How AI Can Support Vulnerable Customers

If you were asked to define vulnerability, how confident would you be that you could reduce it down to one clearly defined thing? The point is that vulnerability is a complex state and there are so many factors that contribute to whether someone is ‘vulnerable’ and even if they fit into one of those categories, that doesn't mean that they see themselves as vulnerable.

So when we kicked off this podcast with such a broad and slightly philosophical question, "can AI understand vulnerability?", James Brooks, Head of AI & Automation at FourNet gave an impressively clear and concise answer, “The short answer is no. But it can assist.”

He goes on to emphasise that distinction is everything. AI can “pick up on specific identifiers” without truly understanding vulnerability in the way humans do. James breaks this down into “the spoken and the unspoken. So the spoken being in a conversation and whatever channel that happens to be, so voice, text-based, the AI can analyse language and pick up on what’s being said within that in terms of the topics that they’re talking about, but also the way they kind of frame the language.”

On the unspoken side, “you’ve got more of their behaviours. So what’s happening behind the scenes?” In financial institutions, there are “tons of behavioural metrics, scores, flags, indicators. They can all be used to give or to paint a picture of potential vulnerability or predict a vulnerable status.”

Christina Albert, Director of Operations at MoneyPlus who joined us for this session agrees, stating that “I don’t think AI can understand or define vulnerability. I think it’s far too broad. It’s a spectrum of different needs that customers have that can change all the time.” 

But here’s where AI can support vulnerable customers. “In a busy advice call where there’s lots going on, we’re taking a lot of information from the customer, we’re trying to listen to a lot of information and get advice across at the same time, the kind of prompting or support that we can give an advisor during a call will be hugely valuable.”

Even something as simple as “pause here, check for understanding. You’ve gone through a particularly complicated part of the journey” can make a real difference. The excitement comes from scale, “Being able to scale that up and look at trends. If we can identify trends, if we can find cohorts of customers that are experiencing specific types of vulnerabilities, then we can design journeys around that. We can, rather than being reactive, we can be quite a lot more proactive.”

This can be as simple as a group of keywords triggering vulnerability flags, or as sophisticated as behavioural pattern analysis across multiple data sources. But Andrew Tucker, Solutions Architect at NiCE, cuts to the fundamental challenge of deciding “where do you focus what is in effect a finite amount of resources to have the most impact on the people who are the most needy?” That’s where clear vulnerability definitions become operational, not just theoretical. 

MoneyPlus addresses this through “a specialist care team that’s in place for customers who we believe are particularly vulnerable,” while also recognising that some customers “might want to communicate with us via digital channels rather than over the phone. “The definition has to work for both specialist intervention and everyday support.”

Unfortunately there isn’t a simple answer to this question. The key is defining vulnerability in ways that are specific enough to be actionable, broad enough to catch those who need support, and flexible enough to adapt as circumstances change. Start by analysing your current customer journeys; where do bottlenecks occur? When are customers most likely to need support? How can you offer it to them in those moments? The most effective vulnerability strategies combine data analysis with intelligent customer journey mapping to meet people where they actually are.

A keyboardless agent is someone freed from system navigation, data entry, multiple screens, and CRM updates to focus purely on the customer conversation. AI handles the background tasks whilst the agent concentrates on listening, empathising, and responding to vulnerability cues.

Andrew challenges organisations to look beyond customer-facing journeys and examine “the internal touch points and friction points to access these systems and processes.” Often, organisations create their own blockers. Identifying vulnerability is one thing, but if the path from identification to specialist support involves ten steps and three system handoffs, you’re holding yourself back.

Christina connects this directly to Consumer Duty, noting that “products and services are designed in a way that meets our customers’ needs.” If internal complexity prevents agents from delivering good outcomes, that’s a Consumer Duty issue, not just an operational one.

Consider mapping your internal processes alongside customer journeys. Where does complexity slow down your response to vulnerable customers? Which system handoffs could be automated? How much cognitive load are you placing on agents who need to spot subtle vulnerability cues whilst managing multiple tools? AI can declutter these processes, but only if you first identify where the friction exists.

Christina is clear about the nuance. “Some customers are happy for us to know their vulnerabilities, some find it more difficult to talk about.” The challenge is that over-labelling creates several risks including stigmatisation, permanence of labels when vulnerability is often temporary, and forcing classifications onto customers who don’t identify that way themselves.

Not everyone who meets vulnerability criteria would describe themselves as vulnerable. Forcing a label onto someone who doesn’t identify with it can damage the relationship rather than help it. There’s also the risk that labels stick in CRM systems long after circumstances change, leading to inappropriate treatment months or years later.

The balance lies in what Christina describes as ensuring “every customer that we touch base with, we have to consider their individual needs and make the best possible decisions that we can.” This suggests a more subtle approach than binary vulnerable/not-vulnerable flags.

When designing your vulnerability identification processes, consider implicit support mechanisms that adapt service delivery based on observed cues without explicit labelling. Build regular review cycles to ensure temporary vulnerability markers don’t become permanent. And always give customers agency in how they’re supported, rather than assuming a label dictates the entire relationship.

