Data is the key to making good business decisions. Every insurance company collects vast amounts of data on their insureds, such as claims information, credit history and police records. Carriers know, however, that just having this data is not enough. They must be able to glean insights from the data to make the best risk assessments. Artificial intelligence (AI) and machine learning (ML) are the newest tools used to discover new insights. But using the insights to inform business decisions only works when the data is correct, and unfortunately, many data sources contain inaccurate information. Using better quality data helps you make better data-driven decisions.
Impact on your business
It’s a given that even the best data sources aren’t 100% accurate, which can create an incomplete picture of risk that should be considered when underwriting a policy. Decisions based on poor-quality data can mean increased risk exposure and inaccurate policy pricing, which can lead to decreased profitability and competitiveness for the carrier.
Carriers know that data holds the key to making good business decisions and will invest a lot of money in acquiring vast data sets that they can process via AI and ML. Sometimes more data can cloud the picture, leading to decisions based on poor-quality data. How do you know if you are using high-quality data in your AI/ML systems? As we share in our new whitepaper, Optimal data quality means better data-driven decisions, a few questions to ask yourself include:
- Can this data be validated against another data set?
- Who owns the data?
- How often is it updated?
Knowing if the data you are using is accurate isn’t just important for making decisions about risk. It can positively or negatively affect your customers.
Impact on customer experience
Better quality data helps insurers be better attuned to the needs of their customers. Consumers expect a seamless experience when purchasing or updating their policies, or when they need to make a claim. In this age of digitization, they’ve come to expect that carriers will respond swiftly to concerns, with little friction. When it comes to marketing campaigns, poor-quality data can lead to ill-timed outreach with inaccurate policy pricing, putting off potential customers. To sum up, if your data quality is poor, you risk a poor customer experience, which can lead to retention loss. If your data is accurate, you can create a happy – and loyal – customer.
In the next blog in this series, I will highlight best practices for avoiding data quality pitfalls, and how LexisNexis Risk Solutions can help you improve data quality. For more on this topic, download our new white paper, Optimal data quality means better data-driven decisions.