Banks are increasingly adopting mature AI strategies, leading to reduced reliance on single providers like OpenAI. Financial institutions are moving beyond the experimental phase and strategically scaling AI across their enterprises. This shift is driven by the need for measurable performance gains, improved efficiency, better risk management, enhanced customer experience, and revenue growth.

Diversification of AI Providers

A recent analysis by Evident indicates a reduction in OpenAI's dominance in the banking sector. While OpenAI once powered about half of the AI use cases among the world's largest banks, that share has decreased to around one-third by the end of 2025. This diversification is attributed to banks seeking more from the supplier community and alternative AI providers like Anthropic and Google gaining market share. Banks are forming partnerships with various AI startups and tech giants, such as HSBC with Mistral AI, BNY with Google's Gemini, and Goldman Sachs and Wells Fargo with Google Cloud.

Maturing AI Implementations

Banks are now focused on large-scale AI integration and strategic scaling across their enterprises. Financial institutions are moving their AI applications into production, particularly those that deliver the most value. This includes streamlining advisor research in investment banks, sharpening AI implementations for wealth managers, enhancing real-time fraud prevention for retail banking and credit card providers, and alleviating client onboarding challenges for commercial banks.

Key Areas of AI Application

AI is being applied to various critical banking functions:

• Fraud Detection: AI agents are being trained to monitor financial transactions at scale, identify anomalies, and execute corrective actions instantly. Advanced AI fraud systems incorporate multiple detection modalities, including behavioral biometrics, voice pattern recognition, and network analysis.
• AML Compliance: AI is reshaping AML compliance by supporting document verification, biometric checks, sanctions screening, and continuous risk reassessment. It also assists in drafting clearer narratives for suspicious activity reports and improves sanctions screening accuracy.
• Customer Service: AI is at the heart of each customer interaction, anticipating needs, performing repetitive tasks, managing money, and embedding financial decisions in daily routines. AI-driven experiences are designed around guidance and transparency to strengthen customer relationships and reduce friction.
• Risk Management: Wealth managers are using AI to make smarter, more intelligent decisions on risk. Banks are also implementing AI governance as operational infrastructure driven by regulatory expectations.

Challenges and Considerations

Despite the advancements, challenges remain in AI implementation. These include:

• Data Quality: Data quality and availability are critical for effective AI deployment. Models are only as effective as the data used to train them.
• Explainability: Regulatory emphasis is expanding from transparency and documentation to explainability. Financial institutions need to articulate not just what their models do but why they behave that way.
• Skills Gap: A skills gap in AI and financial data science remains a concern. Organizations need to develop capabilities to close this gap.
• Operational Readiness: Success depends less on the technology and more on organizational readiness. Leaders need to prepare their people and culture to adapt to AI.

The Road Ahead

Looking ahead, AI in financial services will become more precise and accountable. It will be applied intentionally to genuinely improve consumers' financial lives. The institutions that succeed will be the ones that apply AI where it can genuinely improve financial wellness. The real impact of AI will be measured by how well it leverages data to improve and strengthen consumers' financial lives. By the end of 2026, AI is expected to move from being a promise to becoming a reliable partner, enhancing human judgment rather than replacing it.

Ultimately, banks are embracing AI to move from reactive to proactive operations, providing true financial guidance to their customers. Institutions that design AI-driven experiences around guidance and transparency will strengthen customer relationships, reduce friction across key journeys, and set new expectations for digital banking.