CRIF: AI in credit lifecycle requires data and expertise for real impact

Artificial intelligence (AI) is increasingly demonstrating its value across the credit lifecycle, though its impact is not uniform, according to insights shared at the Nordic Fintech Summit 2026 in Helsinki. CRIF hosted a roundtable discussion with financial institutions and industry experts to examine the practical application of AI in credit processes, from customer acquisition to portfolio management and collections.

The consensus was that while AI offers tangible benefits, particularly in customer support and collections, its effectiveness varies significantly across different stages. Participants noted that AI is already yielding strong results in customer interaction and support through conversational tools, enhancing customer experience. However, in customer acquisition, AI use cases remain more limited and harder to scale, underscoring the dependence on context and data availability.

Early successes in areas like marketing automation and customer engagement are providing quick gains. Yet, these benefits tend to plateau, requiring continuous model refinement and a stronger focus on data quality to maintain value. The discussion also highlighted that automation does not always necessitate advanced AI; structured data and rule-based systems can effectively streamline processes like onboarding and verification, proving that the right use of data and processes can be as impactful as complex technology.

Limitations were also identified, particularly in credit risk management and corporate lending, where detecting sophisticated fraud remains a challenge for current AI. Across all applications, there was strong agreement that human oversight remains essential. AI supports decision-making by structuring information and enhancing analysis, but it does not replace human judgment, especially in areas with regulatory and accountability demands. Scaling AI initiatives faces several barriers, including legacy systems, fragmented data, complex governance, skill gaps, and cost considerations.

The focus is shifting from experimentation to execution, with an emphasis on measurable outcomes and integration into real processes. CRIF aims to support financial institutions in this transition, helping them to unlock tangible business value from AI in the credit lifecycle. The key to sustainable impact lies in the right combination of data, governance, technology, and expertise.