AI is becoming core financial infrastructure, with all major banks, insurance companies, asset managers, and fintech companies incorporating machine learning into their fundamental operations. In its 2024 Banking Report, McKinsey estimates that AI could add $200 billion to $340 billion a year in value to the global banking industry. JPMorgan employs more than 2,000 AI and machine learning specialists. Goldman Sachs uses AI across trading, risk management, and compliance. BlackRock’s Aladdin platform uses AI to analyze risk for over $21 trillion in assets and generates over $1.5 billion in annual technology revenue.
From tools to infrastructure
The distinction between AI as a tool and AI as infrastructure is important. Tools are optional. Requires infrastructure. Email was a tool in the 1990s. By 2010, it had become infrastructure. AI in financial services is undergoing a similar transition. Fraud detection, credit scoring, customer service, compliance monitoring, and risk management are all increasingly dependent on AI systems operating continuously and at scale.
When Visa processes 200 billion transactions a year and evaluates 500 risk attributes for each transaction in less than 100 milliseconds, AI is not a feature. That’s the system. When Klarna’s AI handles 66% of customer service interactions, AI does not augment human agents. This is the primary service delivery channel. When Upstart’s AI models reduce defaults by 75% and process millions of credit decisions, AI isn’t helping loan officers. It’s an underwriting engine. Growing at a 23% CAGR, fintech revenues are structurally dependent on AI infrastructure.
AI infrastructure stack in finance
There are four layers to financial AI infrastructure. The data layer includes data lakes, real-time streaming systems, and data quality tools from companies like Snowflake, Databricks, and Confluent. The model layer includes machine learning frameworks, training infrastructure, and model management tools from companies such as AWS SageMaker, Google Vertex AI, and Weights & Biases. The deployment layer includes service delivery infrastructure, monitoring systems, and A/B testing tools. The application layer includes fraud detection, credit scoring, chatbots, and analytics.
Each layer requires specialized investment. JPMorgan’s $15 billion technology budget includes spending across all four tiers. Small educational institutions use cloud-based AI platforms to access capabilities without building their own infrastructure. The democratization of AI through cloud services means that institutions of all sizes can adopt machine learning, even though larger institutions maintain an advantage in data volume and model sophistication. More than 30,000 fintech companies operate at various points in this infrastructure stack.
Hiring patterns within the facility
Big banks are building AI centers of excellence. JPMorgan, Bank of America, and HSBC each have dedicated AI research teams developing their own models. JPMorgan’s COiN platform uses AI to review commercial loan agreements, completing tasks in seconds that previously took lawyers 360,000 hours a year. Erica at Bank of America has handled over 1.5 billion customer interactions.
Medium-sized educational institutions are licensing AI capabilities. Rather than building a model from scratch, integrate AI services from specialized providers. FICO provides credit scoring AI. NICE Actimize provides fraud and compliance AI. Personaletics provides customer engagement AI. This model gives community banks and credit unions access to institutional-grade AI at a subscription price.
Fintech companies are AI natives. Companies founded in 2020 and beyond typically build their entire product around AI capabilities from day one. This means there are no legacy systems to integrate or legacy processes to replace. Their cost structure, customer experience, and competitive advantage are fundamentally AI-driven. Fintech companies, which account for 25% of bank revenues, compete primarily on AI capabilities.
Regulatory framework for AI infrastructure
The world’s most comprehensive AI regulation, the EU AI Act, classifies financial AI applications as high risk and requires risk assessment, documentation, and human oversight. US regulators such as the CFPB, OCC, and Federal Reserve have issued guidance on AI fairness, explainability, and model risk management. Singapore’s MAS published the world’s first AI governance framework for financial institutions in 2023.
These regulatory requirements are actually accelerating AI adoption in organizations by providing clear rules. Banks that were hesitant to adopt AI due to regulatory uncertainty now have a framework to follow. Compliance requirements will also create demand for AI governance tools and expertise, expanding the market for AI infrastructure companies.
The trajectory is clear. AI has moved from experimental to operational to financial services infrastructure. The growth of fintech unicorns from 20 to more than 300 companies over the past decade has been made possible by AI. In the next decade, AI will become as fundamental to financial services as databases and networks are today. Institutions that fail to build or access AI infrastructure will be unable to compete on cost, speed, accuracy, and customer experience.
AI banking, AI core system, AI financial infrastructure, financial technology, machine learning finance
