AI On: How Financial Services Companies Use Agentic AI to Enhance Productivity, Efficiency and Security

Editor's note: This post is part of the AI On blog series, which explores the latest techniques and real-world applications of agentic AI, chatbots and copilots. The series also highlights the NVIDIA software and hardware powering advanced AI agents, which form the foundation of AI query engines that gather insights and perform tasks to transform everyday experiences and reshape industries.

With advancements in agentic AI, intelligent AI systems are maturing to now facilitate autonomous decision-making across industries, including financial services.

Over the last year, customer service-related use of generative AI, including chatbots and AI assistants, has more than doubled in financial services, rising from 25% to 60%. Organizations are using AI to automate time-intensive tasks like document processing and report generation, driving significant cost savings and operational efficiency.

According to NVIDIA's latest State of AI in Financial Services report, more than 90% of respondents reported a positive impact on their organization's revenue from AI.

AI agents are versatile, capable of adapting to complex tasks that require strict protocols and secure data usage. They can help with an expanding list of use cases, from enabling better investment decisions by automatically identifying portfolio optimization strategies to ensuring regulatory alignment and compliance automation.

Where AI Agents Offer the Most Value in Financial Services To improve market returns and business performance, AI agents are being adopted in various areas that benefit greatly from autonomous decision-making backed by data.

Elevated Customer Service Experiences According to the State of AI in Financial Services report, 60% of respondents said customer experience and engagement was the top use case for generative AI. Businesses using AI have already seen customer experiences improve by 26%.

AI agents can help automate repetitive tasks while providing next steps, such as dispute resolution and know-your-customer updates. This reduces operational costs and helps minimize human errors.

By handling customer inquiries and forms, AI chatbots scale support and ensure 24/7 availability, enhancing customer satisfaction. Employees can focus on higher-level, judgment-based cases, rather than performing case intake, data analysis and documentation.

Advanced Fraud Detection In addition, AI agents are crucial for fraud detection, as they can detect and respond to suspicious transactions automatically. The State of AI report highlighted that out of 20 use cases, cybersecurity experienced the highest growth over the last year, with more than a third of respondents now assessing or investing in AI for cybersecurity.

AI closes the time gap between detection and action, as a lack of action can result in significant financial loss.

To combat fraud, AI agents can monitor transaction patterns in real time, learn from new types of fraud and take immediate action by alerting compliance teams or freezing suspicious accounts - all without the need for human intervention. Plus, teams of AI agents can work with other systems to retrieve additional data, simulate potential fraud scenarios and investigate abnormalities.

Managing Digital Payments and Banking Transactions AI agents make financial management easier, especially for bill payment and cash flow management. Because agentic AI supports machine-to-machine interactions in digital ecosystems, it can ensure regulatory compliance by automatically maintaining detailed audit trails. This reduces compliance costs and processing time, making it easier for financial institutions to operate in complex regulatory environments.

Intelligent Document Processing For capital markets, the most powerful investment insights are often hidden in unstructured text data from everyday document sources such as news articles, blogs and SEC filings. AI agents can accelerate intelligent document processing (IDP) to provide insight and investment recommendations for traders, enabling faster decision-making and reducing the risk of financial losses.

In consumer banking, handling documents like loan records, regulatory filings and transaction records involves a lot of complex data. This amount of data is so large that it can be difficult and time-consuming to process and understand it manually. IDP helps solve this issue, using AI to identify document types, summarize documents, employ retrieval-augmented generation for answers and support, and organize data.

The data-driven insights from multi-agent systems inform strategic business decisions as these systems continuously learn from customer and institutional data using a data flywheel.

Examples of AI Agents in Financial Services Many industry customers and partners have benefited significantly from integrating AI into their workflows.

For example, BlackRock uses Aladdin, a proprietary platform that unifies investment management processes across public and private markets for institutional investors.

With numerous Aladdin applications and thousands of specialized users, the BlackRock team identified an opportunity to use AI to streamline the platform's user experience while fostering connectivity and operational efficiency. Rapidly and securely, BlackRock has bolstered the Aladdin platform with advanced AI through Aladdin Copilot.

Using a federated development model, where different teams can work on AI agents independently while building on a common foundation, BlackRock's central AI team established a standardized communication system and plug-in registry. This allows the firm's developers and data scientists to create and deploy AI agents tailored to their specific areas, improving intelligence and efficiency for clients.

Another example is bunq's generative AI platform, Finn, which offers users a range of features to h

LINK:	https://blogs.nvidia.com/blog/financial-services-agentic-ai/...
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