On 18th April in Cardiff, we had the pleasure of hearing from some of the UK’s leading academics in personal finance, data sharing and vulnerability at the inaugural Flexys Roundtable dinner.
Researchers and industry practitioners gathered together to discuss how finance and utility firms can build trust and transparency with consumers and articulate the value of sharing their data. Our guests came from the banking, motor finance and utilities sectors to name but a few.
Trust and Transparency
Sharon Collard, Director at the Personal Finance Research Centre, University of Bristol, explained that from the PFRC’s research, the most likely point in the process at which firms can identify vulnerability is when the individuals disclose it themselves.
Therefore, the issue is getting people to engage. In terms of where things currently sit, engagement with customers seems to occur more towards the end of the chain with debt collectors and advisers being informed instead of lenders and utilities companies themselves. According to PFRC researcher, Jamie Evans:
- 44 per cent of respondents in PFRC’s survey have told their bank about their mental health condition
- 38 per cent have told other lenders
- 63 per cent have told a money or debt adviser
Debt collection staff receive on average 15 disclosures per month of a serious physical illness (of the customer or their family), 12 disclosures of a mental health problem and 9 bereavement disclosures
Collaboration and Culture
One difficulty Sharon raised with disclosure was the necessity of multiple vulnerability disclosures for many customers. People who are vulnerable, suffering from physical or mental health problems or going through a temporary, difficult period in their lives, find it incredibly challenging to repeatedly share information about their circumstances. The emotional cost of this repetition is high.
What is clear is that the infrastructure we have to deal with vulnerability in the industry needs to change. A centralised database similar to the Ofgem Priority Services Register, which enables vulnerability data sharing between energy companies, could be the answer.
Ethics and Infrastructure
When building trust with the consumer, the critical part is maintaining an ethical approach, said Bola Karimu, BA Accounting and Finance Programme Manager at the University of the West of England. If a centralised industry database were to be founded, there would need to be procedures to ensure companies sign an ethical agreement to agree not to misuse data, which would help build customer trust.
Sharing data around a vulnerable situation is a great starting point for an outcome-focused conversation between the customer and the firm. In my opinion, it’s about reducing the friction in the process and making it easier for someone to engage.
The infrastructure needs to be in place within the industry’s IT systems (lenders, finance providers, utilities companies, collectors and debt advisers) to communicate the channels of communication a customer requires. You may be able to pass on instructions that a customer only responds to communications via email, rather than a phone call, for instance, and make the collection experience less stressful for them.
A valid point was raised during our discussions that, even internally, vulnerability data sharing might not be streamlined enough. Different departments in a bank may not even share vulnerability data effectively enough yet with each other – people in the savings department may not know their same customer has declared details of their vulnerability to the current account department, for instance.
Technology and Machine Learning
Technology can assist with vulnerability detection. As Bola mentioned, using artificial intelligence and machine learning to predict vulnerability is tough because you’re removing the human angle which is such a key part of the interaction. Equally, though, he advised that if we can identify potential vulnerability characteristics earlier in the customer communication process using technology like machine learning, then we can intervene faster to help them.
By using machine learning technology, firms can track the fluctuations of a customer’s potential vulnerability, said Jon. Just because a machine learning algorithm predicts a customer is vulnerable, it doesn’t mean they necessarily are. But – once their vulnerability likelihood reaches a certain threshold, an agent can be automatically prompted to have a conversation with the individual, bringing back the human involvement at the right time.
There is considerable progress to be made on identifying and supporting vulnerable individuals.
Rather than pursuing a prescriptive approach, we agreed that the path academia and industry executives need to take now is collaboration – learning to engage and collect data in the right way, and use and share it correctly, taking support where appropriate from regulatory guidance and technology like machine learning.
Jon Hickman, CEO & Co-Founder, Flexys Solutions