InsureTech is the latest buzzword that is making the headlines in the insurance sector and with good reason. From suitable risk assessment using alternate data to tapping big data analytics and artificial intelligence in insurance for better outcomes in diverse arenas, insurers are expected to step on the gas further across the next couple of years in this domain. Here is a brief glimpse into the same. 1.What are the key drivers of InsureTech? These are some of the major driving forces behind the InsureTech revolution that is sweeping the world today. Let us now learn a little more about the deployment of artificial intelligence in insurance. 2. How is AI used in insurtech? Artificial intelligence in insurance and InsureTech are symbiotically linked due to the multifarious applications and use cases that have transformed the industry in recent years. Here are a few aspects worth noting in this regard: AI is beneficial for the entire InsureTech ecosystem in multiple ways, as is mentioned above. A closer look is also necessary at the various sources or types of alternate data that insurance companies can use for better risk assessment. 3.What kind of alternate data can help towards solving the credit risk? FAQs 1.What are the privacy and ethical considerations associated with using alternate data in risk assessment for InsureTech? InsureTech players must address privacy, ethics, and data validity when using alternate data. Key considerations include responsible data collection and usage, obtaining consent, ensuring analytical tool validity, fairness, and unbiased systems, data quality, regulatory compliance, and full disclosure principles. 2.Are there any successful case studies or real-world examples of InsureTech companies leveraging alternate data for risk assessment? There are many examples of InsureTech entities making use of alternate data for risk assessments. ZestFinance, for instance, deploys AI for evaluating both traditional and non-traditional information to gauge risks while automating its underwriting procedure for lower risks. Nauto has already been using AI for forecasting purposes. The aim here is to avoid collisions of commercial fleets (driverless) by lowering distracted driving. The AI system uses data from the vehicle, camera, and other sources to predict risky behavior. 3. What future trends do you foresee in the use of alternate data for risk assessment in the InsureTech industry? There will be greater emphasis on leveraging telematics and usage data garnered through connected vehicles and IoT devices along with smart home devices. At the same time, more machine learning models will be used for algorithm-based risk assessments. The Metaverse will be another channel for insurers to combine their AI-backed Chatbots with sales pitches, internal training, data gathering, and even NFTs for personal document verification. 4. Are there any challenges or limitations in leveraging alternate data for risk assessment in InsureTech? There are a few limitations/challenges in using alternate data for assessing risks in the InsureTech space. The quality of the data and whether it tells the whole story is one challenge along with the fact that there are ethical and privacy-related considerations, regulatory aspects, and the issues related to disclosures, user consent, and the methods of gathering data.
