Why Data Annotation is the Key to Success
Insurance companies are trying very hard to shake the stigma that they are behind the times, and with good reason. The popular consensus among consumers is that dealing with an insurance provider is like getting a root canal without anesthesia. Ouch. To be fair, the process is typically complex and tedious. It’s no wonder research says insurance companies lag behind other industries in the realm of digital transformation. Even as outdated methods are phased out in favor of faster, sleeker digital tools, the insurance industry has a long way to go to catch up to the seamless experience consumers now demand. However, many within the insurance sector believe artificial intelligence (AI) will help fast track that transformation.
From insurance claims processing to insurance underwriters, AI is slowly but surely taking what was a predominately paper-based business into the 21st century with a digital-first approach. AI-enabled technologies aim to improve everything from customer experience to the way insurance providers make predictions and manage risk. But the real foundation of this evolution starts and ends with data.
AI Needs the Right Data to Learn
Data is certainly not a new revelation for the insurance industry. Insurance companies have collected massive amounts of data including customer demographic data, property data, automotive data, historical claims pay out data, historical applicant risk data, and sales/pricing data with regards to premiums. For years, it has used this information to help guide the most critical business decisions. But the advancement of technology and innovation has also created an explosion of new data sources, making it harder for insurance companies to understand good data from bad data.
The old adage of garbage in, garbage out, is fundamentally applied in AI and machine learning solutions. After all, AI is only as smart as the data it consumes. What the insurance industry really needs as it develops its AI strategy is to integrate, clean, link, and supplement their data so they have an accurate foundation on which to build the ground truth data that drives real AI innovation. But even if the datasets are curated and validated, one single error in the data or in the training sets used to create predictive models could potentially be catastrophic. Insurance companies cannot afford that risk.
The Role of Data Annotation
Data annotation (also commonly called data labelling) is the initial step in ensuring AI and machine learning projects can scale with accurate information. It provides the setup for training a machine learning model with what it needs to understand and how to discriminate against various inputs to come up with accurate outputs. Data annotation can be applied to any type of data asset. It can range from images and video to text and audio – essentially any information that can be used as the basis of AI training data will benefit from going through the annotation process. But the machines can’t do this alone, at least not at the beginning. Humans are needed to identify and annotate specific data so machines can learn to identify and classy information. Without these labels, the machine learning algorithm will have a difficult time computing the necessary attributes.
When it comes to processing and analyzing insurance applications, insurance claims, reviewing medical records for identifying risk, or even gauging customer sentiment, having high-quality annotated data will help drive success across many areas where AI is being employed. Below is a list of some of the most popular ways the insurance claims and insurance underwriting industry is beginning to use AI to change perceptions of a storied industry.
Natural language processing (NLP) is a sub-set of artificial intelligence that deals with programming software to process and analyze large amounts of data that has been captured to reflect the ways humans write, speak, or document information.
An insurance carrier could use NLP to develop a conversational interface/chatbot that can answer questions from customers or allow them to file a claim from the chat window. Chatbots enabled by NLP AI deal with recognizing the intent within text data, as well as responding to customers with text. In essence, the NLP software needs to “learn” the appropriate text responses to text it receives.
- Conversational Interfaces/Chatbots allow customers to file claims, move payment dates, and get auto insurance quotes
- Customers can use the chatbot to get a quote for auto insurance, file a new claim or schedule a payment.
- Chatbots can help business owners by answering initial questions such as “what is a deductible?” and “how does the insurance claims process work?”
Customer Service NLP enables large insurance enterprises to offer:
- Improved customer service and better buying experiences, especially to millennial customers that are accustomed to seamless, digital channels.
- Insurance carriers might prefer to develop omni-channel conversational interfaces to make it easier for their customers to access information about their policies or file claims through chat messages.
Internet of Things (IoT)
The Internet of Things or IoT refers to the larger connected device environment that is emerging from the combination of electronics and internet capabilities. This includes smart home devices such as Amazon Echo or Google Home and wearable devices such as smartwatches or fitness trackers. Insurance firms can use the data being collected by all these different devices to personalize insurance products. AI-enabled IoT devices are seeing the most use in auto insurance; drivers can install devices in their cars or download an app on their smartphones. With IoT technology, Insurers track their driving behaviors, feeding this data into an auto insurer’s predictive analytics algorithm.
- Some auto insurance companies offer customers an IoT sensor that can be placed in cars to collect data about individual driving habits, such as how hard a driver breaks or how wide their turns are. The company can use this IoT sensor data combined with customer demographic data to offer customers an auto insurance rate that is tailored specifically to them. The auto insurer could then use this information to decide whether or not to onboard an applicant and how much the applicant’s policy should cost. Furthermore, the insurer could adjust the price of an existing customer’s policy for good or bad driving habits, decreasing or increasing the premium the customer pays respectively.
- Similar use cases can be found in other insurance verticals. Health insurance providers are very interested in their customers’ everyday health habits. Data from smart watches can be used to influence premiums and keep track of anomalies that could cost them money in the long-term.
Computer vision is a type of machine learning that allows computers to “see” entities within images and videos. In doing so, the user can verify the existence of these entities and run analytics on them that can inform business decisions.
Home insurers can use computer vision algorithms to run through satellite images of a property to determine if the property is prone to flooding or if the property has a trampoline. It can use this data to determine whether or not to underwrite a property. The algorithm may stop at pointing out an element of the property, or it may include a predictive analytics aspect that recommends the insurer to approve or reject an applicant based on the risk the property poses.
Insurers can better manage insurance for catastrophe risk management for homes and businesses applying for reinsurance. AI can help insurers to evaluate properties on whether or not they had a pool enclosure, if it is located in an area prone to floods or fires etc.
What’s more, some auto insurers allow their customers to take pictures of their car’s damage using their smartphones. These images are uploaded to the insurer’s system and run through a computer vision algorithm paired with predictive analytics capabilities. Based on the damage, make, and model of the car, the algorithms provide an estimate on how much the auto insurer should compensate the customer on their claim. This reduces the time it takes for customers to receive their pay outs and avoids claims leakage, saving insurers money.
Pretty cool stuff for an industry that is considered out of touch, right? It won’t be long before customer sentiment changes quickly. But only those insurance companies that are on top of their data and ensuring it is ready for AI will have the real advantage over their competitors.