FCA ‘vulnerable customers’ – how transaction data can be used to identity and support them

Vulnerability has been a key focus for the FCA in recent years, with expectations for firms to identify financial vulnerability early, train staff effectively and improve customer outcomes.

One of the biggest lessons I've learned from working in risk and compliance is that managing these expectations in the real world is a constant struggle, and it's even harder when you're dealing with digital finance. 

Consumer Duty: vulnerable customers

When the Consumer Duty came into effect in 2023, it reinforced the importance of proactively identifying customers who may be vulnerable and acting to avoid foreseeable harm to vulnerable customers.

I’ve thought about this a lot. How do financial institutions proactively identify potentially vulnerable customers in the digital environment and, just as importantly, how do they avoid harm when indicators point to vulnerability?

It wasn't until after a chat with Jenny, a Product Manager at Bud, that I realised that part of this problem statement (identifying vulnerable customers) can be solved by using transactional data to help flag potentially vulnerable customers. 

The FCA vulnerable customer definition

The FCA defines a financially vulnerable customer as “someone who, due to their personal circumstances, is especially susceptible to harm, particularly when a firm is not acting with appropriate levels of care.”

It also identifies four main drivers of vulnerability:

• Health
• Life events
• Resilience, and 
• Capability. 

Now that I’m awakened to the power of data insights, I can apply how each of these drivers of vulnerability can be flagged using first party or open banking-permissioned customer transactional data.

Before we dive in, it’s important to note that a view of transactions on one account may not provide the complete picture. For example, a customer’s discretionary spending could all be done with another account, such as a credit card with another institution, and therefore be effectively invisible. At Bud, we provide a steer on this with a ‘profile completeness’ flag or score which considers the activity we see and relationships with other providers. Indicators of financial vulnerability should only be considered where you have a complete view of the customer’s financial data.   

FCA drivers of customer financial vulnerability 

Health

Health is a tricky one. You usually have to rely on customers telling you if they have a condition affecting their financial decisions, which isn’t always easy. In a digital world, where you can’t see or speak to the customer in person, spotting health-related vulnerabilities becomes even more challenging.

But what if you didn't need to rely on the customer telling you something was wrong? What if you could use big data to help tell you that a customer’s circumstances might have changed? For example, data can help to categorise customers who might be potentially vulnerable and then allow firms to take steps to help manage their experiences more appropriately. 

For health, these flags could suggest financial vulnerability: 

• Irregular or missed payments – If a customer who usually pays bills on time suddenly starts missing payments, this could suggest that something is wrong, such as illness, mental health or other struggles. 
• Spending in particular categories – Increased or excessive spending related to the likes of alcohol or gambling could be an indicator of poor health. Accurate categorisation helps flag these indicators early.
• Reduced engagement with finances – If spending patterns become highly unpredictable or show signs of neglect, for example where the customer has adequate funds but isn't paying essential bills on time. 

Life events

The FCA acknowledges that vulnerabilities come in lots of different shapes and forms and can be permanent or transient. 

This is why life events, such as a bereavement or a divorce, are identified as a key driver. People going through difficult times are more susceptible to harm.

Individuals who receive a big, unexpected windfall (such as a lottery win or an inheritance lump sum) might reasonably be considered vulnerable too, especially if they lack financial knowledge or confidence.

We can identify possible life disruptions through transactional data by looking for:

• Drop in income or change in spending behaviour – A sudden reduction in income for example could indicate that the customer has recently lost their job. 
• Spending shifts after bereavement or separation – If a customer suddenly stops paying joint household bills or increases spending on legal services, it may indicate separation or bereavement. Firms can provide relevant financial guidance or payment flexibility in these circumstances.
• Over-reliance on credit or high-cost loans – Categorisation can flag increased spending on short-term credit (e.g. payday loans), signaling a need for financial support before debt spirals out of control.

Resilience

Financial resilience has nothing to do with the amount of money someone has, but rather their ability to withstand and recover from financial shocks.

For example, someone who is living paycheck-to-paycheck might be more resilient to falling into overdraft, for example, than a high earner who might struggle to absorb financial shocks or manage their financial commitments effectively. 

Using transactional data, firms can proactively identify customers who may not be financially resilient by looking for: 

• Lack of discretionary spending – If most transactions fall under essentials (rent, groceries, utilities) with little discretionary spending, the customer may have little financial resilience.‍
• Early warning of financial difficulty – Firms can use categorisation trends to detect when a customer is at risk of persistent overdraft use, credit card reliance, or debt spirals, allowing proactive outreach.

In 2022, the FCA said that 24% of UK adults (approximately 12.9 million people) were considered to have low financial resilience, indicating a lack of capacity to withstand financial shocks. 

Smart use of customer transactional data can help solve this through:

• Simplified insights and alerts – Transaction categorisation allows firms to provide personalised spending breakdowns, helping customers see where their money goes.
• Automatic budgeting and spending limits – AI-driven insights can highlight excessive spending in non-essential categories and suggest budget-friendly alternatives.
• Identifying fraudulent transactions – Customers who struggle with digital banking may be more susceptible to scams. Categorisation helps spot unusual transactions quickly, improving fraud detection.

Capability 

People with lower levels of financial literacy and numeracy may have lower confidence and competence in managing their money and financial matters. In addition, those without literacy and numeracy challenges may still lack the expertise and confidence to navigate financial jargon and decisions.

This can be amplified when individuals are struggling financially. For example, they may prefer to ‘bury their head in the sand’ rather than engage with digital banking where their account balance is revealed.   

Indicators of lower levels of financial capability could include:

• Frequent late fees or overdraft use – Consistently missing due dates or having insufficient funds for recurring payments could suggest difficulty managing bills or understanding financial obligations. 
• ‍Irregular or manual bill payments – A preference for paying bills in cash, at physical locations or making inconsistent payments instead of using automated payments, might indicate a struggle with setting up digital services.‍
• Transaction patterns suggesting avoidance of digital banking – Limited or no digital transactions despite access to a bank account, or frequent large cash withdrawals, could suggest difficulty navigating banking apps or websites. 

One way to help individuals with lower financial capability is to find alternative ways to show financial information. For example, categorised spending could be summarised or presented visually (such as our ‘weekly summary’ feature)  to provide an easily interpretable way of understanding where an individual’s money is going, while a line graph that shows the account balance over time could show a reduction of available cash to spend until income is received.

Similarly, use of generative AI in money management features (such as our Intelligent Search feature) could help consumers with low literacy to engage with their finances using natural language.

How can Bud help with customer vulnerability initiatives?

At Bud, our personal financial management product, Engage, can be used to facilitate customer insights alerts and budgeting features, while our data exploration and insights platform, Drive, can help identify cohorts of potentially vulnerable customers at portfolio level. 

And Intelligent Search, our generative AI transaction search feature, makes it easy for all customers, including those with literacy and numeracy challenges, to better understand their financial position.

Ultimately, it’s up to firms to decide how they act on these insights but, if it were me, I’d be pushing hard for a proactive approach. By doing so, we can drive better outcomes for customers – especially those who need extra support.