Massive AI investments Lead to Massive Efficiency Gains for Major Financial Institutions

Artificial intelligence has matured from mere support tool to fully autonomous component of business strategy and financial decision-making–and the investments pumped into AI by financial institutions have begun to pay off in significant efficiencies..

A case in point: JPMorgan already is seeing returns from its enterprisewide investment strategy, which has allocated $18 billion to its annual tech budget, a large portion of which is dedicated to the advancement of its AI capabilities. The strategy, which is projected to deliver up to $2.0 billion in annual business value, has yielded a robust contract intelligence platform, which has saved over 360,000 hours of legal work in one year. And the institution’s asset and wealth management division has contributed to a 20% growth in sales with the help of generative AI tools.

Morgan Stanley has pursued a different albeit equally successful strategy by targeting a specific high-value division to prove the efficacy of its models before scaling. By developing a suite of generative AI tools in partnership with OpenAI and deploying them across its network of 20,000 financial advisors, it has achieved a near-100% adoption rate, saving advisors an average of 30 minutes of administrative work per meeting and contributing to a record $64 billion in net new assets for the firm in the third quarter of 2024.

The application of advanced AI systems isn’t just transforming internal workflows; it’s also changing the customer-facing side of finance, setting new standards for the world of fintech. With the industry rapidly moving on from simple pre-scripted chatbots, we’re seeing the rise of autonomous AI business consultants that can analyze a company or an individual’s financial portfolio and suggest data-driven growth strategies. 

By drawing on real-time data from a multitude of sources, such as transaction histories, IoT devices, and social media activity, AI systems are also becoming hyper-personalized to the point they can make precise predictions and recommendations, thus anticipating needs before users even realize them themselves. Fintech and financial services companies have been quick to adopt such capabilities to improve key operations like credit scoring, customer support, and investment strategy.

These trends represent a shift in the interaction model from one where a user asks AI a question and receives and answer to one where autonomous AI agents proactively manage a customer’s financial life or a business’s operational strategy. However, this increasing reliance on automation comes with some profound implications for liability, ethics, and governance, fueling the growth of a new discipline known as ‘AI governance’, complete with advanced regulatory technology (RegTech) tools to mitigate risk. While, there’s no single ‘correct’ path to AI implementation, the need for dedicated AI governance is becoming clearer by the day.