Editor's Note: This post is part of AI-on Blog series. Explore the latest techniques and real-world applications for Agent AI, chatbots, and copilots. The series also highlights advanced AI agents powering NVIDIA software and hardware, forming the basis for an AI query engine that gathers insights, transforms everyday experiences, and performs the tasks of rebuilding the industry.
With advances in Agent AI, intelligent AI systems are mature and foster autonomous decision-making across the industry, including financial services.
Last year, customer service-related use of generated AI, including chatbots and AI assistants, has more than doubled in financial services, up from 25% to 60%. Organizations use AI to automate time-intensive tasks such as document processing and report generation, driving significant cost savings and operational efficiency.
According to NVIDIA's latest AI Financial Services report, over 90% of respondents reported positively impacting organizational revenues from AI.
AI agents are versatile and can adapt to complex tasks that require strict protocols and secure data usage. By automatically identifying portfolio optimization strategies, you can help with a broad list of use cases by enabling better investment decisions by ensuring regulatory adjustments and compliance automation.
Where AI agents provide the most value in financial services
To improve market returns and business performance, AI agents are adopted in a variety of areas that benefit greatly from data-backed autonomous decisions.
Improve your customer service experience
According to the AI state of Financial Services Report, 60% of respondents say customer experience and engagement are the biggest use cases for AI generated. Companies using AI are already seeing a 26% increase in customer experience.
AI agents can help you automate repetitive tasks while providing the next steps, such as knowing dispute resolution and customer updates. This reduces operational costs and minimizes human error.
By processing customer inquiries and forms, we ensure AI chatbot scale support and 24/7 availability, increasing customer satisfaction. Employees can focus on high-level judgment-based cases rather than performing case intake, data analysis and documentation.
Advanced fraud detection
Additionally, AI agents are essential for fraud detection as they can automatically detect and respond to suspicious transactions. The AI Report state highlights that out of 20 use cases, cybersecurity experienced the highest growth of last year, with over a third of respondents currently valuing or investing in AI for cybersecurity.
AI closes the time gap between detection and action, as lack of action can result in significant economic losses.
To combat fraud, AI agents can monitor transaction patterns in real time, learn from new types of fraud, and take action immediately by alerting their compliance teams and freezing suspicious accounts. Additionally, teams of AI agents can work with other systems to retrieve additional data, simulate potential fraud scenarios, and investigate anomalies.
Managing digital payments and banking transactions
AI agents facilitate financial management, particularly for bill payments and cash flow management. Agent AI can ensure regulatory compliance by automatically maintaining a detailed audit trail to support machine-to-machine interactions in the digital ecosystem. This reduces compliance costs and processing times, making it easier for financial institutions to operate in complex, regulatory environments.
Intelligent Document Processing
In the case of capital markets, the most powerful investment insights are often hidden in unstructured textual data from everyday document sources such as news articles, blogs, and SEC filings. AI agents can accelerate intelligent document processing (IDP) to provide traders with insights and investment recommendations, enabling faster decisions and reducing the risk of economic losses.
Consumer banks handle documents such as loan records, regulatory applications, and transaction records with many complex data. This amount of data is so large that it is difficult and time-consuming to process and understand manually. IDP helps to solve this problem by using AI to identify document types, summarise documents, employing searched generations for answers and support, and organizing data.
Data-driven insights from multi-agent systems inform strategic business decisions as they continuously learn from customer and institutional data using data flywheels.
Examples of AI agents in financial services
Customers and partners in many industries benefit greatly from integrating AI into their workflows.
BlackRock, for example, uses Aladdin, a unique platform that integrates investment management processes across the public and private markets for institutional investors.
With numerous Aladdin applications and thousands of professional users, the BlackRock team has identified opportunities to use AI to streamline the user experience for the platform while also driving connectivity and operational efficiency. Quickly and safely, BlackRock has strengthened its Aladdin platform with advanced AI through Aladdin's co-pilot.
BlackRock's central AI team established standardized communications systems and plug-in registry using a federated development model that allows different teams to work independently on AI agents, building on a common foundation. This allows enterprise developers and data scientists to create and deploy AI agents tailored to their specific disciplines, increasing client intelligence and efficiency.
Another example is FINN, the generation AI platform for BUNQ. This gives users a variety of features that will help them manage their finances through in-app chatbots. It can answer money questions, provide insight into spending habits, and provide tips for using the BUNQ app. Finn uses advanced AI to improve responses based on feedback, and handles more than 90% of all users' support tickets, beyond in-app chatbots.
Capital One helps customers of Chat Concierge, a multi-agent, conversational AI assistant designed to enhance the car buying experience. Consumers have 24/7 access to agents who provide real-time information and take actions based on user requests. In one conversation, Chat Concierge can perform tasks such as comparing vehicles to help car buyers find their ideal choice and schedule and schedule appointments with the sales team.
Aiden, the latest platform for RBC's global research, uses internal agents to automatically perform analysis when a company targeted by RBC Capital Markets releases SEC filing. Aiden has orchestration agents working with other agents, including SEC filing agents, revenue agents, and real-time news agents.
Designing AI-powered finance agents
The components of a powerful financial services agent include:
- Multimodal and multiquery features: These agents can process and respond to text-image-combined queries, making the search process more versatile and user-friendly. It can also be easily expanded to support other modalities such as voice.
- Integration with large-scale language models: Advanced LLMs, such as the Nvidia Llama Nemotron family, bring in reasoning capabilities to AI assistants, allowing them to engage in natural, human interactions. NVIDIA NIM Microservices provides industry-standard application programming interfaces for easy integration into AI applications, development frameworks and workflows.
- Managing structured and unstructured data: The nvidia nemo retriever microservice allows for the ingestion, embedding and understanding of relevant data sources, ensuring that AI agent responses are relevant, accurate and contextually recognized.
- Integration, Optimization, Automation: The NVIDIA NEMO Agent Toolkit enables the construction, profiling, and optimization of AI agents through data-driven optimization tools that expose unified monitoring, detailed workflow profiling, and bottlenecks, reducing costs and ensuring scalable and reliable agent systems across popular frameworks and custom workflows.
- Guardrails for safe and topic conversation: Nvidia Nemo Guardrails is implemented to ensure that conversations with AI assistants are safe and topical, ultimately protecting brand values and strengthening customer trust.
Learn more about how financial services companies use AI to enhance their services and operations. The status of AI in Financial Services Reports.
