What It’s About
There are three key reasons why you should consider implementing a Big Data solution into your workflow.
The Amount of Data
You can get tons of various data on your customers from a wide range of sources, such as social media and Google tracking systems. What remains unclear for some insurers is how this data can be used. Big Data analyzes the data to get the most out of it.
The Types of Data
Insurers usually make all the important decisions by relying exclusively on structured data. Nowadays, you can get tons of data, but until it’s processed, it remains quite chaotic and almost impossible to comprehend. Big Data allows gathering different types of data and structuring it to provide you only with relevant insights.
The Data Processing Speed
In the case of legacy software, it may take weeks to obtain, analyze, and structure data. This is too long in the world of rapid changes. Big Data solutions can provide you with immediate detailed reports within seconds, so you can make wise decisions without any delays.
This Is For
The insurance organizations, agents, and brokers that:
- regularly get large amounts of unstructured customer data
- want to get the most out of the received data
- realize how important efficient data management is
- would like to cut costs and save time
Wiser decisions that are more likely to lead to a positive outcome
Efficient and smart data management
Accurate detection of fraudulent claims and other actions
Automated and, therefore, faster claims processing and settlements
Personalized and more cost-efficient pricing and reinsurance strategies
Increased level of customer retention
improved customer experience
How It Works
We can bring you an efficient Big Data solution that will help you achieve even the most ambitious goals.
The scope of the Big Data solutions we can make for you includes:
- Tools for fraud detection
- Behavior analytics for marketing purposes
- Analytic systems that provide insights into who your customers are, their needs, preferences, behaviors, etc.
- Solutions for improving the customer experience
- Data mining algorithms for prioritizing claims
- Apps for accurate risk assessment
- Algorithms for automating routine tasks related to data processing and management