Transforming Paradigms

The report finds that AI is rapidly becoming a core business driver in financial services, with 77% of respondents expecting high importance within two years. While FinTechs lead in creating new AI-based products, incumbents primarily use AI to enhance existing services. Key hurdles include data quality, access to talent, and regulatory uncertainty. The study also highlights a 'perception gap' where firms see AI as a net risk mitigant at the firm level but a potential driver of systemic risk at the market level.

• AI is expected to become an essential business driver in the short term, with 77% of respondents anticipating it will have high or very high importance to their businesses within two years. While FinTechs currently perceive higher strategic relevance, incumbents aim to close this gap within the same timeframe.
• Risk management is currently the most implemented AI domain at 56%, but firms expect the generation of new revenue potential through new products and processes to become the most important usage area within two years, with 95% expecting to harness these capabilities.
• There is a widening technological and financial gap between AI leaders and laggards. Pay-offs from AI investments appear to accelerate once R&D expenditure reaches a 'critical mass' of approximately 10%.
• FinTechs and incumbents employ different AI strategies: FinTechs more frequently create new AI-based products and sell AI as a service, while incumbents focus on improving existing products. Consequently, 30% of FinTechs report a significant AI-induced increase in profitability, compared to 7% of incumbents.
• AI is projected to have divergent impacts on employment by 2030: incumbents expect a net reduction of approximately 9% of jobs (estimated at 336,000 jobs), whereas FinTechs anticipate a workforce expansion of 19% (estimated at 37,700 jobs). Investment Management is expected to see the highest reductions, with a projected 24% decrease within 10 years.
• Data quality and access to talent are the primary obstacles to AI implementation, cited as hurdles by over 80% of respondents. Access to talent is particularly critical for AI Leaders, who often require more specialized research capabilities for complex use cases.
• There is a disconnect between firm-level and market-level risk perceptions. While firms generally see current AI implementation as a modest contributor to risk or even a mitigant, 48% to 58% believe mass AI adoption will exacerbate market-wide risks, specifically systemic data breaches and entrenched algorithmic bias.
• Regulation is perceived inconsistently across jurisdictions. Chinese firms are more likely to view their regulatory framework as a facilitator (53%) compared to firms in the US, UK, and mainland Europe, potentially due to historically less demanding data protection rules.
• Alternative datasets are widely used to generate novel insights, with 60% of respondents utilizing them. Social media is the most common alternative source (55%), particularly within Investment Management and Deposits and Lending sectors.
• In credit analytics, users expect AI to reduce credit defaults by an average of 10% in the short term. However, 47% of respondents believe AI will exacerbate existing biases in credit decision-making, with psychometric testing (75%) and social media (64%) viewed as the most bias-prone data sources.

APA

World Economic Forum (2020). Transforming Paradigms. http://www3.weforum.org/docs/WEF_AI_in_Financial_Services_Survey.pdf

Chicago

World Economic Forum. Transforming Paradigms. 2020. http://www3.weforum.org/docs/WEF_AI_in_Financial_Services_Survey.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Transforming Paradigms |date=January 2020 |url=http://www3.weforum.org/docs/WEF_AI_in_Financial_Services_Survey.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2020transforming, author = {{World Economic Forum}}, title = {{Transforming Paradigms}}, institution = {World Economic Forum}, year = {2020}, month = jan, url = {http://www3.weforum.org/docs/WEF_AI_in_Financial_Services_Survey.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }