AI and ML Transform Credit Underwriting Strategies Today
Artificial intelligence and machine learning are becoming essential tools for companies in finance and telecommunications. A recent study by Forrester Consulting, commissioned by Experian, highlights how these technologies are enhancing decision-making and minimizing risks. Out of nearly 1,200 senior decision-makers surveyed across Europe, the Middle East, Africa, and Asia-Pacific, about 67% reported active use of machine learning in their processes, with another quarter experimenting with it.
The findings reveal that organizations employing machine learning have seen significant improvements in lending acceptance rates. Notably, 88% of small and medium-sized enterprises utilizing these technologies reported better acceptance. Additionally, improvements in bad debt performance were notable, with 86% of credit card issuers acknowledging a reduction in bad debt since integrating machine learning into their systems.
These advancements stem from machine learning’s ability to provide more accurate predictions and analyze diverse data sources to identify at-risk customers more effectively. Around 70% of decision-makers mentioned that machine learning helps with operational efficiency, cost savings, and enhanced risk assessments. This indicates that organizations prioritizing machine learning will likely experience a measurable return on investment and improved customer interactions.
However, integrating machine learning is not without its challenges. A significant barrier, reported by 55% of respondents, is the substantial time and resources required for effective implementation. Additionally, there is a growing need for AI talent, as evidenced by the increasing demand for AI and machine learning professionals in the job market.
Regulatory concerns also loom large in this space. Approximately 75% of those surveyed expressed that compliance issues restrict their company’s capacity to innovate in credit decisioning. Moreover, 70% of decision-makers hesitate to adopt more automated machine learning methods for fear of negative regulatory repercussions.
Looking ahead, 73% of respondents believe that firms that embrace machine learning in credit underwriting will achieve a considerable competitive advantage over time. Their immediate focus for the next few years includes incorporating AI and machine learning into risk-related decisions and streamlining decision-making processes for customers.
Various financial institutions already demonstrate the practical applications of AI and machine learning beyond credit oversight. For instance, HSBC employs AI to monitor nearly 900 million transactions monthly to detect financial crimes, while JPMorgan is heavily investing in AI technology.
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