Machine learning (ML) has emerged as a key driver for expanding financial access and strengthening risk management across financial services, according to a new study from Experian with Forrester Consulting.
The survey found that 91% of organizations using ML reported improved acceptance rates for vehicle funding, while 88% have seen a decline in bad debt levels. Almost three-quarters (70%) said ML has allowed them to expand access to financial services for underserved groups such as thin files and bank consumers.
Profitability has also improved, with 69% of respondents citing a stronger risk forecast and a decline in debt. Additionally, 71% emphasized the profits of operational efficiency, and over half said ML could increase automation of credit decisions, reduce manual workloads and speed up approvals.
Generated AI is beginning to support sectors, with 61% of respondents looking at the potential to streamline regulatory documents and improve collaboration between risk and compliance teams.
However, adoptions remain uneven. Two-thirds of non-users said costs outweigh profits, and concerns about compliance, model transparency and outdated IT systems continued to hamper wider implementation.
At Malaysia, Dawn Lai, CEO of Experian Information Services, stressed that advances in financial inclusion are a national priority, with “70% of employers already using ML to expand their access to credit, while 69% simultaneously promote profitability.”
Mariana Pinheiro, CEO of Experian EMEA & APAC, added: “Machine learning has disengaged access to financial services for millions of historically excluded. By leveraging alternative data and sophisticated risk models, ML can help lenders make fairer and more accurate decisions.”
