Experian Research Reveals How Machine Learning Drives Smarter, Faster Financial Decisions

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● 91% of organisations already using ML have seen an improvement in acceptance rates for vehicle financing since adoption.

● 88% of organisations already using ML have seen an improvement in vehicle finance bad debt rates since adoption.

● 75% of respondents believe that organisations adopting ML in credit underwriting can gain a significant long-term competitive advantage.

● 63% of organisations already using ML plan to significantly increase investment in their ML capabilities over the next 1–3 years.

Kuala Lumpur, 30 September 2025

Experian’s latest research, conducted by Forrester Consulting, reveals how Machine Learning (ML) is transforming decision-making across financial services and telcos in eleven countries in EMEA and Asia Pacific. The findings show that ML is helping organisations improve access to financial services, reduce risk, and accelerate automation, while also highlighting the barriers that still hinder broader adoption.

ML as a Driver of Financial Inclusion and Sustainable Growth

The report shows that ML is enabling organisations to expand access to financial services for underserved segments, particularly thin-file and underbanked consumers. By incorporating richer, alternative data sources, ML models allow for more accurate assessments of eligibility, helping providers make fairer, more inclusive decisions.

According to the research, 70% 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.

At the same time, 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.

Automation, Efficiency, and Cost Saving Are Top ML Benefits

Close to three-quarters (71%) of ML users cite improved risk prediction accuracy and operational efficiency as key benefits. These capabilities enable lenders to confidently increase automation, with more than half (56%) agreeing that ML allows them to automate more credit decisions — reducing manual workloads and speeding up time-to-decision. Looking ahead, almost four in five (83%) respondents believe that in five years’ time, the vast majority of financing decisions will be fully automated.

Generative AI Emerging as a Powerful Productivity Tool in Credit Risk

Generative AI (GenAI) is emerging as a powerful productivity tool, particularly in traditionally time-consuming areas such as model documentation and business intelligence. Close to three-fifths (58%) 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 (61%) agree that GenAI’s biggest advantage lies in streamlining regulatory documentation, enabling faster validation cycles and improving collaboration between risk and compliance teams.

Organisational Resistance to ML Adoption Persists

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 (68%) of non-adopters believe the cost of implementation outweighs the perceived benefits, while 50% admit they don’t fully understand the value ML can bring.

Concerns around explainability and compliance also persist, with 66% of non-adopters worried about model transparency and 60% fearing regulatory misalignment. These challenges are compounded by legacy IT and data infrastructure, which 57% say are 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.

Dawn Lai, Chief Executive Officer of Experian Information Services Malaysia, says:

“In Malaysia, where advancing financial inclusion is a national priority, machine learning (ML) is emerging as a powerful enabler. The latest report highlights that 70% of adopters are already using ML to broaden access to credit, while 69% are simultaneously driving profitability — demonstrating that innovation and inclusion are not mutually exclusive but mutually reinforcing. By leveraging richer datasets and advanced models, lenders in Malaysia are making faster and more accurate lending decisions that fuel sustainable growth for consumers, businesses, and the broader economy.”

Mariana Pinheiro, CEO of Experian EMEA & APAC, adds:

“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.”