General Finance
May 8, 2025
Finance Firms Favor AI’s Benefits Over Risks
Leading financial firms like JPMorgan Chase, Capital One, and Moody’s are deploying generative artificial intelligence (AI) in a number of ways across their businesses. For an industry that’s known for conservatism and risk mitigation, this widespread embrace of AI is a sign that the benefits of AI outweigh the potential downsides. This article will explore the risks commonly associated with AI and why these three firms are choosing AI over stasis.
The Risks of Deploying AI
Generative artificial intelligence (AI) is an imperfect technology (just like most new technologies) and is known to create three common risks for companies looking to use it:
Accuracy: AI models have a tendency to create false information (known as “hallucinating”), which often calls the validity of their answers into question.
Safety: Companies deploying AI must ensure that their models do not exude bias or toxicity that could cause harm to the company or its customers.
Transparency: Almost every company prioritizes transparency when leveraging AI, both in the data the AI uses and the outputs it creates.
The financial sector faces additional risks, such as the fact that AI models must create outputs that are compliant with all legal and regulatory requirements. Additionally, because the finance sector deals with sensitive data, models must also be held to the highest security standards.
That said, many financial institutions believe that the potential benefits of AI dwarf these risks.
JPMorgan Chase Projects $2 Billion ROI on AI
JPMorgan Chase was an early adopter of generative AI and has leveraged different variations of the technology for tasks like trading, mitigating fraud, making credit decisions, personalizing the customer experience, and creating operational efficiencies.
The bank recently disclosed that it expects to generate a return on investment (ROI) of $2 billion in 2024 through efficiency and productivity gains, with the bulk of this ROI driven by fraud prevention. Two strategies that the bank has used to adopt AI include:
Rolling out Custom AI Assistants: JPMorgan administered AI assistants to all 140,000 employees to help them save time when drafting emails and reports.
Assigning AI Personnel and Assigning Responsibility: The bank appointed a new chief data and analytics officer and made them responsible for all AI initiatives across the organization. This role reports directly to CEO Jamie Dimon.
Capital One Restructures to Capitalize on AI
Capital One has a long history of innovation, and this culture has extended to the AI revolution. To position itself for success in AI, Capital One restructured its organization to establish key personnel responsible for executing AI strategies, including a head of enterprise AI.
According to Forbes, Capital One committed to “embedding AI throughout its business” with proprietary solutions built into its modern tech stack. Today, the financial services firm has hundreds of AI use cases in production, including:
AI Agent Servicing: This proprietary genAI tool helps agents access information and resolve customer complaints more efficiently.
Customer Personalization: A proprietary AI model helps provide a customized user experience through both mobile and online channels. This has led to a double-digit improvement in personalization relevance compared to the previous model.
Fraud Detection: An AI-powered fraud platform helps proactively surface and mitigate fraud in the time it takes for a customer to swipe their card.
Moody’s Goes All-in on AI
The financial analytics firm Moody’s has gone one step further than most companies by going “all in” on generative AI. This decision was made when CEO Rob Fauber weighed the risks of deploying or avoiding AI:
Risks of Deployment AI: As mentioned earlier, Moody’s was concerned about the common risks associated with deploying AI, which include model hallucinations, potential regulatory issues, and questions about transparency.
Risks of Avoiding AI: After extensive research, Moody’s determined that GenAI could potentially disrupt its analytics division, a business line that brought in $750 million in annual revenue.
To avoid getting disrupted, Moody’s sought to change its company culture into one that was AI-first and took the following steps to achieve that goal:
Incentivizing Employees to Learn About AI: Moody’s launched a generative AI training program and attached a bonus pool that would only trigger if 95% of employees completed it, providing a strong incentive for employees to enroll.
Rapidly Integrating New AI Tools: As soon as new AI tools landed on the market, Moody’s technical team would integrate them onto employees’ computers and encourage them to experiment.
Forming Partnerships to Maintain Security: Moody’s worked closely with leading AI providers to develop orchestration layers on top of foundation models. This allowed the company to experiment with new prompts and build new applications in its own secure environment.
When asked what advice Moody’s would offer to other companies looking to deploy AI, the analytics firm stressed the importance of not “going it alone” and finding a talented AI solutions provider to partner with.
Deploy AI With Gradient: The AI Copilot for Investors
As discussed, financial firms looking to leverage AI face several risks, including compliance, accuracy, safety, transparency, and security. We designed the Gradient Investor Copilot specifically to mitigate these three risks for financial firms. We place an emphasis on:
Compliance: Our platform maintains the highest level of privacy and compliance from start to finish, which includes meeting requirements for SOC 2 Type 2, GDPR, and other common standards.
Leveraging Domain Experience: We keep humans in the loop by allowing customers to integrate their institutional knowledge into their model. This allows our models to handle higher-level tasks that require specific domain expertise.
Reasoning: Our platform uses data reasoning to digest both structured and unstructured datasets quickly in order to execute financial processes while maintaining accuracy.
We hope that you’ve found this article valuable when it comes to learning more about why many major finance firms favor AI’s benefits over its risks.
Interested in learning how a high-performing, cost-effective custom AI system could benefit your business? Contact the Gradient team today to learn more.
FAQ:
1. Why are finance firms willing to accept the risks of AI deployment?
Despite the risks of using AI tools – like hallucinations, bias, and transparency concerns – leading financial institutions are embracing AI because the potential upside far outweighs the risks.
2. How are companies like JPMorgan and Capital One deploying AI while minimizing risk?
Major financial firms are adopting AI while reducing risk by assigning key personnel to oversee initiatives and by partnering with AI solutions providers to develop custom applications in a secure environment.
3. What makes Gradient’s platform different from other AI solutions?
Gradient’s platform is purpose-built for the financial sector, with a focus on compliance, accuracy, and transparency. It allows finance teams to integrate their institutional knowledge into the model, improving outputs and task execution. Gradient also ensures high security and regulatory compliance (e.g., SOC 2 Type 2, GDPR), which is essential for financial firms navigating strict industry regulations.
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