Experian: How Machine Learning Shapes Funds
The new research highlights how machine learning can improve access, reduce risk and promote sustainable financial growth.
Machine learning (ML) is no longer just a technical upgrade. This is a strategic force to restructure financial services around the world.
Experian's latest research conducted by Forrester Consulting reveals that ML allows for fairer, faster, and more comprehensive decision-making with distinct advantages for both lenders and underserved consumers.
Unlock financial inclusion
According to a survey by Exparian at Global Information Services Company, 64% of ML employers agree that the technology is expanding access to financial services.
By integrating alternative data, the ML model allows lenders to assess their credit score more accurately and comprehensively.
As Mariana PinheiroCEO, Experian Emea & Apac, states that.
Profitability and resilience are closely related
This study highlights the double impact. ML expands financial access, but also increases profitability. 69% of organizations report improved profitability due to improved risk forecasts and reduced bad debts.
In Singapore, this balance is particularly important. Kabir KhannaGeneral Manager, Credit Services, Experian Singapore: «ML is no longer a technical upgrade, but a strategic enabler of sustainable growth, competitiveness and resilience».
Automation drives speed and efficiency
The adoption of ML is also increasing efficiency. Almost 68% of users cite risk forecasting and improved operational efficiency, while 61% confirm that ML enables more automated credit decisions.
Future, four of the five respondents believe that most funding decisions will be fully automated within five years, accelerate time to decisions and reduce manual workloads.
Generation AI joins the toolbox
Generator AI (genai) is quickly appearing as a powerful complement to ML. 72Respondents' payouts believe Genai will reduce the time it takes to develop and deploy new credit models, while 77% emphasize their ability to streamline regulatory documents.
This, according to the report, will help risk and compliance teams work together more effectively and respond quickly to regulatory demands.
There's a barrier left
Despite the promise, the hurdles last. 63% of non-employers complained of cost concerns, while 74% admitted they didn't fully understand the value of ML. Explanability and compliance issues remain, with 60% concerned about model transparency.
Legacy IT infrastructure also poses challenges. Because 70% say the system is not ready for ML deployment. However, the report highlights that many of these fears are caused by misunderstanding, pointing out that modern ML solutions can be explained and compliant.
Turning points in the financial system
As financial institutions embrace ML and Genai, the broader financial inclusion and the potential for sustainable growth will become clearer.
With 94% of organizations already reporting improved acceptance rates for SMEs, 87% are looking at better results for mortgages and consumer loans, Experian's research highlights the pivotal role of ML in shaping the future of finance.
