Machine learning boosts credit access & profitability in Australia
New research from Experian, conducted by Forrester Consulting, has highlighted the impact of Machine Learning in transforming credit risk decision-making across Australia's financial services and telecommunications sectors.
The Forrester study surveyed 1,195 senior decision makers responsible for developing and implementing artificial intelligence and machine learning technologies in credit risk, including 109 participants from Australia.
Australian results
The findings for Australia show that 96% of organisations using Machine Learning have seen an improvement in acceptance rates for small and medium enterprise (SME) financing since its implementation. This signals broader access to credit for Australian SMEs, which may help businesses expand, hire staff, and continue operations.
Other Australian insights include 97% of organisations reporting improved credit card bad debt rates following the adoption of Machine Learning. Additionally, 75% of respondents believe that organisations integrating Machine Learning into credit underwriting processes can achieve a significant long-term competitive advantage. More than half of those using Machine Learning (54%) indicated plans to significantly increase their investment in these capabilities within the next one to three years.
Global perspective
On a global scale, the report found that 88% of organisations using Machine Learning observed higher acceptance rates for SME loans, and 86% noted improvements in credit card bad debt rates. The research also revealed that 73% of global respondents see Machine Learning adoption in credit underwriting as providing a significant long-term competitive edge, with 70% planning to boost their investment in the area over the coming years.
The study highlights that Machine Learning enables organisations to improve access to financial services for underserved customer segments, such as thin-file and underbanked consumers. By leveraging richer and alternative data sources, Machine Learning models support more precise assessments of eligibility and foster financial inclusion.
Of those using Machine Learning, 70% agreed the technology enables them to widen access to financial services and support customer groups who may have previously been excluded by traditional assessment methods. Additionally, 71% of respondents reported improved profitability due to better risk prediction and a reduction in bad debt. These factors position Machine Learning as a strategic tool for organisations seeking sustainable growth.
Operational efficiency and automation
Greater operational efficiency and accuracy in risk prediction were cited as major benefits by 70% of users. More than two-thirds (67%) said Machine Learning allows them to automate more credit decisions, thus reducing manual workloads and improving speed. Looking to the future, nearly 79% of participants believe that most financing decisions will be fully automated within five years.
The role of Generative AI was also explored, with 73% of respondents stating that Generative AI can streamline the development and deployment of credit risk models. This technology is proving valuable for tasks such as model documentation and business intelligence. Additionally, 67% agreed that Generative AI can enhance regulatory compliance processes and aid collaboration between risk and compliance teams.
Barriers to adoption
The report identified cost, regulatory uncertainty, and lack of internal expertise as primary barriers to broader Machine Learning adoption. Among non-adopters, 66% viewed the cost of implementation as outweighing the benefits, while 59% indicated uncertainty or limited understanding of the value Machine Learning could provide. Concerns regarding explainability and compliance were also prevalent, with 64% of non-adopters citing worries about model transparency and 62% pointing to fears of regulatory misalignment. Legacy IT infrastructure also poses a challenge, with 59% stating their current systems are not fit for supporting Machine Learning deployment.
"The report highlights that improving profitability is a top priority for business leaders – the ability to enhance decision accuracy and reduce financial risk is key to achieving this. And ML enables that by unlocking richer datasets than were previously possible. This allows lenders to grow responsibly, become more inclusive and support social progress," says Barrett Hasseldine, Head of Data Science, Experian A/NZ.
"Machine Learning is unlocking access to financial services for millions who have historically been excluded from the financial system. By leveraging alternative data and more advanced risk models, ML enables lenders to make fairer, more accurate decisions, especially for consumers with limited financial histories. This technology is becoming central to building more inclusive and sustainable financial systems," says Mariana Pinheiro, CEO, Experian EMEA & APAC.
The Forrester report demonstrates the continued shift towards automation and advanced analytics in credit risk decisioning across Australia and globally, while also outlining the remaining challenges facing organisations considering Machine Learning adoption.
