Machine learning or ML is helping organisations improve access to financial services, reduce risk, and accelerate automation, while also highlighting the barriers that still hinder broader adoption.
According to a report from Experian, 64% of ML adopters agree that the technology enables them to widen access to financial services, responsibly serving new customer segments that traditional scorecards often exclude.
The report is based on a survey of 755 decision-makers from the financial services sector across Denmark, Spain, Italy, Germany, South Africa, Norway, Singapore, New Zealand, Australia, Malaysia and India. All respondents represented enterprises with at least 500 employees.
Findings show that 69% of respondents report that ML improves profitability by enhancing risk prediction and reducing bad debt. This dual impact, expanding access while improving financial outcomes, positions ML as a strategic asset for organisations aiming to grow sustainably.
Close to three-quarters (68%) of ML users cite improved risk prediction accuracy and operational efficiency as the most significant advantages of ML.
These capabilities enable lenders to confidently increase automation, with more than two-thirds (61%) agreeing that ML allows them to automate more credit decisions – which reduces manual workloads and speeds up time-to-decision.
Looking ahead, close to four out of five (78%) of respondents believe that in five years’ time, the vast majority of financing decisions will be fully automated.
Also, generative AI is emerging as a powerful productivity tool, particularly in traditionally time-consuming areas such as model documentation and business intelligence.
Close to three-quarters (72%) of respondents believe that GenAI can significantly reduce the time and effort required to develop and deploy new credit risk decisioning models.
More than two-thirds (77%) agree that GenAI’s biggest advantage lies in streamlining regulatory documentation, enabling faster validation cycles and improving collaboration between risk and compliance teams.
Despite these benefits, some organisations remain cautious. The report reveals that cost, regulatory uncertainty, and lack of internal expertise are the primary barriers to ML adoption.
Two-thirds (63%) of non-adopters believe the cost of implementation outweighs the perceived benefits, while 74% admit they don’t fully understand the value ML can bring.
Concerns around explainability and compliance also persist, with 60% of non-adopters worried about model transparency, and a similar percentage (67%) fearing regulatory misalignment. These challenges are compounded by legacy IT and data infrastructure, which 70% say is not equipped to support ML deployment.
However, the report also notes that many of these concerns stem from misconceptions, modern ML models can be explainable and compliant, and third-party platforms can help bridge skills and infrastructure gaps.
“For underserved consumers, ML is opening new pathways to credit access; for lenders, it is strengthening risk oversight and profitability,” said Kabir Khanna, general manager, of credit services at Experian Singapore.
According to the report, 64% of adopters report expanded access to financial services, while 69% cite improved profitability, evidence that the technology is delivering measurable impact.
“In Singapore, this dual benefit underscores a critical shift: ML is no longer a technical upgrade, but a strategic enabler of sustainable growth, competitiveness, and resilience for financial institutions.” said Khanna.
