By Ellie Engley, Account Director – Financial Services at REaD Group

Consumers’ credit standings have been through the mill in the past ten months: furloughing, reduced salaries, redundancies, as well as mortgage and other repayment holidays – and even a rise in fraud scams – have impacted almost everyone.

During these difficult economic times, it can be hard for financial services providers to make sound decisions such as how to communicate with vulnerable customers (or even to identify whether a customer is now vulnerable) or whether to give new financial products to customers. Data is one such tool that can help financial institutions make these kinds of decisions, and help them ensure they are meeting their know your customer (KYC) obligations; demonstrating that they have made an effort to both recognise different levels of vulnerability and to manage the customer accordingly.

Customers at risk

In 2017, the FCA’s Financial Lives survey found that 50% of UK adults are at risk of one or more indicators of being vulnerable, including physical and mental health, life events, financial capability and financial resilience. That means half of all UK consumers may be at increased risk of harm – from making poor decisions or being at greater risk of mis-selling or being excluded from products or services.

In February 2020, the FCA set out its approach to ensuring firms treat vulnerable customers fairly. It highlighted the importance of giving firms greater clarify and explaining what they need to do to “ensure that vulnerable consumers are treated fairly and consistently across financial services sectors.”

They emphasised the shift from a box-ticking exercise to one in which firms step back to ask what their vulnerable customers’ needs are, how they respond to this to deliver good outcomes, and how they can ultimately embed this in their culture.

With the global pandemic and the impact that the resulting financial fallout is having on so many people, being able to identify and deal appropriately with vulnerable customers is more important than ever. Credit profiles are changing rapidly, and financial services providers need to have greater insight into their customers’ financial situations or predicaments.

Identifying vulnerable customers

The first step is to be able to identify and treat vulnerable consumers appropriately and respectfully. However, for most brands the only way to do this is by asking the consumer to self-declare. But with the decrease in physical branch visits and telephone banking becoming less prevalent, the actual number of occasions for the customer to self-declare is very low. In addition, the cost of sending out communications to everyone and the likely low response rates mean this is also difficult to achieve.

With these points in mind, a reputable data partner can help you identify who your vulnerable customers are using vulnerability modelling. A vulnerability model will create a score for every customer, based on a combination of factors including age, income, housing and education, combined with transient states such as health or market forces, and weighed against that customer’s level of susceptibility to these forces.

While this scale does not give an absolute measure of vulnerability of the consumer in every interaction, it does indicate the risk that the consumer may be vulnerable in a given interaction. This score can be built at postcode level and can be applied to any customer or prospect that you know the postcode for. Using a consumer vulnerability scoring system will allow companies to identify areas that have a higher instance of vulnerable consumers and enable them to adapt their practices and policies where they need to.

Data-driven decision making

Once vulnerability or not has been established, financial services providers can then seek out additional data points to support their decision-making. For example, they can support credit data with occupation, lifestyle and location data from a data partner to gain a greater depth of understanding about an individual’s personal circumstances.

This innovative use of data enables ethical, responsible decision-marking, giving financial services the insight to ensure that people do not borrow beyond their means. Modelling an individual’s ability to repay a loan, for example, protects them from debt in the long term. This supporting data model also puts decision-making in the hands of people, not machines, and provides insight to enable informed and sound decision-making around the provision of financial services.

A vast array of financial variables can be also used by financial service providers to select or append to existing records to acquire, retain or re-engage with prospects and customers.

Retail banking: an example

With the ongoing movement towards a more digital banking relationship, many retail banks have embarked on projects to help determine priorities. For example, who are the customers who would be happy to move to fully app-based banking, who are likely to want some relationship in branch, who are likely to want to retain the use of the phone? Many banks have used surveys and questionnaires to help find the answers to these key questions. The big challenge is then how to roll out these results across the rest of the customer base.

By using a combination of internal and external data, including demographics, income, family make-up, health data (much of which is also used to create vulnerability models), as well as savings balance, mortgage take-up and likely income from current accounts, some sophisticated models have been developed to help score each person across a range of outcomes.

While the outcome is not a hard and fast rule – more of an indication of likelihood – it both helps the bank determine the likely scale of movement and therefore where to invest, as well as a priority to target which customers for which service.

Life insurance: an example

For insurance carriers, being able to underwrite risk for life insurance products is not proving easy. COVID-19 is creating a higher than usual risk that is not incorporated into insurance mortality tables. Pricing in the additional risk, collecting additional data and filing changes to get the right product for the customer in the market is the immediate next step for these providers. This will require a shift in how underwriters operate, requiring them to collect data from multiple sources and orchestrating this so it can be used to price risk accordingly.

Insurers will need to examine mortality risk scores to add a new dimension of risk assessment. Using new third party data and existing self-created models with historical data can streamline medical risk assessment and provide non-medical parallels between user behaviour and medical risk. Integrating third party data on demographics, including financial variables, into risk predictions can speed up and simplify the underwriting process for customers while managing risk. Insurers can also look at the data and metadata captured from the whole customer lifecycle and create models in its underlying systems and processes to start incorporating underwriting risk into the new market normal.

As we move towards the new normal, this shift towards responsible decision-making should be at the heart of the financial services sector.

About Author

Ellie Engley is Account Director – Financial Services at REaD Group, a marketing data and insight company that uses its unrivalled data products, insight and expertise to helps its clients in the financial sector get closer to their customers, with market-leading data quality and cleaning solutions and trusted marketing data.

 

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