According to new research from the University of Sydney Business School, people who possess a trait known as “mental accounting” are more likely to be able to repay loans than others.
This means assessments measuring this trait could become a viable option for customers without a credit history.
“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.
“They are unlikely to spend money set aside for loan repayments on entertainment or travel when those accounts are exhausted.”
This trait is more common in people older than 40 and people with higher levels of education.
Mr Gan said this behaviour could be used as a measure of creditworthiness, noting that those with a bias towards mental accounting are also more likely to pay debts earlier.
“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 explained.
The research was informed by a study carried out in Vietnam with fintech firm Rich Data Corporation. The researchers, Mr Gan, associate professor Eliza Wu and PhD candidate Bei Chen, analysed study participants’ Facebook, Twitter and Google Plus accounts to gain an insight into spending priorities.
“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,” Mr Gan said.
Rich Data Corporation’s CEO Ada Guan added that the research could be utilised through the use of behavioural economics, artificial intelligence and alternative data sources to extend credit to people with short or no credit histories.
Ms Guan said the results allow lenders to differentiate credit risk outside of credit data.
“Combining with machine learning, we will be able to build highly predictable credit scoring models for these people,” she said.