Big data in insurance: 4 tips for better data management

The tech industry has been talking about the impact of big data and the rapidly increasing networking of data pools for years. According to “IT business”, these incredibly large amounts of data collected on the internet, in the cloud and in local data centres are the oil of the 21st century. One of the most important and influential developments of our time. But while oil resources are becoming increasingly scarce, data volumes are growing rapidly and are therefore becoming more and more valuable for economic progress – including, if not especially, for insurance companies.

But how can insurance companies make the best possible use of these data volumes and what are the advantages of big data in insurance? In this blog article, we give you an insight into the influence of big data in insurance and give you 4 tips for better data management.

The advantages of big data in insurance

The business model of insurance companies fundamentally consists of analysing data. The advantages of big data are therefore obvious. For example, Guidewire reports that big data can help insurance companies to assess risks and insurance claims, increase customer satisfaction through personalised customer experiences and react faster and more specifically to accumulation losses.

Big data can also be used to uncover fraud schemes in your insurance company more quickly and efficiently. Large amounts of data help to identify behaviour and damage patterns and, for example, to put a stop to fraudsters with automated image recognition of the claim.

Good data management is fundamental for insurance companies to make the best possible use of big data. After all, collecting large amounts of data requires sufficient storage space. However, recognising and evaluating patterns from these data volumes requires more than just an archive. A document management system with AI-based document recognition and audit-proof archiving is the perfect basis for utilising big data.

In the following 4 steps, we will show you how you can improve data management in your insurance company.

1. Clean master data maintenance

IT experts talk about the “shit-in-shit-out” principle. Or rather: poor data maintenance also leads to poor data management in insurance companies. The benefits of big data are not just about collecting data, but also about analysing it. So it’s not enough just to have a lot of data if you can’t do anything with it. In order to benefit from the advantages of big data, it is therefore important that data is available correctly and completely in your insurance system at all times. This allows analyses to be correctly evaluated and strategic, data-based decisions to be made.

The use of technology can play a crucial role in updating and enriching insurance data. For example, artificial intelligence can identify duplicate data records, such as customer data, enrich data records with missing information (e.g. postcodes) and recognise trends and patterns in large amounts of data.

2. Rely on standards

The integration of industry-wide standards is a critical influencing factor for efficient data management. You probably know it first hand: insurance companies operate with a variety of different systems. Each system has its raison d’être and helps with day-to-day work. However, the individualisation of software often makes data handling, secure data exchange and the analysis of existing data more difficult. IT experts therefore recommend as much standardisation as possible and as much customisation as necessary. This enables IT service providers to guarantee the best possible support, minimise sources of error and reduce downtime. Especially when it comes to big data, it makes sense to use standards so that insurance companies can work efficiently without spending too much time on data management.

3. Small steps with a big impact

Optimising the entire data management process in one fell swoop and, at best, even automating it using workflows in dark processing sounds more like a perfect world than reality. The fact is that you don’t necessarily need a large, costly migration project and a new DMS to be able to utilise big data in insurance companies. It is often the small improvements that can make a significant difference. For example, you can significantly improve your data quality with the integration of a single workflow, e.g. for changing the address of insured persons. At the same time, you also relieve your employees of time-consuming routine tasks and optimise the customer experience for your policyholders. An agile approach that relies on dark processing in iterative steps and continuous evaluation has proven to be particularly effective for our insurance customers in the past.

Excursus: Dark processing as a pillar for the use of big data in insurance companies

When we talk about big data, we are talking about unimaginably large amounts of data. So large that it can hardly be managed manually. Process automation is the first step towards managing big data more effectively. Dark processing goes one step further. It enables fully automated processing of business transactions without manual intervention. This means that no employee has to tip, check or file insurance documents into a workflow in dark processing. Dark processing is 100% automated. Different software solutions can communicate with each other and act accordingly on the basis of existing data.

An example: An insured person moves house and wants to change their address. He logs on to the insurance portal and follows the steps for changing his address in the customer portal. For the insured person, the process is already complete after registration in the customer portal. An automated workflow starts so that this change of address is now also adjusted in the insurance company’s systems. The insured person’s master data is read from the DMS, extracted and adjusted using artificial intelligence. At the end, the adjusted data of the insured person is stored in an audit-proof manner so that changes can be tracked transparently.

Dark processing therefore eliminates time-consuming routine tasks and significantly improves data quality. At the same time, the d.velop process studio enables employees to help design workflows first-hand without any programming knowledge, thereby improving not only the customer experience in insurance companies, but also their daily work.

4. Involve employees and specialist departments in your insurance company

Which departments in your insurance company manage the most data? These employees are your data management experts. Their experiences, needs, problems and challenges are the basis if you want to improve data management in your insurance company. Listen carefully to what they struggle with in their daily work and what wishes they may have for the automation of these processes. The dark processing of insurance processes, such as cancellations, address changes, postal returns or the reading of insurance data, can only be successful if employees have a positive attitude towards this evolution.

Conclusion: Insurance companies need to rethink their data strategy

The increasing importance of big data in insurance companies emphasises the need for sophisticated data management. Insurers are faced with the challenge of not only storing large amounts of data, but also utilising it sensibly for risk assessment, increasing customer satisfaction, accelerating claims processing and detecting fraud.

Master data is the foundation for making these data-based decisions. Missing or incorrect data undermines the quality of analyses and therefore has a major impact on the strategy of insurance companies. Good data quality is therefore essential in order to benefit from the advantages of big data. However, big data in insurance also brings with it a number of challenges. After all, enormous resources are required to keep data up to date at all times. It therefore makes sense to automate routine processes and process them in the dark using workflows.