Introduction to AI in Finance
Large financial firms have spent years testing artificial intelligence in small projects, often limited to data analysis or customer support tools. The next phase appears to involve something more operational: systems that can take action in business workflows.
Manulife’s Approach to AI
Canadian insurer Manulife is moving in that direction as it works to deploy agent-based AI systems inside its internal operations. The company is building these abilities with a runtime platform designed to support agentic AI, the type of system that can carry out tasks in different software tools and datasets.
Benefits of AI in Finance
Manulife said the effort is part of a broader plan to automate high-volume work and assist internal decision making in the business. The insurer has been investing in AI for several years, but the current push focuses on integrating the technology more deeply into day-to-day operations.
For example, an AI agent might collect data from several internal systems and prepare summaries for employees who are reviewing cases or preparing reports. The goal is to reduce the time staff spend gathering information before making a decision.
Challenges of Implementing AI in Finance
Financial institutions face extra hurdles when they try to move AI into production. The sector operates under strict regulatory oversight, which requires strong controls around data use and decision transparency. Systems used for underwriting, risk analysis, or investment decisions must be auditable and explainable.
Governance and Security Controls
Manulife said the platform includes governance and security controls intended to manage how AI agents interact with internal systems. The controls help track how decisions are produced, monitor how data is used, and ensure the systems operate in company policies.
Conclusion and Future of AI in Finance
The case for AI agents lies in their ability to reduce manual work in large administrative operations. Claims processing, policy management, internal reporting, and customer support involve repetitive tasks that require staff to gather data from different sources. AI systems that can collect and organise information in systems may allow employees to focus elsewhere.
As companies push beyond early experiments, the focus is on making technology work inside the everyday systems that run large organisations. If AI agents can deliver reliable results while meeting regulatory expectations, they may become a regular part of financial operations, handling routine work that once required large teams of staff.
FAQs
- What is agentic AI, and how is it used in finance? Agentic AI refers to the type of system that can carry out tasks in different software tools and datasets, and it is used in finance to automate high-volume work and assist internal decision making.
- What are the benefits of using AI in finance? The benefits of using AI in finance include automating high-volume work, assisting internal decision making, and reducing the time staff spend gathering information before making a decision.
- What are the challenges of implementing AI in finance? The challenges of implementing AI in finance include meeting regulatory expectations, ensuring data security, and implementing governance and security controls.
- How can AI agents be used in finance? AI agents can be used in finance to collect data from several internal systems, prepare summaries for employees, and assist with repetitive tasks such as claims processing and policy management.
- What is the future of AI in finance? The future of AI in finance is expected to involve the increased use of AI agents to automate routine work and assist employees, with a focus on making technology work inside the everyday systems that run large organisations.
