New Experian Study Reveals Critical Role of AI in Lending and Key Drivers of Accelerated Adoption by Financial Institutions

Experian’s new “Perceptions of AI” study finds that most major financial institutions now view AI as critical to their lending strategy, with usage rapidly expanding across the entire lending lifecycle. The research highlights both the expected benefits (efficiency, accuracy, risk management) and the main obstacles (regulation and data quality) driving how and how fast lenders adopt AI.​

Core findings

• 84% of surveyed financial‑institution decision‑makers say AI is a critical or high‑priority part of their business strategy over the next two years.​

• 89% say AI will play a critical role across the full lending lifecycle, from application and underwriting to fraud prevention and portfolio management.​

• Respondents expect AI to deliver three main outcomes: increased operational efficiency, more accurate credit decisioning and stronger risk mitigation.​

Key drivers of adoption

• Competitive and performance pressure: Institutions want faster decisions, lower costs and better risk prediction in a more complex credit environment.​

• End‑to‑end use cases: AI is being embedded into origination, credit scoring, collections, fraud detection and portfolio monitoring rather than used as a narrow point solution.​

• Measurable impact: Many lenders are already seeing improvements in speed and accuracy as AI models move from experimentation into everyday decisioning.​

Main obstacles and risks

• Regulatory uncertainty: 73% of respondents are concerned about the evolving regulatory environment around AI in lending and credit decisions.​

• Data readiness: 65% say having AI‑ready data is one of their biggest challenges, with data quality named the single most important factor in trusting an AI vendor.​

• Explainability expectations: The study stresses the need for explainable, transparent AI that avoids “black box” behaviour and supports compliance and customer trust.​

Implications for lenders

• Strengthen data foundations: Prioritise high‑quality, well‑governed, “AI‑ready” data, since it is viewed as the cornerstone of trustworthy AI outcomes.​

• Build explainable AI: Focus on models and platforms that can clearly justify decisions to internal risk teams, regulators and consumers.​

• Partner selection: When choosing AI vendors, lenders place the greatest weight on data quality, regulatory expertise and advanced analytics capabilities.​

If you share your role (e.g., credit risk, product, compliance), a more tailored summary of what this study means for your organisation can be provided.