Explicit support is when customers declare vulnerability or accept specialist care team assistance. Implicit support is adapting your approach based on detected cues without formal declaration. Both matter, and organisations need strategies for each.

MoneyPlus uses explicit support through their “specialist care team that’s in place for customers who we believe are particularly vulnerable.” But they also recognise implicit support for customers “who might want to communicate with us via digital channels rather than over the phone.” These customers may be experiencing vulnerability but prefer privacy or autonomy.

Implicit support might mean offering additional time, simplifying language, checking understanding more frequently, or providing follow-up documentation. None of these require labelling someone as vulnerable. They’re simply good practice, universally applied when cues suggest they’re needed.

In busy contact centres, vulnerability cues can easily slip past even experienced agents. Christina describes the reality: “In a busy advice call where there’s lots going on, we’re taking a lot of information from the customer, we’re trying to listen to a lot of information and get advice across at the same time.”

Agents are simultaneously processing information, navigating systems, providing advice, and ensuring compliance whilst trying to spot subtle signals that someone is struggling. It’s a significant cognitive load that makes missing cues almost inevitable without support.

This is where AI prompting becomes valuable. Simple nudges like “pause here, check for understanding” at the right moment help agents catch what they might otherwise miss. AI can monitor for hesitation patterns, tone changes, repeated questions, or difficulty understanding, then surface these to the agent in real-time.

Customer comfort with AI varies significantly by context and perceived severity of the situation. Some customers actively prefer AI for certain sensitive topics. Debt conversations can carry shame or embarrassment for some people. Mental health triage, benefit eligibility, or relationship difficulties might all be scenarios where some customers prefer the perceived privacy of AI interaction.

However, other situations typically demand human connection. Crisis situations, complex emotional scenarios, or moments where genuine empathy and validation are needed usually require human support. The same person might be comfortable with AI for routine account queries but want human support when discussing bereavement or serious illness.

The key is choice and transparency. Customers need to know when they’re interacting with AI, and they need easy pathways to escalate to human support when they want it. Christina’s point that “some customers are happy, some find it more difficult” speaks to the importance of respecting individual preferences.

Design your AI interactions with clear escalation pathways. Be transparent about when customers are speaking with AI. Provide prominent “speak to a human” options. And most importantly, don’t assume that because AI can handle a conversation, it should. Test your AI implementations with real customers and gather feedback on comfort levels across different scenarios.

Christina raises a powerful example of why AI is so impactful for vulnerability support, pointing out that people experience financial stress at midnight, relationship difficulties on weekends, and health anxiety in the early morning. Traditional 9-5 contact centre operations miss these critical moments when people are most likely to need support.

AI enables organisations to meet customers where they are, both temporally and emotionally. Whether that’s a chatbot providing debt advice resources at 1 AM or an AI-guided journey helping someone access support services on Sunday, the principle is the same.

Christina’s focus on being “proactive rather than reactive” speaks to another dimension of this. “If we can identify trends, if we can find cohorts of customers experiencing specific types of vulnerabilities, then we can design journeys around that.” Rather than waiting for customers to reach crisis point, organisations can provide support earlier when behavioural signals indicate emerging difficulty.

When supporting vulnerable customers, guardrails aren’t optional. The stakes are too high for unclear boundaries. A miscalculation, a missed escalation, or an inappropriate response can have serious consequences.

Christina emphasises this, saying that “We have to make sure every step we’re challenging ourselves on is what we’re doing delivering the right outcome for the customer.” This means defining clear boundaries for what AI can and cannot handle autonomously. Crisis situations must escalate to humans immediately, including self-harm indicators, suicide references, abuse disclosures, or serious mental health distress.

Similarly, complex financial decisions, vulnerability declarations that trigger regulatory obligations, or situations requiring discretionary judgement all need human oversight. But defining guardrails is just the start. Testing through red team scenarios, edge case exploration, and failure mode analysis helps identify where guardrails might fail before they do so with real customers.

Start by listing scenarios where AI should never act alone. Define escalation triggers with clear, unambiguous criteria. Build in human oversight for high-stakes decisions. Test extensively with vulnerable customer scenarios before going live. And implement continuous monitoring to ensure guardrails remain effective as customer behaviours and vulnerability patterns evolve.

An AI knowledge base is a curated repository of trusted information that AI draws from when responding to customer queries. For vulnerability support, this needs to include approved language, appropriate responses, escalation guidance, and verified resources.

Letting AI pull from the open internet introduces unacceptable risk. Hallucinations, inappropriate suggestions, or incorrect information can cause real harm when vulnerability is involved. Building a knowledge base starts with content inventory. What vulnerability-related information do customers need access to? This might include debt advice resources, mental health signposting, financial difficulty guidance, benefits information, and safeguarding protocols.

Source authority validation is critical. Information should come from trusted sources like regulatory guidance, approved organisational resources, and verified support services. Each piece of content needs review and approval before entering the knowledge base.

Start by auditing what information your agents currently provide to vulnerable customers. Identify trusted sources for each topic area. Create approval workflows for new content. Build regular review cycles to keep information current as regulations change and resources update. And ensure your knowledge base integrates with existing systems so information surfaces at the right moment in agent desktops.

