This blog, written by expert Giles Whittam, follows on from the FCA 2020-21 Business Plan blog published in May. Whilst the FCA has significantly amended its Business Plan in response to the COVID-19 pandemic, the need for fairness in digital finance has been highlighted, particularly with regard to pricing and the treatment of vulnerable customers.
Since this, the FCA has released a number of additional statements[1] further commenting on the need for firms to support the consumers. Before we explore how technology can help financial institutions resolve key customer issues amidst considerable regulatory scrutiny, it is worth exploring the disproportionate financial impact of COVID-19.
Lower-income and gig workers most impacted
Along with the terrible human toll COVID-19 has taken, financial statistics are emerging which are now illuminating the financial impacts of the pandemic. The Standard Life Foundation “Coronavirus Financial Impact Tracker” report is grim reading and it predicts the position will worsen over the next few months.
“In the first three weeks after the UK government introduced the ‘lockdown’, an estimated 7 million households (a quarter of all households in the UK) had lost either a substantial part or all of their earned income as a consequence of the COVID-19 crisis. This included: 2.8 million who were affected by the main or secondary earner being laid off temporarily without pay; 2.25million where either the main or the secondary earner had lost their job or all their self-employed income and become unemployed, and a further 3.1 million where earnings had fallen substantially for other reasons.”
Another example of the disproportionate impact is those with a lower-income who have been impacted more by the requirement to work from home. Many in lower incomes are simply not able to undertake their jobs at home compared to higher-income workers who have been able to transition. One estimate is that of those earning below £20,000, a third of the UK population, are now unable to work whereas the other two-thirds with an income of £50,000 or more can work from home.
The resulting environment leaves the financial services industry with a number of key questions to address:
- How to effectively manage and analyse the large volume of customer-related data points?
- Can additional data sources be utilised to build a more comprehensive picture of the customer?
- How to identify vulnerable customers in an increasingly digital interface?
- Should newer models more suitable for understanding the fluid employment landscape be utilised?
- How to improve the communication and education of customers?
- Are the current customer treatments suitable in the changing employment landscape?
Each consideration presents opportunities for RegTech firms to assist financial institutions in building and/or co-developing solutions.
Automation of the collation, cleansing of large amounts of data
Financial institutions have vast amounts of data, but not always the right information to make correct assessments. There has been much effort undertaken to improve data collation and cleansing activities. However, I would suggest that this has been more in the realm of credit assessments for new lending rather than in the collections and recoveries area, which has been neglected owing to the low levels of arrears during the last decade.
Utilising additional and new data sources
Open Banking offers financial institutions the potential to develop a more comprehensive picture of a customer’s true financial position, thereby helping to assess the customer’s ability to repay the loan. Have they been able to utilise these additional sources easily into the assessment process or is it still a manual process? If so, RegTechs could look to assist with this collation. Additionally, as the percentage of temporary workers has increased, should or could financial institutions utilise the additional data from social media to gain a fuller picture of the customer’s financial position and spending habits.
Better analysis to identify vulnerable customers and act earlier
Firms will need to identify the most vulnerable customers as early as possible and look to assist them quickly to minimise customer detriment. But how easy is it to identify these customers in an increasingly digital-only interface? Fewer customers are visiting the branch, which has historically been the most effective channel to identify those at risk and initiate discussions. This is also exacerbated by the fact that some of the most vulnerable customers may lack access to, or not engage with, digital channels.
Artificial intelligence and machine learning have been developed to a high standard to enable banks to understand critical behavioural traits and potential trends. This means banks can personalise messages to the customer, communicating at the right time, using the most effective channel.
New models for a changing employment market
Historically the most reliable way to predict the future was to review the past and long-standing banking and credit relationships provided banks with a reasonable basis for extending credit. Data such as credit reports and salary history were used by lenders to make predictions and these used three main characteristics in the assessments:
- Identity – to reduce fraud
- Ability to repay – based on income and current debt load
- Willingness to repay – usually based on past credit performance
However, in a changing employment world with an increasing number of temporary/gig workers experiencing significant fluctuations in their income, these methods may be less effective. Some banks are now utilising additional data sources and models to assess customer affordability, including mobile-phone usage patterns and utility-bill payment history to build better risk models.
As much as RegTech firms are able to assist in the areas highlighted above, there are some areas that financial institutions will also need to address to support customers more.
Improved customer engagement and education/assistance programmes
Firms also need to consider innovative education programmes to assist customers in identifying ways to save money and budget better. In terms of tone, collections should be treated as a form of communication and not the imposition of terms. It’s a matter of developing a dialogue and providing solutions that are personalised to suit a customer’s unique story.
Nudges come in all shapes and sizes, playing an integral role in optimising the messaging of digital forms of communication, such as emails and SMS.
Conclusion
There are significant opportunities for RegTech firms to assist regulated firms in embracing technological innovations in collection processes and unlock consumer insights. Tech vendors have the opportunity to:
- assist in the automation of the collation and cleansing of collections data;
- cut through superfluous data by providing solutions which assimilate information and identify customers in distress and those who are struggling; and
- look at new innovative payments solutions to assist the gig economy workers in managing their money.
These have the potential to enhance the relationship with the customer and prevent distress and/or enable earlier intervention in the process. Exploiting consumer insight to provide a holistic platform solution will undoubtedly give financial institutions a competitive edge.
[1] https://www.fca.org.uk/news/press-releases/fca-confirms-further-support-consumer-credit-customers,
https://www.fca.org.uk/news/press-releases/fca-confirms-support-customers-who-are-struggling-pay-their-mortgage-due-coronavirus,
https://www.fca.org.uk/publications/feedback-statements/fs20-9-further-support-consumers-impacted-coronavirus-feedback-draft-guidance-and-rules-personal.
Giles has 25 years’ experience in retail banking, evaluating new technologies in the FinTech arena to deliver safe and secure customer solutions and transform risk and compliance functions. He has worked at a variety of financial institutions and consultancies on large scale transformation projects. Most recently he led the compliance team assisting RBS Ventures developing Mettle, ESME and Tyl.
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