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The final stage of the data lifecycle in insurance is decision making. Data is used in decision-making processes carried out by both human underwriters and AI software and is used to inform decision making at every stage of insurance.
All insurance brokers have many gigabytes of data at their disposal, but no matter how much data is extracted and even augmented, it's only when insurers understand how this data can be fully utilised that data like this can see its full potential.
How data is used for decision making in insurance
If you haven’t already, consider reading our previous articles in the data lifecycle in insurance series on data extraction and data augmentation to get a full understanding of how the data lifecycle works.
Once insurers have conducted data extraction and data augmentation, it's time to feed that data into AI and machine learning software that can truly put it to good use. Data in volumes of this size, which is what's known as 'big data', isn't usable by human underwriters, but machine learning software can scan through it, looking for patterns, trends and risk factors.
With the help of machine learning, big data can be put to work at almost every stage of the insurance process; from the initial stages of application and risk assessment to claims processing and, more generally, customer service.
The first stage of an insurer's job is always risk assessment; when new applications for coverage are submitted, how do insurers figure out which ones are worth the risk? This is where big data can help, ensuring that risk assessment methods are effective, neither overestimating nor underestimating the level of risk each submission poses.
The more data that underwriters have at their disposal, the more accurate and reliable risk analysis becomes. The effects of this are even clearer when we look at the effects that new data sets - for example, telematics and fitness trackers - can have on risk identification.
In turn, enhanced risk assessment techniques make it possible for insurers to offer more innovative products that better suit the needs of their customers. Insurers who have solid, reliable risk identification algorithms can branch out into complementary offerings such as parametric insurance, which requires a good grasp on risk (or a broker could face serious losses).
For more information, read our article about scoring and triaging risk with the help of AI.
Underwriting & pricing
We've written already about how big data and AI can aid commercial underwriting. A part of this work is in improving risk identification, as described above, but it's also about making an underwriter's work simpler, and the results better for customers.
For example, when risk analysis is improved, underwriters no longer have to use outdated, broad risk categories designed around primitive data collection techniques. Why not use our greater understanding of risk to offer customers fairly priced premiums that reflect their own individual level of risk, calculated using every data point we have?
Insurers can even use their greater understanding of risk to offer better insurance deals to customers willing to go the extra mile to reduce their premiums; for example, if insurers can offer policyholders a discount for having a burglar alarm fitted or car telematics installed, it opens up more choice for customers who do want lower premiums without compromising on general service.
Just as big data can be used in risk, so can this pattern identification technology in machine learning be tuned to fraud. According to the ABI, over 107,000 fraudulent insurance claims were discovered by insurers in 2019.
Like risk, signs of fraud are difficult to spot without many data points to work with; not only can machine learning software learn to spot these signs, it can learn, over time, to identify new red flags that may signal that something is amiss with a claim.
Finally, big data can be used to improve the customer experience, and not just in terms of the improvements that insurers can make to the products and value they offer customers. Big data can be used to establish to insurers what customers want, as well as in developing AI tools that can be used to directly improve customer experience.
For example, machine learning software can be used to program live chatbots to deliver reliable, fast customer service online without the need for customers to wait in phone queues. Complex enquiries can be forwarded to human support staff, while simple requests for information can be processed by chatbots immediately, reducing customer support wait times and improving response rates without compromising on quality.
Insurers can also use high volumes of customer feedback to inform their decisions when it comes to developing new products, streamlining the customer journey, and even when making marketing decisions. Data such as average handling times can be used to identify issues in customer support that need to be addressed, while tracking click-through rates of marketing emails can help insurers to assess the performance of marketing strategies.
Customers who are satisfied by the service they receive are 80% more likely to renew their policy than those who aren't, so good customer service should always be a top priority.
How decision making in insurance is changing
For a long time, decision making in insurance has seemed to be solely the work of human insurers, but today's sophisticated AI means that this is no longer the case. The ease with which data extraction in insurance can occur, and the capability of modern computers to analyse large volumes of data, means that decision making can now be partially automated by AI.
At Artificial, we know that topics like AI, machine learning, and big data are a lot to get your head around. This is why we created our blog, full of advice and ideas to help your insurance business write better risks, faster.
If you'd like to know more, get in touch with us.