3 examples of AI in financial services
When you think of the most technologically innovative organisations in the world, do you think of your bank? You should.
According to McKinsey, the global payments industry handles over 3 trillion transactions per year. Financial services (FS) businesses such as banking, insurance, and asset management companies provide critical infrastructure that must be carefully implemented to run without fault.
FS companies have been using AI for tasks such as fraud detection and credit risk assessment for decades. According to the ServiceNow Enterprise AI Maturity Index 2024, banking has the second-highest average index score (45%) of industries surveyed, reflecting strong performance in AI strategy, governance, and workflow integration.
The majority (83%) of banking Pacesetters—those embracing emerging technologies—are making large AI investments to optimise tech infrastructure, as reported in banking transformation research by ServiceNow and ThoughtLab.
FS leaders are experiencing an evolution of AI use cases powered by technological innovations. Here are three examples of how AI in FS can help protect the business and improve experiences.
1. Addressing fraudulent activity and threats
Fraud detection and anti-money laundering services are some of the longest-standing use cases in AI for FS—where AI is used to “find the signal in the noise” of huge datasets. According to our banking transformation study, nearly half (49%) of the Pacesetters use AI to help with fraud detection.
More sophisticated AI lets businesses scale incident management to consume larger datasets more quickly. FS can get better at preventing fraudulent activity, at speed, with less human intervention—something we’re all thankful for, as it’s time-consuming and stressful when a fraudulent transaction gets through.
AI also helps organisations detect malicious cyber activity, such as distributed denial-of-service (DDoS) attacks. Cloudflare blocked around 21.3 million attacks in 2024—a 53% increase over the previous year. By assessing billions of data points and delivering the potentially threatening ones to a live agent, AI removes the time-consuming element of searching.
Employees can then review AI assessments and results to make decisions based on their experience and emotional intelligence, which the technology lacks. This approach enables organisations to reach their desired outcome far more quickly and cheaply.
2. Accelerating dispute and claims resolution
Banks and insurers can improve customer experience using AI. For example, AI can produce real-time, up-to-date information summaries for claims and card disputes, freeing live agents from having to dig through complicated case details.
Case summaries are presented in an intuitive, conversational format, allowing employees to parse complex details and make informed decisions. This makes end-to-end customer case management more efficient and enables seamless handoffs so that live agents can resolve issues more quickly.
Agentic AI can also be implemented to autonomously respond to customer enquiries, for example, by providing information about available products or advice to help resolve service issues. Live agents have more time to focus on complex and emotionally engaging tasks that people are better equipped to handle than AI.
If somebody phones an insurance company because of an emergency, for instance, this sensitive work requires the empathy of a live agent. FS employees can divide their tasks with AI agents to improve customer experience.
3. Improving employee experience
AI for FS supports back-end threat mitigation and front-end customer experience, but there’s also a large “middle tier” of untapped potential to improve employee experience.
The traditional FS response to data breaches and security incidents makes poor use of staff time: Incident managers are hired for their incident management skills, not to write endless reports. Risk managers are employed to identify, quantify, and mitigate risks, not to re-cut the same story for different audiences when a risk materialises.
GenAI enables technical specialists to get on with their jobs while the administrative back end is managed automatically. For example, the technology can produce the first draft of a report that employees can adjust and submit to regulators. Systemisation allows specialists to respond more quickly to regulatory demands for reporting in FS.
AI is a force multiplier that can help improve both FS customer and employee experiences, creating a well-rounded total experience to benefit the wider business.
Find out how ServiceNow helps put AI to work for financial services.