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According to a blog post penned by Scott Matthews, the head architect of analytics at CoreLogic, artificial intelligence (AI) can play an important role in increasing transparency, reducing risk and improving efficiency – especially in the aftermath of the Hayne royal commission, which uncovered weaknesses in corporate governance within the financial services sector.
“AI thrives where there is data – and property and banking are both data rich environments,” he wrote.
Mr Matthews believes the technology can especially add value when incorporated into lenders’ decision-making processes, adding that CoreLogic itself has seen how integrating AI into its automated valuation model (AVM) has simplified the property valuation process.
“It’s vitally important from a risk perspective that the bank gets an accurate property valuation, but this has typically been a human-led, manual and time-consuming process,” the CoreLogic head architect of analytics wrote.
“Previously, the valuer would make predictions based on what it knows to be important – bedroom size, car parking or location.
“Now, we can present a range of attributes to the model and [the AVM] machine learning to make its own decision – independent of human input.”
Further, if the customer is detected to have missed loan repayments, and the AVM suggests that their property has increased in value, the lender would be able to take a more “relaxed” approach to addressing the situation, Mr Matthews noted.
Standard & Poor’s expressed a similar view in its report, titled Australian Structured Finance: Credit Analysis in a Digital Ecosystem, stating that machine learning-powered systems could predict changes in the borrower’s spending patterns and allow lenders to “step in early and customise a solution to borrowers’ financial situations before they miss a payment”.
However, the impact of AI depends on the diversity, quality and volume of data that is fed into it.
“The more data we can get, the more helpful the machine will become and the industry will benefit from greater transparency all round,” the CoreLogic head architect of analytics said.
AI is ‘not corruptible’
According to Mr Matthews, one of the biggest advantages of AI is that it is “not corruptible” and therefore could reduce financial fraud.
“It does the job it’s supposed to do in an unemotional manner. For example, if the machine finds a person does not qualify for a mortgage based on the data provided, then they are ineligible and there is no negotiation,” he wrote.
“It delivers greater transparency in processes and outcomes, and removes any chance for human decision-makers to give leeway that could increase risk.
“AI considers the best interests of the bank and the customer.”
However, as S&P noted in its report, a heavy reliance on the use of AI to make credit decisions, however, could make it difficult for regulators to audit and interrogate decision-making processes in the future.
“This risk needs to be actively managed to ensure digital platforms driven by AI and machine learning don’t become a new type of ‘black box’,” the report states.
Regulators have many times expressed this concern over the last few years. For example, the chair of the Australian Competition and Consumer Commission (ACCC), Rod Sims, warned in 2017 that “data-driven” innovation can increase the risk of machine-powered collusion and decrease competition in the market without necessarily violating any competition laws.
A number of cases have demonstrated that “profit-maximising” machine learning algorithms can work out and sustain the “oligopolistic pricing game” and companies can deny knowing how a machine came to a particular conclusion, Mr Sims noted at the time.
The ACCC chair called for changes to competition law to ensure market-dominating companies cannot avoid liability by saying “my robot did it”, further referring to a 2013 High Court case that centred on the use of Google Adwords to generate misleading search results.
[Related: AI could transform loan servicing: S&P]
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