By Krishna Sai, CTO at SolarWinds
According to the Bank of England (BoE) report Artificial intelligence in UK financial services 2024, three-quarters (75%) of financial institutions are using artificial intelligence (AI) to improve their operations in areas such as data analysis, insights, fraud prevention and cybersecurity and an additional 10% of firms were planning to use it over the next three years.
‘The insurance sector reported the highest percentage of firms currently using AI at 95%, closely followed by international banks at 94%’, said the BoE. ‘Data from financial market infrastructure firms responding to the survey suggest that, at 57%, this is the sector with the lowest percentage of firms currently using AI’.
The report highlighted a long list of use cases, including the optimisation of internal processes and credit underwriting, liquidity management and algorithmic trading.
Another area where the industry is leading is the use of AI agents, which are specialised tools to handle various tasks, such as reviewing contracts or guiding customers through account queries.
Unlike large generative AI models, such as ChatGPT, which are designed to answer open-ended questions across broad domains, AI agents are built for specific purposes within defined environments. This makes them suitable for industries such as financial services, where outcomes must be precise, compliant and explainable.
From cost savings to customer impact
The efficiency gains are substantial. By automating routine and repetitive tasks, AI agents reduce the need for large service teams while increasing the speed and consistency of delivery.
A call centre, for example, can use AI agents to instantly answer common queries, allowing employees to focus on more complex or sensitive cases. The result is lower operational costs and a better customer experience.
Personalisation is another key part of the experience. With instant access to customer history and interaction data, AI agents can tailor their responses and anticipate customer needs, providing a more personalised service. For instance, JP Morgan Chase has deployed a contract intelligence platform that automates the review of thousands of legal agreements, potentially saving thousands of hours of employee time while improving accuracy.
Similarly, Bank of America’s AI assistant, Erica, helps millions of customers manage account security and transactions in real time. Instead of replacing human expertise, these initiatives show how AI agents can complement it, providing faster, more consistent and more proactive service.
In the United Kingdom, NatWest has become one of the early adopters to deploy generative AI through a digital assistant. In March 2025, it announced that it had become the ‘first UK-headquartered bank to work with OpenAI as part of a collaboration that supports its strategic focus on bank-wide simplification’.
The importance of observability
What’s clear from these examples is that AI agents are already paying dividends. However, in financial services, efficiency alone is insufficient. Success depends on whether these systems can be trusted to perform reliably and transparently while conforming to strict regulations.
This is where observability comes in. Observability is the ability to monitor what a system is doing and why. It’s used extensively in IT operations to track the health of applications and infrastructure. In the context of AI, observability means keeping sight of how agents make decisions, spot anomalies quickly and ensure outcomes remain accurate and fair.
For the financial sector, this extends beyond technical monitoring and becomes an integral part of risk management and compliance. For example, if an AI agent declines a loan application, blocks a payment or flags a customer for fraud, the financial firm must be able to see why it made such a decision. Without this transparency, firms expose themselves to regulatory breaches, reputational damage and potential financial loss.
Similarly, with observability, leaders can see when an AI agent is underperforming, allowing them to fix any problems while providing an audit trail that demonstrates compliance. In other words, observability turns promising AI use cases into dependable business tools.
The finance industry is leading the way
Observability gives institutions the confidence to innovate at pace, knowing that automation and personalisation are supported by transparency, governance and control.
In other words, if you’re going to entrust your business to AI, you need a way to ensure it’s doing what it should be doing.
As the BoE research outlined, the financial services sector is already exploring ways to use AI. We should celebrate this creativity and recognise AI is not a short-term investment but something that looks set to deliver long-term value. But to do so effectively, it must be done with the right guardrails in place.
Observability serves as that safeguard, ensuring efficiency gains are matched by transparency, personalisation is delivered responsibly and innovation does not come at the expense of trust.
