How Are Insurance Companies Using Artificial Intelligence?

In an insurance market this competitive, how do you raise your company's profitability? The answer is hiding in your own data.
Insurers have historically been slow to adopt new technology. Now, as disruptive insurtech startups fight incumbents for market share, the smartest companies are using artificial intelligence to pull ahead.

• How are insurance companies using artificial intelligence?
• How can AI and data analytics move your KPIs?
• How do you use AI to capture the highest return?
• How do you build a proof of concept (POC) in two to four weeks?

In this article we share the talking points, tips and practical advice that will help you get the most out of AI inside your company.

1 – The most common AI use cases in insurance

Implemented properly, AI has the potential to cut costs, raise efficiency and deliver stronger margins. So how do leading insurers actually use it?
Fraud detection and claims automation come first.

Using AI for fraud detection is a well understood use case in insurance. It lets you set concrete return-on-investment targets and key performance indicators, because every fraudulent attempt you catch is money you no longer spend on illegitimate claims.
Alongside it, claims automation has opened a newer field of opportunity. One of the most pressing questions on the agenda is how much efficiency insurers can unlock by automating significant parts of the loss assessment process.

That same efficiency potential shows up in lead scoring, where a model surfaces how ready a customer is to buy from you.

This is how you maximise the efficiency and productivity of your sales team, because the system lets them focus on the most promising customers. Your marketing team can use the same insights to improve product personalisation and raise return on investment by concentrating on the customers, or the segments, most likely to convert.

The real goal is reaching the right customers at the right moment and making sure they receive the right advice. Mapping communication types against content is critical here, because it helps companies understand which customers care about which kind of content.

2 – Winning stakeholder support

Insurance lags other sectors in digital transformation. At the same time, many insurers know they are sitting on a large volume of valuable data whose worth has never been measured and which needs AI to unlock.

3 – Finding the hidden gems

Finding the hidden gems inside your data starts with the right perspective. Companies typically turn to AI at the very beginning, point it at their data and try to see what it can produce.
Start the other way around. First define the business problem you want to solve, then work out which data you need to solve it. Following that path also shows you whether you already hold the data or whether you need to collect it.
The next step is validation: determining how feasible each use case is and what impact it would have on your company. This is how you eliminate scenarios with low feasibility or low contribution. What remains are the two or three cases that genuinely deserve your focus.
Data can give you almost anything, but to extract maximum value you first need a problem worth solving.

4 – Comparing the value of use cases

Once you have identified your candidate use cases, run a benchmark before you decide where to invest.
The aim is to build use cases that can be compared on a normalised business impact. That way a use case designed to optimise a registration funnel can be set against one designed to raise the conversion rate in the sales funnel.
Positioning every use case on the same scale stops you comparing apples with pears, and it makes your recommendations far more convincing in the eyes of your stakeholders.

5 – Building an effective proof of concept

When you build a POC for an application, we recommend the following.
Connect the dots on how the machine learning system should be measured so that it delivers added value. In a machine learning case, name the KPIs you want to optimise, then wire those performance indicators into the system itself.
Set the return on investment you expect as a reference point. If the project has profit potential, you can move forward iteratively and manage it like a typical agile software development process, improving one KPI at a time.
Without a change management plan that already works, even the best designed machine learning and AI business models can fail. You may produce excellent results, but if your customer does not understand what action to take or how to use the solution, the problems come back.

6 – Recommendations for the insurance sector

Focus on the processes full of small repetitive tasks, the ones that consume people and time. These make strong starting points, because they let you assess how machine learning can automate the work.
Always start from your business processes and ask 'why' first. Which KPIs are we actually interested in? Once that is clear, you can assess data sources and technology requirements.
To move fast and capture value, think about how to bring your data together so it can serve many different projects across AI, machine learning and business intelligence.
It is also important to measure the opportunity cost of building and running projects in house. Look beyond your own four walls and consider Software as a Service (SaaS) vendors that can deliver positive returns.

What is the key to improving performance with AI?

The key to using AI successfully in insurance is to start from the problems you have identified in your own business. Nobody knows your work better than you do.
There are many costly or manual processes that AI can improve or automate with relative ease. In some cases a simple heuristic or another automation technology will do the job just as well. The point is to avoid chasing trends blindly, and to avoid missing a real opportunity to improve the processes inside your own business.

Sources

• Deloitte, AI to Fight Insurance Fraud, 2025
• Deloitte, 2026 Global Insurance Industry Outlook