Data Analytics, Blockchain, Bots – there are many new emerging technologies set out to disrupt the traditional insurance industry market. With the emergence of these disruptors, traditional insurance companies are exploring Artificial Intelligence (AI) applications to advance customer satisfaction, automate processes, mitigate human prone errors, and expedite business tasks. For example, AI-enabled chatbots can become frontline customer support execs, damage assessors, fraud detection experts, risk and premium evaluators. All thanks to data analytics.
This article will give you a sense of WHY the insurance industry is incorporating AI solutions, and WHERE (processes/departments) they are being implemented.
Trends that are causing the influx of AI in the insurance sector
There are certain general technology trends that are influencing the infusion of AI in the insurance sector:
1. Massive surge in data – Technology has made data collection and storage easy, as a result of which, the volume of data collected today is enormous. Starting from the device that is being used, all the way to the final endpoint (which could be a website or an application), multiple data points are collected at various levels.
The insurance industry, in particular, thrives on data. From calculating a policy’s premium to analyzing claims fraud, accuracy is a result of the amount of data available. The possibility to collect and store large volumes of user data, and analyze it with higher accuracy through AI, is a lucrative proposition for the insurance sector.
2. Open source and shared data ecosystems – Aiding data collection is the cross-compatible tech ecosystem we see today. There was a time when a Microsoft document wouldn’t function on an iOS device. Cross-compatibility is no more an issue; in fact, it has become a requirement. This, in turn, has opened up more avenues to collect user data, further improving data-dependent tasks.
Insurance companies were once limited to internal data sources (which was again within a narrow scope) collected via customer interaction, email communication, call centres, etc. The implementation of AI broadens the level of data that can be collected from multiple sources – from analyzing behaviour on the company website or app, data from IoT devices like a smartwatch, social media, or from credit bureaus.
3. Advances in cognitive technologies – Data collected from customers are very sensitive, and customers expect a great experience when interacting with an insurance company, especially during claims and customer support. Cognitive technology like NLP (Natural Language Processing) makes bots seem (more) human, putting customers at ease and nurturing a positive customer experience.
How AI is being implemented in the insurance industry
1. Customer service and support
Chatbots for customer support is a very common use of AI in almost every industry today. In fact, customers have come to expect the first line of communication with any company to be a bot. Chatbots are an excellent way of collecting preliminary user data (like name, age, purpose, location, etc.), data that is utilized in various processes in the insurance lifecycle. They can also reduce wait time by routing a chat/ticket to the right department based on keywords in the chat (which is what it means to be ‘AI enabled’). Finally, with cognitive technology, a chatbot is capable of ‘learning’ and mimicking human conversation to provide an authentic user experience.
2. Underwriting and pricing
Evaluating and analyzing risk during processes like underwriting and pricing is data-dependent. Existing data is analyzed to assess current risk and predict future risk before fixing a premium and insuring a customer. Machine Learning and Big Data Analytics (both branches of AI) can greatly reduce the time consumed for risk analysis and prediction, and improve the accuracy of results. AI solutions available today are capable of aggregating data from multiple sources that are just not possible manually (from social media, smartwatches, IoT home devices, etc.). The analytics capabilities are also higher, and with AI, systems are able to ‘learn’, which means as you give feedback, the system improves.
3. Claims management
Claims management involves four steps – Communication by the policyholder, ticket acceptance by the insurer, claim handling (verifying), and settling. With AI, all communication can be automated for faster and more accurate handling. OCR (optical character recognition) enabled bots are capable of extracting written text from forms, allowing even manual processes to trigger an automated workflow. Repetitive tasks like compliance checks, data entry, etc. can be automated. Secondly, the process of analyzing the claim, fraud detection, and finalizing the settlement can be made more accurate by incorporating AI. Lastly, the process of managing multiple claims can be streamlined to improve customer experience and reduce load while also saving time.
4. Risk analysis & management
Risk analysis is an integral component of the insurance industry and manifests in two ways – identifying a customer’s risks to analyze premium, and analyzing risk to the company because of existing (or potential) customers. With new customers, analyzing data derived from internal and external sources helps predict potential risks which in turn predicts the chances of a policy being claimed. Data like past accidents, social activity, health conditions, personal habits, etc. can all be aggregated and analyzed.
There also is a matter of internal risk to the insurance company that can be predicted – insurers who could cancel their policies, chances of losses due to a fluctuation in severity and/or distribution, etc.
5. Claims fraud detection and prevention
Losses due to fraudulent claims is a rampant issue even today. In fact, it is estimated that a whopping $80 billion is lost to insurance fraud in the US alone! AI can help with fraud detection and mitigate this risk. There are various ways an AI system can analyze and raise red flags – identify information mismatch by comparing with multiple data points (internal and external), predict actual damage (in case of a car accident, for example) through machine vision and 3D modelling, analyze post-disbursement behaviour to identify fraudulent patterns, or analyze information from credit bureaus.
Where do you go from here?
AI for insurance industries is a massive topic, but the importance of adopting it is abundantly clear. While there obviously is a lot to gain, by not aligning your business to modern times, you could have a lot to lose, especially if competitors are quick to digitize their business. The ideal step forward is to consult with experts in the field of AI and analytics (I’ve worked with Suyati in the past with positive results). Questions like what solutions to implement, legacy systems that can be retained, processes that can be automated, etc., are best answered by experts in this area.
Alexander Eck , Insurance & Tech Advisor at Suyati (Ex Sr. VP QBE Insurance)
