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Nevan McBride

Risk Practice Director

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News & Views / Consumer Duty: How data and modelling can detect and prevent consumer vulnerability
21 March 2023

How data and modelling can detect and prevent consumer vulnerability


The final rules and guidance for a new Consumer Duty, published by the FCA in a policy statement in July 2022, set higher and clearer standards of consumer protection across financial services, and requires firms to put their customers’ needs first. The rules require firms to consider the needs, characteristics and objectives of their customers – including those with characteristics of vulnerability. If firms can understand who, within their customer base, is vulnerable then appropriate actions can be applied to protect them against further harm.

  • A vulnerable customer is defined as someone who is suffering from physical or mental health issues, has experienced a negative life event, has low financial resilience or impaired financial capability.
  • The FCA’s Financial Lives 2022 Survey revealed that 47% of adults showed signs of vulnerability, with over half of those classed as having low financial resilience exacerbated by the recent cost of living crisis.

These stark statistics highlight the extent of vulnerability in the UK with the vast majority of firms’ customer base expected to be impacted by this to some degree. Understanding vulnerability isn’t a new regulatory requirement within the industry, it was initially set out in the FCA’s Treating Customers Fairly guidelines. The Consumer Duty, however, reinforces the vulnerability requirements and sets out clearer and more prescriptive expectations. Firms must respond by ramping up their policies and practices, this should include more robust and considered vulnerability detection and prevention measures.

Firms should look at means of identifying vulnerability via their contact points, including introducing self-declaration forms on digital channels and sensitive questioning from specially trained customer services agents on telephony channels. Some technology even exists to detect vulnerability via key phrase detection and speech patterns. These measures will help highlight vulnerable customers together with their vulnerability type, although not all will be able or willing to disclose this highly sensitive and personal information.

Recent studies have revealed that up to half of consumers are uncomfortable revealing their vulnerability to their financial provider. Firms can bridge this gap with proactive strategies to identify undisclosed vulnerabilities. Firms could monitor the data they hold on their customers to detect signs of vulnerability such as erratic spending behaviours, sudden declines in income, a surge of credit searches or new credit facilities and evidence of macroeconomic indicators such as inflationary or interest rate rises squeezing customer’s disposable incomes.

The drivers of vulnerability, such as those examples cited above, can be brought together in a model, built using machine learning techniques, to make vulnerability detection more efficient, accurate and consistent. The model output, typically a score or likelihood, can be used to segment your customer base by risk of vulnerability to enable appropriate monitoring strategies and proportional actions to be applied. This can also provide a general barometer of financial vulnerability in your portfolio in the same way a lender would monitor its credit, fraud and other risks.

Financial distress can be both a cause and a consequence for a vulnerability event. A loss in income can have a detrimental effect on your mental health or lead to a relationship breakdown. Financial distress can be an outcome from having a low financial resilience, a negative life event, health issues or low financial capacity. So, modelling financial distress will be pivotal in both the detection and even the prevention of vulnerability. This will be key to meeting the Duty’s guidelines as the FCA places particular focus on “preventing harm before it arises”.

Lenders will be well versed in examining their customer data for signs of financial distress as this can signal a future loss event. Credit risk models, such as customer management or pre-delinquency scorecards, which incorporate indicators of financial distress, are utilised to highlight customers most at risk of default to enable mitigating actions to be deployed such as granting payment plans or managing down lending limits. So, a model using financial distress indicators can serve to identify vulnerability whilst also mitigating bad debt. Firms may want to explore building a distinct vulnerability model (with a vulnerable flag as the target variable) to have alongside their credit risk models (typically have delinquency or default as the target variable). The outputs of these models are likely to be highly correlated, especially if the input variables are restricted to the financial and economic data used to detect the shared leading indicator for both objectives: financial distress.

Identifying vulnerability within your customer base is a key facet of the new Consumer Duty principles. Without this critical information, improper actions may inadvertently be taken, which could cause further detriment and harm to the customer. Vulnerable customers can be identified using discreet and delicate communications although this should be supplemented with a predictive model to detect financial distress: one of the leading indicators of vulnerability. A model of this kind will not be unfamiliar to a lender given its use in managing bad debt, but it can also serve to identify and even prevent vulnerability within your customer base.

If you need help identifying, detecting and preventing vulnerability ahead of the Consumer Duty implementation deadline, contact me here.