A business with a data-driven approach bases its decisions on the study and interpretation of data.
An organization with a data-driven approach can contextualize and personalize its operations to its prospects and customers for a more consumer-centric approach by leveraging data to lead its decisions, which applies to handling customer claims.
But what does it mean for the personal injury claims process?
Digital Darwinism and winning the claims management evolution
Data-driven claims management is all about building a solid foundation on data and proactively using that data to improve all parts of the claims management process.
The business benefits of a data-driven approach in claims management are clear:
- Improved process quality
- A reduction in claims costs
- Time per claim reduction, improving customer satisfaction
- Increased accuracy in claims decisions
- Improved customer engagement
In this piece, we'll show you the foundations of data-driven claims management and how they help to deliver the benefits listed above.
Those insurers that don’t adopt a data-driven approach will be left behind — digital Darwinism is very much at play.
But don't just take our word for it.
The 2021 Gartner Eye on Innovation for Financial Services review found that there has been a huge upsurge in AI and machine learning with a focus on cost reduction and greater customer centricity.
If the financial services sector is moving this way and at pace, then insurers, and leaders in claims management positions, cannot afford to ignore these trends.
Personal injury claims, particularly, should be a priority for a data‑driven approach because they are complex, time-consuming, expensive, and critical for customers.
The 4 pillars of data-driven claims management
When we talk about the foundations of data-driven claims management, we're referring to the most important things claims management departments need to be aware of when pushing for a more data-driven approach.
They are the underpinning of a successful data-driven strategy, so they should be considered in any decision-making in a claims environment.
1. Data consciousness
Data must be reliable to be used. The accuracy and completeness of the data are directly linked to how much you can trust it.
Where did the data come from?
How was the data collection carried out?
Was it done in a safe and auditable way?
When the data is accurate, sharing it is easy because everyone is confident in it. Poor quality data cannot be shared or used in a meaningful way because it is difficult to work with and impossible to trust.
Just as important as the quality of the data is its source because you don’t want questionable data informing your decision-making.
For data to be accurate, you must be able to demonstrate that it was collected securely, thoroughly, and in a timely manner. It also needs to be processed by someone who understands the context surrounding that data.
When the sample size is too small or biased, the quantity may also impact the accuracy and quality of the data. Interestingly on a very small or incomplete sample, a human can sometimes infer some of the contexts more effectively than a machine.
However, common human errors normally include:
- Manual entry — grammatical errors and getting date orders wrong
- Bias in data interpretation and entry
- Missing data from incomplete or incorrectly entered necessary fields
- Incorrect data entered into fields without input field validation
- Unvalidated and inconsistent data formats – addresses, numbers, etc.)
These look like small issues on paper, but in a wider business context, they can have massive implications on a claims department because these errors can make data worthless. After all, if you can't trust it, you can't use it.
Let’s take a simplified example:
You're investigating what type of injury takes up the most resources during your claims process. That information could show where you need to contract more medical advisors, better train your claims handlers, and even adjust the pricing of certain insurance policies.
But without enough good-quality data, you can't act because a poor decision could hurt your bottom line.
Being able to make good decisions comes down to the data you gather.
"Intelligence is not just about using more data; it is also about making data actionable, managing data across the ecosystem, and adjusting the strategy over time."
— Gartner 2022 Digital Insurance Success Requires Leveraging Data, Analytics, and Artificial Intelligence
2. Data Responsibility
Data responsibility focuses on not breaking the law and following the rules that govern your business and industry. This should be motivated by wanting to look after your customer's data.
When managing personal injury claims, you handle extremely sensitive information, so you must take data security seriously.
Any potential data breach would put your clients' security and, consequently, your company's future in danger, in addition to subjecting you to serious fines under legalization like GDPR.
Years are spent building a reputation, which can be destroyed in a moment of carelessness.
We often see processes that include workarounds, printed documents, and just generally lots of unnecessary work that's also often illegal.
That's because many insurance businesses are still maturing digitally. Staff are often used to working in a paper-based environment, which doesn't fit with a hybrid of remote setups.
A cultural shift is required to move away from this behavior.
If you push for a more data-driven environment for your claims management, you need to develop secure, reliable, and effective processes because mistakes can be costly. Claims management is your responsibility, so don't let security slip.
Claims managers, like you, need to educate staff on current security practices and make them aware of security standards and regulations like GDPR.
You also need built-in security tools because it passively helps foster compliant processes and ways of working and gives each user the right level of access.
3. Technological adoption
If you are ready to make data a big part of improving your claims management setup, then you will need to put in place technology that helps to use and deliver data throughout your organization.
Here are a few examples of tech you might have heard of and how it can help you in claims management.
Artificial Intelligence isn't actually 'intelligent,' but it can process information extremely fast and, given the right parameters and training, can produce consistent results in almost any application.
Almost every industry can utilize AI to boost productivity and streamline processes. AI handles tasks at a speed and scale, allowing for trend spotting in data and augmenting decision‑making based on historical information in ways people cannot achieve.
AI can form part of predictive models that help establish potential claim costs early in the claims process using historical information. A predictive model, for example, might recognize a low-value, low‑severity claim and automatically move it through for straight‑through processing.