When organisations ask where to start with AI for vulnerability support, the answer is often counter-intuitive. Don’t start with the hardest problem. Start with something that builds confidence, demonstrates value quickly, and gets agents comfortable with AI as an assistant.

Auto Summary fits this perfectly. After a call, AI generates a summary of what was discussed. Agents review it, make amendments if needed, and use it for CRM updates. It’s low risk, high value, and helps agents develop trust in AI capabilities before introducing it into more sensitive areas.

The benefits are immediate. After-call work time reduces, documentation consistency improves, and critically, it generates data for continuous improvement. Are agents amending summaries frequently? That indicates the AI needs refinement. Are amendments decreasing over time? That’s evidence of learning and improvement.

Agent vulnerability is a reality that organisations often overlook. Christina makes this point clearly, stating that agents are “facing similar challenges” to the customers they support, often “dealing with customers in distress while potentially experiencing their own difficulties.”

Vicarious trauma is a recognised occupational hazard in roles involving repeated exposure to others’ distress. Contact centre agents supporting vulnerable customers face bereavement calls, financial crisis conversations, mental health disclosures, and abuse situations. Even with training and support, this emotional toll accumulates over time.

Christina adds that organisations “need to continue to run a business and provide the service, but we have to make sure that every step we’re challenging ourselves on is what we’re doing delivering the right outcome.” That includes outcomes for agents, not just customers.

On-call support is what happens during the interaction. Real-time agent prompting, immediate escalation, and in-the-moment assistance. It’s reactive, time-sensitive, and focused on the current customer conversation.

Off-call support is everything else. Christina describes it as “being able to scale that up and look at trends. If we can identify cohorts of customers experiencing specific vulnerabilities, we can design journeys around that.” It’s analytical, strategic, and focused on prevention and improvement.

Both are essential and AI enables both at scale. On-call, AI monitors conversations for vulnerability cues and prompts agents appropriately. Off-call, AI analyses thousands of interactions to identify patterns, cohorts, and opportunities for journey improvement. The goal is moving from reactive to proactive, spotting early warning signals and reaching out before customers reach crisis point.

When it comes to measuring the success of vulnerability support, Andrew raises the challenge of measuring something so complex and nuanced. Rather than trying to measure vulnerability, which is subjective, context-dependent, and constantly changing, perhaps organisations should focus on measuring empathy, which is a universal skill that benefits every customer interaction.

Trying to measure vulnerability is fraught with challenges. But empathy? That’s a consistent competency that improves every interaction, whether the customer is vulnerable or not. AI can evaluate observable empathy markers like tone, validation, active listening, pacing, and acknowledgement in every conversation.

Training becomes more universal too. Rather than specialised “vulnerability handling” training that can stigmatise certain customers, focus on universal empathy skills. Teach agents to listen actively, validate feelings, check for understanding, and adapt their pace. The outcome is better for everyone, with vulnerable customers getting enhanced support alongside all other customers.

The FCA’s Consumer Duty has transformed compliance requirements around vulnerability. Organisations must demonstrate they’re delivering good outcomes for vulnerable customers, not just following processes. As Andrew notes, there’s “the compelling financial nature of getting it wrong” alongside reputational damage and customer harm.

AI provides the audit trail that regulators expect. Every interaction, every vulnerability indicator, every support intervention can be documented and traceable. Christina emphasises that MoneyPlus produces “consumer duty metrics every month that are discussed right from our board level through our exec and with our teams.”

But data alone isn’t enough. It’s “not just having that data and understanding of that data, it’s what you do with it then.” The product owners need to be mission-orientated, focused on getting the journey right from the moment a customer thinks about contacting you to when they close their plan.

Implement systematic tracking of vulnerability identification, support interventions, and customer outcomes. Create board-level reporting that demonstrates good outcomes, not just process compliance. Build feedback loops that turn insights into action. And maintain transparency with regulators about your AI deployment, showing how tools provide confidence in reporting ability and audit trails. The FCA expects you to identify vulnerability, adapt your approach, document decisions, and evidence good outcomes. AI makes this possible at scale.

Christina is definitive about AI’s role in training, “Take on the role, no. Augment the role, absolutely.” AI won’t replace training teams, but it can dramatically enhance their effectiveness when the challenge is volume and time.

The issue is that keeping up with everything from regulatory changes to customer behaviour shifts would be “more than a full-time job for anyone.” Currently, managers spend significant time on administrative tasks. Andrew frames it as “60% of your time is spent on planning, prep and admin to be able to deliver coaching sessions.” The opportunity is flipping that ratio.

Christina explained that she “would like our managers, the coaches to be informed, to be given suggestions and prompted and guided towards how to have good coaching interventions with that team.” AI can handle the analysis, identify coaching opportunities, and surface insights, letting managers focus on actual coaching conversations.

The strategic dimension matters too. “We’re prioritising what areas you’re focused on and making sure that they’re aligned with your strategy, with consumer duty outcomes.” This ensures coaching effort concentrates in the right places.