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How fintech can help banks serve vulnerable customers

06 July, 2021Juno Baker

The Financial Conduct Authority (FCA) requires financial firms to “pay attention to indicators of potential vulnerability” and have policies to deal with vulnerable consumers. But how can data management and artificial intelligence (AI) help?

laptop in the dark with data onscreenThis March, UK bank fraud hit an all-time high – including a 94% increase in impersonation scams, where bank customers lost almost £97m.

The damage is not just financial. The emotional impact is devastating too.

When an elderly person was scammed out of money intended to fund his care, his doctor felt compelled to act.

Dr Dexter Penn has worked in Elderly Care and Neurology since 2010 and is a Clinical Research Fellow at the University College London (UCL) Dementia Research Centre.

He discovered a correlation between the financial transactions of those suffering from neurological conditions and their worsening health. Alzheimer’s, for example, makes it more difficult to recognise numbers, make a good judgements or sensible financial decisions.

Dr Penn realised it was possible to identify when someone had become vulnerable to scamming by monitoring their decision-making patterns and founded Kalgera.

How data management can help protect vulnerable customers

Chryssi Chorafa is Chief Operations Officer (COO) at Kalgera, which is Greek for “good old age”.

“The identification and monitoring of financial vulnerability is a data management problem,” she says.

“Managing financial data effectively can help safeguard the finances of vulnerable people. Kalgera enables banks and financial institutions to identify and protect vulnerable customers from financial harm.”

Banks can use data to continuously monitor customers’ behaviour

including:

  • signs of stress
  • low average balances
  • heavy overdraft usage, and
  • other indicators of vulnerability.

This puts banks in a strong position to take action.

Using data to find solutions

In November 2020, the FCA launched their first digital sandbox with datasets to explore two main areas of vulnerability – individuals and small to medium enterprises. Kalgera got involved as financial experts.

“We were on the FCA sandbox for three months – with a view that we would use the sample and the data to develop further propositions on helping financially vulnerable individuals.”

Kalgera used its own data – as well as the open-source data provided by the FCA – to develop algorithms to help banks:

  • identify financially vulnerable customers
  • monitor their customer base to get a better understanding of the needs of vulnerable customers
  • evaluate how vulnerable they were
  • identify skills gaps in their staff, and then upskill staff so that they can better support vulnerable customers.

“Then, as a business, we said let’s widen the scope beyond those who suffer from Alzheimer’s, Parkinson’s and other neurological conditions.”

Covid-19 and financial vulnerability

Kalgera’s motive for widening the scope was partly because the FCA recognises that consumers have different levels of financial resilience.

But the pandemic had also highlighted factors like stress and depression as people were furloughed or lost their jobs. Not being able to make ends meet causes anxiety, which leads to poor financial decision-making and greater susceptibility to financial abuse.

“In our discussions with banks, one said their customer base had become financially vulnerable overnight,” says Chorafa, adding that Covid brought a rise in scamming.

Kalgera created a framework with different scenarios of financial abuse. Selecting a few, they drew on Dr Penn’s research to develop models. They based their algorithms on these models.

“But when one develops models – especially when we talk about artificial intelligence and machine learning – those models can’t be taken for granted,” says Chorafa.

“As things move on, the demographics and circumstances change. The pandemic’s a great example of that. That’s why it’s so important to keep learning and updating the models, going back and analysing, and reiterating and developing more.”

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