Experian study reveals machine learning’s impact on financing decisions

Experian’s recent study, conducted by Forrester Consulting, has examined the influence of Machine Learning (ML) on decision-making processes within the financial services and telecommunications sectors. The research surveyed 1,195 senior decision-makers responsible for AI/ML in credit risk across 11 count

ries, including Australia.109 respondents participated.

The findings indicate that 96% of Australian organisations using ML reported enhanced acceptance rates for SME financing. Additionally, 97% of these organisations observed improvements in credit card bad debt rates. A significant 75% of Australian respondents believe that ML adoption in credit underwriting offers a substantial long-term competitive edge.

Globally, the study found that 88% of organisations using ML experienced improved acceptance rates for SME loans, while 86% saw enhancements in credit card bad debt rates. Furthermore, 73% of global respondents perceive ML adoption in credit underwriting as a significant long-term competitive advantage.

The research highlights that 54% of Australian organisations plan to increase their investment in ML capabilities over the next one to three years. This aligns with the global trend, where 70% of organisations intend to boost their ML investments within the same timeframe.

Machine Learning is increasingly seen as a catalyst for financial inclusion, facilitating access to financial services for underserved segments, such as thin-file and underbanked consumers. Seventy percent of ML adopters agree that ML broadens access to financial services. Moreover, 71% of respondents report that ML enhances profitability by improving risk prediction and reducing bad debt.

The study also reveals that 70% of ML users cite improved risk prediction accuracy and operational efficiency as key benefits. Additionally, 67% agree that ML enables more automated credit decisions, thereby reducing manual workloads and expediting decision times. A notable 79% of respondents believe that most financing decisions will be fully automated within five years.

Generative AI (GenAI) is perceived as a productivity tool, with 73% of respondents believing it reduces the time and effort required to develop credit risk models. Furthermore, 67% agree that GenAI streamlines regulatory documentation and enhances collaboration between risk and compliance teams.

Despite these advancements, barriers to ML adoption persist. These include cost, regulatory uncertainty, and a lack of internal expertise. Sixty-six percent of non-adopters believe the cost of ML implementation outweighs the benefits, while 59% do not fully understand ML’s value. Concerns about model transparency and regulatory alignment continue to be significant hurdles for non-adopters.

Barrett Hasseldine, Head of Data Science at Experian A/NZ, stated, “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.”

Mariana Pinheiro, CEO of Experian EMEA & APAC, commented, “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.”