Noah Gross — Scale Venture Partners - Where’s my AI banker? Why financial services is lagging on AI adoption - July 2025
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About Noah Gross — Scale Venture Partners - Where’s my AI banker? Why financial services is lagging on AI adoption - July 2025
An analysis by Noah Gross of Scale Venture Partners exploring why financial services has not yet fully adopted AI, despite being data-rich and process-heavy. The article identifies key barriers such as the culture and compliance trap, high bar for explainability, data quality issues, and low risk tolerance, while also highlighting promising use cases and momentum from startups.
Key Features
Analysis of AI adoption barriers in financial services
Identifies the culture and compliance trap
Discusses human-in-the-loop approaches
Covers need for explainable and auditable AI
Highlights high-impact use cases for LLMs in banking
Provides perspective from VC and industry leaders
Pros & Cons
Pros
- Provides nuanced, real-world perspective on AI adoption
- Based on discussions with top bank and fintech leaders
- Identifies specific, actionable barriers and potential solutions
- Offers balanced view with both challenges and optimism
Cons
- Does not provide a specific AI tool or implementation guide
- Focuses on problems rather than offering technical solutions
- May be too high-level for practitioners seeking tactical advice
Best For
Understanding AI adoption challenges in bankingIdentifying regulatory and trust barriers for AI in financeExploring human-in-the-loop AI for high-volume, low-risk tasksGetting insights on LLM application in loan underwriting and fraud detection
FAQ
Why is AI adoption in financial services lagging?
Banks already have mature pre-LLM ML systems, so the bar for improvement is high. Additionally, they need deterministic, explainable systems that never hallucinate, operate within regulatory constraints, and integrate with legacy infrastructure. Data quality, low risk tolerance, and trust issues also contribute.
What is the way forward for AI in financial services?
Approaches include human-in-the-loop systems where AI handles high-volume, low-risk tasks (like triage and summarization) with human oversight, and developing frameworks that make AI output more explainable, reproducible, and auditable by design.
What is the culture and compliance trap?
There is a double standard: humans are expected to be flawed but machines are expected to be flawless. In financial services, regulators are more comfortable with human errors than AI errors, creating a chilling effect on deployments and slowing organizational learning.