Kuala Lumpur, 9 October 2025 – Experian’s latest research, conducted by Forrester Consulting, reveals how Machine Learning (ML) is transforming decision-making across financial services and telecommunications sectors in eleven countries across EMEA and Asia Pacific. The study shows that ML is helping organisations expand financial access, enhance profitability, and drive automation—while highlighting the challenges that still slow adoption.
Driving Financial Inclusion and Sustainable Growth
The report finds that ML is enabling providers to extend financial access to underserved and underbanked consumers. By integrating alternative data sources, ML models deliver fairer and more accurate credit assessments.
According to the findings, 70% of ML adopters say the technology helps them widen access to financial services, responsibly serving new customer segments that traditional credit models often exclude. At the same time, 69% report improved profitability due to enhanced risk prediction and reduced bad debt, making ML a strategic tool for both inclusion and sustainable growth.
Boosting Efficiency Through Automation
Nearly three-quarters (71%) of ML users cite improved risk accuracy and operational efficiency as top benefits. Over half (56%) say ML enables them to automate more credit decisions, reducing manual workloads and accelerating time-to-decision. Looking ahead, 83% believe most financing decisions will be fully automated within five years.
Generative AI Enhancing Productivity
Generative AI (GenAI) is also emerging as a strong productivity driver. 58% of respondents believe GenAI can significantly cut the time needed to develop and deploy credit risk models, while 61% say its biggest advantage lies in streamlining regulatory documentation, speeding up validation and collaboration across risk and compliance teams.
Barriers to Adoption Remain
Despite clear benefits, barriers persist. 68% of non-adopters believe ML implementation costs outweigh perceived benefits, and 50% admit they do not fully understand the technology’s potential. Concerns over model transparency (66%) and regulatory alignment (60%) also remain, compounded by legacy IT infrastructure.
However, the report notes that many of these issues stem from misconceptions, as modern ML models can be both explainable and compliant, with third-party platforms helping to bridge capability gaps.
Malaysia’s Push for Financial Inclusion
“In Malaysia, where advancing financial inclusion is a national priority, machine learning is emerging as a powerful enabler,” said Dawn Lai, Chief Executive Officer of Experian Information Services Malaysia. “The latest report shows that 70% of adopters are already using ML to broaden access to credit while also driving profitability, demonstrating that innovation and inclusion go hand in hand.”
Mariana Pinheiro, CEO, Experian EMEA & APAC, added: “Machine learning is unlocking access to financial services for millions who have historically been excluded. By leveraging alternative data and advanced risk models, ML enables fairer, faster, and more accurate lending decisions.”