It might also recognize that a claim possesses certain traits that make it more likely to experience complications and suggest case management or other measures before the claim progresses.
Dashboards and insight
Business intelligence dashboards play a key role in analyzing an organization's data. Dashboards typically draw visual representations from a central point of truth where data from around the business pools together.
Anyone can have customized dashboards built for their role that gives them the information they need to excel. Business Intelligence, BI, provides users with a quick way to understand what is happening.
Rather than waiting for the month's end to get performance reports, you can have real-time data available on any metric within the claims environment.
Most insurance companies handle personal injury claims manually, following a rigorous process that requires sorting through a lot of unstructured data.
It often takes days, weeks, or even months for more complex cases to fully complete processing. This level of manual work is also prone to human error and harms customer experience.
Automation offers a clear solution to all these problems. As a simple example, a robot 'bot' can use validation algorithms to collect and review claims data.
Automatic validation checks examine the claimant's policy details, benefits, and service provider to ensure that the correct claim handler is allocated to the claim.
It's a basic example, but it means that a human staff member hasn't had to spend any time typing, moving, and checking data, and this has all been achieved with zero errors and at a pace that a human could never keep up with.
When speaking with European insurance companies, we often hear things like:
"When people speak about automation in insurance, they often conclude that personal injury claims are so complex that they can never be fully automated. Unfortunately, this closes them off to the idea of automation."
We agree that complex claims can never be fully automated, and they never should. An injured customer needs a human claims handler to talk to.
But we want to automate the manual, non-value steps of the process to support the claims handler make faster, more accurate decisions.
Beyond the benefits of efficiency, the extra time claims handlers can focus on the part they do best: providing a human service.
We don't want to remove the claims handler. We want to make them better.
4. Stakeholder engagement
A claims manager must drive the change you want to see. You're responsible for claims management, and the buck stops with you. That being said, you can't do it alone, so you need to commit to creating allies in implementing a data-driven approach.
This means you need to show the business impact, the staff impact, and the customer impact of what you're trying to achieve to the key stakeholders.
Ultimately, it needs to make sense from a commercial perspective, so lead with the benefits.
Luckily, people can feel the benefits of a data-driven approach throughout an organization. Even if the data is collected from the claims process, this data is highly valuable in other areas of an insurance business.
For example, underwriting, pricing, and product development all benefit from additional data. So, get them involved and make it a common project because you'll all be better for it.
Connect rationally and emotionally to stakeholder goals. Instead of delivering progress data, emphasize the business outcomes and value.
Like any other goal, you need to speak in specific, measurable, realistic, relevant, and time-bound ways. Those business benefits we’ve touched on, better underwriting, pricing, and product development, lead your charge with them.
You must act and practice what you preach, or people will lose faith. Show people how a data-driven approach improves those areas of claims management by actually doing it first.
Also, identify potential problems and be honest that it isn't always going to be plain sailing.
You'd be surprised how reluctant people are, to be honest about the challenges involved in new initiatives, but if you do from the start, you'll win stakeholders with your transparency. People want to follow someone they can believe in.
But which stakeholders are at the top of your list to get on the side? Well, whether it's information or data in your business, your CIO/CDO will be a critical ally.
They will give you increased influence over IT resources and be senior figures who can back your plan. Your IT team might not have the full skillset, but you will need them to help you with any tech rollout.
According to Gartner, "72% of Data & Analytics Leaders Are Leading or Heavily Involved in Digital Transformation Initiatives".
Please don't leave them out in the dark.
How to get started
As said at the start, you must be data-driven to keep up with the rest of the insurance industry. Statista states that between 2016 and 2021, IT and AI technology spending increased by around 650%.
When looking at your competition, can you see that trend happening in real-time? If you aren't doing it in your business, you need to.
The goal should be to develop a strategy that combines experience, expertise, and enthusiasm for data — building a system that supports that will be central in delivering on your goals.
You could do this yourself, but you're busy running a claims management team, and your IT team probably doesn't have the time or skills to make it live up to its potential.
The first step should therefore be clear: look externally. Using a subcontractor can be risky, so you should find a partner you can rely on.
Understand their business and people, and learn about their understanding of things like GDPR and insurance market trends to ensure they're experts.
Once you're happy that they are who they say they are, start to look at the details of the system they offer.
Will the software fit in with your claims setup?
Can it give you all the analytical tools you need, along with the ability to access shared market data?
This is where we point out that Mavera provides an cloud-based decision support system with insights, machine learning, and medical assessment management to automate your operations.
Here you can see how Mavera has helped companies streamline their claims handling in a wide range of scenarios.
As you can hopefully see, there are measurable benefits for claims management teams and insurers if they pursue a data-driven approach.
Reducing claims costs, improving record accuracy, and building stronger relationships with customers through transparency and security are the big headlines for any claims manager worth their salt.
But these benefits are only achieved if you correctly lay the foundations of your future. With experience handling over 250 000 cases, we know how important data is and that as an insurer, your primary focus is the level of care your customers receive.
Let's work together and create a data-driven claims environment that brings benefits to everyone involved.