Risk assessments based on behaviour may become a viable alternative for loan customers with little or no credit history, according to researchers at the University of Sydney Business School.
The researchers say that people who possess a behavioural trait known as ‘mental accounting’ are more likely to repay loans than other finance customers. They also say that people over the age of 40 and better educated people are more likely to practice mental accounting.
“Mental accounting is a tendency to separate money into different mental accounts based on the source of the money and the importance of its intended purpose,” said senior lecturer Quan Gan. “For example, people with this behavioural bias might divide their money into mental accounts such as rent, food, entertainment, travel and loan repayments.”
Dr Gan when on to say that people with this tendency are highly unlikely to move money from one mental account to another. “They are unlikely to spend money set aside for loan repayments on entertainment or travel when those accounts are exhausted,” he said.
Dr Gan and fellow researchers, Associate Professor Eliza Wu and PhD candidate Bei Chen, recently studied mental accounting in Vietnam with the assistance Rich Data Corp, a fintech firm specialising in ‘predictive behaviour modelling’.
The researchers monitored the behaviour of study participants via their Facebook, Google Plus and Twitter accounts. “This provided a great deal of insight into their spending priorities and their ability to quarantine money for particular purposes,” said Dr Gan.
“Vietnam is a country with underdeveloped individual credit records and lenders are looking for innovative ways to determine the creditworthiness of a customer from sources of information other than the typical credit scores used in developed countries.”
However, Dr Gan said, that behaviour as a measure of creditworthiness could also work in developed countries like Australia in cases where a loan applicant has no credit history.
Dr Gan went on to say that people who showed a bias towards mental accounting were also likely to clear their debts early. “Given a choice between higher interest rates over a shorter repayment period and lower rates over a longer period, these people will opt to pay more because they don’t like debt,” he said.
Rich Data Corp’s CEO Ada Guan said that her company had supported the research because it was keen to “leverage behaviour economics, alternative data sources and artificial intelligence as a way of extending credit services to people with little or no credit history”.
“The results are encouraging as they provide a new way of differentiating credit risk using non-credit data,” Ms Guan said. “Combining with machine learning, we will be able to build highly predictable credit scoring models for these people.”
The Business School’s research will be presented to the 11th annual meeting of the Academy of Behaviour Finance and Economics (ABF&E) in Chicago in October.
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