Different stages in claims handling process in Insurance sector can be improved using machine learning techniques. Algonox’s strong AI competencies help insurers in handling massive amounts of data in short time and automating the process which can speed up certain claims, reduce overall processing time and costs while delivering great customer experience. The algorithms can also constantly recognize patterns in the data and thus help to diagnose fraudulent claims in the process. With self-learning abilities, AI systems can then adapt to new unseen cases and further improve the detection over time. Furthermore, machine learning models can automatically assess the severity of damages and predict the repair costs from historical data.
Typical underwriting process includes enormous number of questions and surveys to decide on premiums. Instead of spending valuable time and money on the underwriting process, Algonox aids insurance agencies in automating the entire process. Automatic underwriting can tremendously speed up the process and often condense expensive tests unnecessary by reviewing several relevant data sources, including external ones like social media profiles to gather the data which is not present in the medical records. Bots could scan a customer’s social profile to gather information and find trends and patterns. For instance, customers with a healthy lifestyle and a steady job can be perceived as a safer driver, which could lower insurance premiums. With AI, we can evaluate information better than humans to more accurately forecast each customer’s risk, thereby providing customers with the right amount of insurance and companies with protection from risky customers.
AI algorithms would also help in building marketing strategy for the insurance agencies. Traditional methods of cold calling customers create a lot of distraction to the sales tunnel. Personalised insurance plans based on customer’s lifestyle, habits and preferences identified by machine learning algorithms help agencies to grow their businesses.