The insurance industry is a heavily competitive industry valued at an estimated $507 billion, or 2.7 percent of the US Gross Domestic Product. For the sector, customer satisfaction is the principal need of the hour, and not just an optional operational aspect. Machine learning, especially a seamless integration of machine learning algorithms, has emerged as an ultimate game-changer in this scenario, allowing adopters to focus on improving compliance, cost structures and competitiveness, while ensuring consumer happiness.
Machine learning (ML) is a branch or subset of artificial intelligence that involves equipping computers with pattern recognition and incremental learning capabilities. The fundamental proposition of machine learning is to build algorithms that can receive input data, and use statistical analysis to predict an output value within an acceptable range.
Machine learning algorithms are mostly being grouped as supervised or unsupervised. Supervised algorithms require human intervention to provide both input and desired output, in addition to delivering feedback accuracy on predictions. Once setup is complete, the algorithm will apply the insights into the new data. Unsupervised machine learning algorithms, on the other hand, do not need to be trained with desired outcome data. Instead, they use an approach called deep learning to review data and arrive at conclusions. Unsupervised learning algorithms are used for more complicated processing tasks than supervised systems.
Here are some practical ways that the insurance industry is using machine learning and machine learning applications today:
Chatbots are software programs that predominantly use B2C (Business-to-Consumer) text-based messaging as the interface through which to carry out any number of tasks. Chatbots evaluate the man-bot interaction and use the data to improve customer satisfaction. Progressively, the bots ascertain the common issues faced by a wider customer base and accordingly empower users with the suitable automated responses, allowing some operations to run 24/7/365.
Approximately half of all customer service interactions are currently highly suitable for bots. As a result, we see a host of systems where humans and bots works together. Very simple questions can be handled directly by a bot, but as soon as the conversation becomes too complicated the bot hands the conversation over to a human handler. The human overseer handles the more complex tasks, and may hand the interaction back to the bot to complete other simple aspects of the interaction.
While the banking industry has been leading chatbot technology adoption, the insurance market may not be too behind. For instance, Singaporean insurtech PolicyPal has announced the launch of its AI-enabled chatbot, on its mobile app.
PolicyPal allows customers to buy and manage their insurance policies via mobile. IBM Watson was trained on PolicyPal's database of more than 9,000 insurance policies to be able to gauge the intent of customer queries instinctively, and answer their questions quickly.
- Machine Learning in Customer Service phone calls:
Machine learning is harder to deploy in voice-based communications. On a purely technical level it is more challenging for a computer system to deal with voice than chat. Background noise, uncommon speech patterns, voice accents, and poor pronunciation all make it hard for an ML-based system to translate voices into text.
To cite an example, the startup Cogito has developed a real-time conversation-analysis tool based on behavioral science and deep learning. Their ML listens to conversations for both content and pitch, and the company claims it can detect mimicking, change in volume, change in tone, etc. to gain real-time insight into how customers are feeling and how all calls are going. The tool provides real-time suggestions to customer service representatives to improve the call and gauge performance.
- Risk Classification and Driver Performance Monitoring:
Vehicle telematics devices track variables such as speed patterns, location, hard braking and weather. Auto-insurance companies can categorize drivers into various risk groups by more accurately leveraging machine learning with this data. The technology, involving the use of machine learning applications, allows for a classification process that is automated and error-proof.
Besides risk group classification, Deep Learning algorithms can be applied to images of vehicle damage, allowing for automated claim classification.
Liberty Mutual announced plans to develop automotive apps with ML capability and products aimed at improving driver safety. Solaria Lab is an innovation lab established by Liberty Mutual, has launched an open API developer portal which incorporates the company’s proprietary knowledge and public data to inform how these technologies will be developed.
Machine learning applications seem set to garner even greater traction, with the insurance company reportedly experimenting with a new app to help drivers involved in a car accident swiftly evaluate the damage to their car in real-time using smartphone cameras. The app’s ML component would be trained on thousands of images from car accidents, with the resultant being capable of providing damage-specific repair cost estimates.