Insurance companies gather and create an enormous amount of data everyday. The life blood of the industry is information, which enables better risk management, increased sales, and solutions that are tailor-made to fit the needs of individual clients.
One of the main challenges insurance companies have long been facing, is the ability to process all these data. Verifying, categorizing, and analyzing information can be extremely labor-intensive, time consuming and expensive. On another hand, humans are prone to errors and bias, which can lead to losses for the company and needlessly high costs for the customer.
Machine Learning and Artificial Intelligence provides better alternatives to manual information handling, and a majority of the world’s insurance companies have started applying these technologies. The future of insurance is guaranteed with things like predictive algorithms and telematics being applied to companies’ big data analytics strategies . Underwriting, sales, claims processing, and fraud prevention are just four of the most powerful use cases for these technologies.
Underwriting is the process by which insurance companies and their partners decide whether or not to provide coverage to a potential client. In the legacy system, this is a lengthy and labor-intensive procedure, during which the insurer must evaluate risks, determine the likelihood of a loss, and calculate the premium. Most of these work can be efficient, effective, quick and automated using AI and machine learning, while losses can be reduced drastically.
Insurance companies have already begun harnessing machine learning algorithms to analyze data and make better decisions about their customers. The technology is applied both before the issuance of a policy and afterward, and it is frequently combined with telematics.
In fact, several car insurance companies have already released mobile applications which can be downloaded by customers to collect data about their driving behavior. If collected data indicates safe driving on the part of the driver, a more profitable policy for the company, with a lower premium for the customer, can be issued
Moreover, this technology is frequently leveraged in the sharing economy. Ridesharing and carsharing companies are able to reduce liability and insurance costs by proving good driver behavior to insurers through advanced telematics and data analytics.
Machine learning technology has, over the last few years, managed to radically disrupt the insurance sales funnel. Customers who reach an insurer’s website via social media and affiliate links no longer need to communicate with a human agent in order to receive personalized service and solutions.
Insurance companies today regularly interface with clients using chatbots deployed on messaging apps. While this occurs at the front end, underwriters are able to apply predictive analytics to review customer profiles. All it takes is a quick survey in a text chat for the company to provide a potential client with a list of tailor-made products or more general insurance advice.
As with all things related to machine learning, this technology will only get more sophisticated as more data is collected. With near-universal adoption of messaging apps like WhatsApp, and with about two-thirds of people feeling comfortable sharing data from wearable devices with insurers, greater adoption of this use case in the insurance industry is virtually assured.
Claims Processing is one of the most tedious processes in the insurance industry. From the point when claims are registered until they are settled, a great number of documents must be verified. Decisions need to be made regarding payouts and future premium increases. To make these things run smoothly, insurers are increasingly applying machine learning.
For instance, AI can be used to process handwritten documents submitted by clients and assessors. What had previously been a labor-intensive procedure, largely carried out offline, can now be executed in seconds. Then, the processed documents can be uploaded to the cloud and converted into a format to which predictive algorithms can be applied, reducing fraud (see below) and automating adjustments to client premiums.
Insurance companies in the loose more than $40 billion per year from fraudulent insurance claims and other schemes FBI Stats). On average, this costs the consumer between $400 and $700 annually. For this reason, companies have began deploying predictive analytics that identify potential fraud before it is ever committed.
Machine learning algorithms are far more efficient than traditional predictive models when used to review unstructured, semi-structured, and structured data to find fraud. During the claims process, surveyors must spend a large amount of time gathering information, including photos, client interviews, and police reports. This information may be stored in multiple databases, or have been sent by the client in multiple e-mails and messaging apps, opening up room for mistakes during the data validation process
AI is able to eliminate human error and instantly compile client data, applying evaluative algorithms to determine the validity of claims all along the way. Ultimately, this not only reduces losses but also decreases labor costs.
Just like other industries that rely heavily on data, including finance, e-commerce, and transportation, insurance (and insurtech) can get a lot of benefit from big data analytics, and from machine learning more broadly.
With the addition of related technologies, like telematics and the IoT, this is absolutely a space to watch. If you are interested in integrating one of these solutions into your business, reach out to the Fredfort Consulting team here.
Fredfort consulting is a global management consulting company that helps the world’s most ambitious companies make better business decisions. We are passionate about helping businesses unlock value through digital transformation and the implementation of new technologies. Feel free to reach out to us if you want to discuss how machine learning can help your business.
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