• TD Bank targeting $1 billion in annual value from artificial intelligence initiatives
• Insurance claims costs expected to fall by $150 million through AI driven efficiencies
• Fraud detection and claims resolution processes being accelerated through digital tools
• Generative AI assistant deployed across contact centers and more than 1,000 Canadian branches
• Machine learning models strengthening transaction monitoring and financial crime risk assessment
• Industry analysts estimate AI could reduce banking sector costs by up to 20 percent

TD Bank is ramping up its artificial intelligence strategy as executives seek to generate $1 billion in annual value from the technology while cutting operational costs and accelerating decision making across the organization.

Speaking during the bank’s latest earnings call, chief executive Raymond Chun outlined a strategy built around scalable AI deployment.

A central principle, he said, is to “build once and use many times,” allowing the bank to replicate AI capabilities across different business lines and reduce the cost and complexity of rolling out new systems.

The approach reflects a broader push across the banking sector to embed artificial intelligence deeper into daily operations.

Financial institutions increasingly see the technology as a key lever for improving productivity, reducing risk, and enhancing customer experience.

At TD, early results suggest the investment is beginning to deliver measurable returns. The bank reported that its AI initiatives generated approximately $170 million in value during 2025, and executives believe that figure will rise sharply as projects expand across the enterprise.

Chief financial officer Kelvin Tran said the bank expects artificial intelligence and related digital investments to deliver significant efficiency gains in the insurance business.

TD anticipates reducing insurance claims costs by $150 million over the medium term through a combination of AI driven fraud detection, vendor optimization, and process redesign.

The technology is also expected to accelerate operational workflows. Tran said AI systems will shorten fraud detection timelines while improving both the speed and accuracy of claims resolution.

In wealth management, the bank expects AI tools to cut the time required to produce financial plans by roughly half.

TD has already introduced several AI applications that are reshaping frontline operations. One of the most visible is a generative AI virtual assistant deployed across the bank’s contact centers.

The system helps employees respond to customer queries by rapidly retrieving information and suggesting answers, reducing client hold times and enabling staff to resolve issues more quickly.

After initial success in call centers, the same knowledge management platform has now been rolled out across more than 1,000 TD branches in Canada.

Chun said the technology allows staff to obtain answers in seconds to questions that previously required navigating multiple systems and internal resources.

The bank is now extending its approach to more advanced forms of automation. Chun said TD is experimenting with agent based AI systems designed to streamline complex internal processes.

One project currently being scaled focuses on simplifying the pre adjudication process for real estate secured lending.

Beyond generative AI, TD is continuing to invest in traditional machine learning systems aimed at strengthening risk management and financial crime controls.

Leo Salom, the bank’s chief executive for the United States, said the organization implemented new machine learning models within its transaction monitoring systems last year.

Additional models are expected to be deployed in the coming quarters as part of a broader modernization of the bank’s anti money laundering and financial crime detection capabilities.

Salom said the new tools have enabled TD to introduce a more data driven methodology for evaluating financial crime risk, producing what he described as a more sophisticated view of potential threats.

The bank’s investments reflect a wider industry shift. Analysts at McKinsey estimate that artificial intelligence could reduce banking sector costs by as much as 20 percent, with agent based systems expected to play an especially significant role in reshaping operational processes.

For TD, executives believe the ability to scale AI quickly across multiple parts of the organization will determine how much value the technology ultimately generates.

Chun argued that building reusable systems and repeatable deployment patterns will allow the bank to expand AI capabilities rapidly while maintaining tight control over costs.

If successful, the strategy could transform artificial intelligence from an experimental tool into a central engine of operational efficiency across the bank’s global business.