Artificial intelligence (AI) automation platform Rainbird has announced a research partnership with the University of East Anglia (UEA) aimed at supporting vulnerable individuals by facilitating fairer credit decisions.

The proposed tool is being designed to better identify individuals who may need to be classified as vulnerable - those suffering from mental health issues, severely indebted or otherwise lacking the capacity to make fully-informed financial decisions - during the credit application process, ultimately streamlining credit providers’ ability to offer hep and guidance sooner.

The solution being developed will more rigorously analyse potential signals for vulnerability, such as low or erratic income, high indebtedness or low savings to arrive at a more holistic, responsible decision from the credit provider.

Sean Ennis, director of the UEA Norwich Business School’s Centre for Competition Policy, commented: “Although the problem of identifying and supporting vulnerable individuals in finance is relatively well-known, few technology solutions have been developed to specifically tackle it.

“There’s an increasing demand across the board for financial institutions to provide transparency in all decision making processes, and nowhere is this more important than where potential discrimination against vulnerable people is concerned.”

In addition to inaccurate credit decisions, vulnerable individuals experiencing financial issues such as over indebtedness face an uphill struggle due to lacking access to financial planning and educational tools and resources.

Rainbird chief executive James Duez said: “This partnership came from the realisation that not enough financial institutions are taking action to support vulnerable individuals financially: 18 per cent of people with a mental health problem also have a debt problem and 46 per cent of people in financial difficulty also experience mental health difficulties.”

Raphael Markellos, professor of finance and director of research at UEA’s Norwich Business School, commented: “Current models often focus on reducing the risk of misclassifying bad credit as good credit, as this is associated with losses due to non-payment of interest and principal.

"Our modelling efforts are also concerned with avoiding good credit being misclassified as bad credit - this is particularly relevant to vulnerable people and households and represents an opportunity cost of lost interest for lenders”.

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