AI Driven CX in Insurance

From automated attendants to self-driving cards, new and innovative technology are disrupting major industries across the globe. One such emerging technology is artificial intelligence (AI) which provides humans with greater computing power, processing capacity and superior memory capabilities. These are systems which will be able to think, learn and act with some kind of intelligence. AI systems can analyze structured and unstructured data from different sources, interpret the patterns and deliver better insights from the data. In general AI is applicable in areas like expert systems, Natural language processing, intelligent robots, Speech recognition, Handwriting recognition, Visual processing Applications, language translation.AI can also be used to automate back office and middle office and mundane tasks, it helps in processing behavioral information to understand the day in day out activities of individuals and accordingly provide recommendations

It is very interesting to know that AI started with Narrow AI which is a machine skilled at one specific task then moved to general AI which are machines with the human level sense and perform a set of task and then the super intelligent AI which are machines smarter than the human and have greater wisdom and social skills. Slowly algorithms were created to help the AI engine learn which also had the capability of getting trained, these AI engine can do a particular task in a specific manner and this gave rise to the machine learning. In machine learning, algorithms are created which helps the AI engine learn from large amount of data. Thus enabling the AI engine to respond according to the situation. These application work brilliantly in object, speech, facial recognition. E.g.: IBM Deep Blue, Google’s DeepMind .Deep machine learning is a further improved version of the machine learning wherein the AI solves real-life problems by using the technique of neural networks .E.g.: Driverless cars

In the insurance context, AI is applicable in diverse phases of the insurance life cycle. Right from getting the quote, interacting with customers, submitting applications to policy servicing. It becomes relevant throughout the insurance value chain. Use cases implemented on AI - can provide cost effective advises to customers, application processing and support acting both as intermediaries and underwriters right from submission until the policy issuance, improved responsive machine for applications processing and policy servicing which also handle voice, visual interactions with the customers, better and quicker claims handling mechanism, also used in fraud detection.

Only when we compare and contrast the traditional setup and the digital enabled AI based setup, we can rightly identify the pros and cons of the 2 setups. Let’s pick the customer care unit use case. In the traditional setup – A customer will call the customer care unit, the executive working in this unit will receive the call and register the complaint/provide resolution for a problem, a service request will be raised and the solution is provided .This service request might have several stakeholders involved to provide resolution.

In the case of AI enabled customer care – There are two scenarios possible; Conversational bot or cyborg where a human works along with the computer to address the issue. When the conversational bot comes into picture customer directly interacts with bots, response is much quicker and the turnaround time is also reduced. E.g.: China Merchant bank has a webchat bot which handles 1.5 to 2 million customer conversations per day is a conversational bot. KLM flag carrier airlines of Netherlands which responds to customer enquires via Facebook messenger is an example for cyborg.

The AI enabled customer care reduced the customer handling time to 10 % or more, showed a reduction in escalation, faster response and 24*7 availability. Want to tailor an AI driven customer journey for your business? Now is the time for action.

Author: Mark Antony , Social Media Marketing Intern