrich segmentation and personalize offerings.
Humanize the Data
- Ask: What are customers trying to achieve? What motivates their financial decisions? How do they want to interact with the bank?
- Design interventions that are empathetic, accessible, and relevant.
Leverage AI and Machine Learning
- Use advanced analytics to detect patterns, predict risk, and automate personalized nudges or support.
- Ensure transparency and mitigate bias in models to build trust and meet regulatory standards.
Visualize and Communicate Clearly
- Develop segmentation maps that are easy to interpret and actionable for all stakeholders.
- Use these tools as a single source of truth to align teams and drive consistent, customer-centric action.

Using AI to Detect Early Warning Signs of Distress

AI and machine learning can process vast amounts of structured and unstructured data—from transaction histories and digital interactions to lifestyle attributes and social sentiment. These technologies can:

Identify hidden patterns and micro-segments that would be impossible to detect manually
Predict customer intent and life events (such as job loss, health issues, or major purchases) before they are explicitly stated
Continuously refine segments as new data streams in, ensuring that support strategies remain relevant and effective

For example, AI can flag customers who show early signs of financial stress—such as increased overdraft usage, missed payments, or sudden changes in spending behavior—enabling banks to offer timely, tailored support before issues escalate.

Designing Empathetic, Personalized Interventions

Supporting financially vulnerable customers requires more than just identifying them—it demands action. Banks can:

Offer budgeting tools and financial wellness resources tailored to individual needs
Provide responsible lending criteria and targeted education to prevent over-indebtedness
Use digital nudges and reminders to encourage healthy financial behaviors
Ensure that support is accessible through preferred channels, whether digital or human-assisted

Crucially, interventions must be designed with empathy. This means using inclusive, motivational, and reassuring language, and ensuring that digital tools are easy to use and understand. Combining technology with the human touch—such as offering access to financial coaches or dedicated support teams—can make a significant difference in outcomes.

Real-World Examples and Best Practices

Budgeting and Savings Tools: Challenger banks have introduced features like ‘pots’ or ‘spaces’ to help customers allocate funds for different purposes, set savings goals, and lock away money to prevent overspending. Traditional banks, with their wealth of historical data, can go further by predicting unhealthy spending habits and proactively offering advice or nudges.
Responsible Lending: Fintechs have pioneered Buy Now, Pay Later (BNPL) products with controls to prevent over-borrowing. Banks can implement similar solutions, ensuring fair eligibility criteria and controls that match customer needs and maturity.
Financial Literacy and Resilience: Leveraging Open Banking, banks can help customers visualize their financial situation, identify opportunities to save, and suggest switching alternatives to reduce costs.
Human + Digital Support: Platforms that combine AI-driven insights with access to human financial coaches empower customers to make informed decisions and build long-term resilience.

Continuous Improvement and Compliance

Customer needs, behaviors, and vulnerabilities are constantly evolving. The most successful banks treat segmentation as a living process—continuously discovering, validating, and refining segments to stay ahead of change. Best practices include:

Embedding test-and-learn cycles to measure the impact of interventions and refine strategies
Breaking down organizational silos to foster collaboration between data science, compliance, CX, and frontline teams
Ensuring robust data governance, privacy, and ethical standards at every stage

By adopting these practices, banks can not only meet regulatory requirements but also build long-term loyalty and resilience among their most vulnerable customers.

The Path Forward: Inclusive, Data-Driven Banking

Advanced segmentation is not just a technical exercise—it’s a strategic imperative for inclusive banking. By integrating behavioral and psychographic data, leveraging AI, and designing empathetic interventions, banks can proactively identify and support financially vulnerable customers. This approach delivers on the promise of Consumer Duty, drives measurable business impact, and—most importantly—makes a real difference in the lives of those who need it most.

Ready to unlock the power of advanced segmentation for inclusive banking? Connect with Publicis Sapient’s experts to start your journey toward more resilient, customer-centric financial services.