Hello, everyone. My name is Praveen Kumar, and I'm currently working as Director of Business and Product Readiness with a large life insurance company here in North America. So my topic of discussion today is how artificial intelligence and machine learning are creating efficiencies and creating a transformative impact in predicting consumer behavior analysis and also how the claims are efficiently processed using this New latest UpToDate technologies like AI and machine learning. So without further delay, let's get onto the agenda of our discussion. So moving on to the next slide. As you can see here. We have quite some agenda to cover today during our conference topic. I would first start with providing you guys some background on the industry context and challenges we have today in the life and energy space. And then we will discuss the power of AI and ML in claims processing, how the latest technologies from AI are providing and creating efficiencies in processing of claims, and then we will also discuss the impact on operational efficiency. And how A. I. And M. L. Are used in predicting the customer behavior before or after a policy is purchased. We would then move on to talk about the customer satisfaction and some key benefits of machine learning and artificial intelligence in the life and energy space. And to close the topic, we will also discuss a few case studies and the road map of artificial intelligence and A. I. Adoption for different life and energy companies. So without further delay, let's start with the industry context. So as you guys know, today, we have three different areas in the whole life and annuity ecosystem, which are most critical. One is the claims processing. Secondly is the consumer behavior or predictive analytics used for consumer behavior. The third is the cost of insurance, right? So as you can see, claim is a very important aspect of any life and annuities value chain. So the current claims process often involves a lot of manual tasks and there are multiple layers of review before a claim is actually paid or, a claim is already approved, right? Once it is issued by the company and, due to this lot of manual effort, there are delays, there might be errors because there's a lot of human, human, factor involved in the processing of claims, which would ultimately is leading to customer dissatisfaction. second topic, what we will be discussing in detail is the consumer behavior insights, right? So the taste of consumer or, the choice of consumer in selecting an insurance company or a policy is quickly changing, right? What they expect and what they don't expect, what are their preferences, right? All these are different, very important data points for a particular company to acquire a consumer. And then the third topic is the cost pressures, right? So the cost of insurance is nothing but the cost of a particular company to acquire a consumer, right? To issue a policy or to sell a policy. to make sure that the admin costs are in control And to make sure that all the operation costs and overhead costs are not, Burdening into the policy of the consumer which is purchasing, right? So to basically deal with these three challenges We can, we want to use AI and machine learning to understand the behavior of the consumer. In that way, we can reduce the complexity in claims processing. We can gather more consumer insights and predict consumer behavior much efficiently. And we can also bring down the cost of insurance. So moving on to the next slide, so what is the role of AI and ML in claims processing? So today, as you can see, most of the, most of the claims processing work, like documentation, getting, evidences, Validating the information, all this is actually done majority of the times using a manual intervention from humans, right? So what AI can do in this particular process? in this particular process, we can actually automate the whole process of receiving documentation, extracting the documentation for a particular claim, validate the information, and route it for approval. the whole process from claim receivable to claim, approval can be automated using AI. And what these, AI helps, because these large machine learning, algorithms, right? which actually worked behind the scenes along with AI, they detect different patterns, in claims data, to see if there are any fraud cases, if there are any inconsistencies, right? and they would automatically flag suspicious cases for human review. So there is never a complete 100 percent automation, but there is always a human factor involved along with collaborative AI, along with machine learning to make sure that, we also identify and detect fraud, predictive analytics, right? So also the AI algorithms. They predict claim outcomes based on the historical data of a particular policy. For example, if we issue a life insurance policy for a particular person who is in an age group of, a senior age group, right? So based on his historical medical data, the AI or machine learning can predict what kind of decision making and improvements Can occur in the processing of claims, once we receive a claim with the insurance company. So basically the AI and machine learning are very playing a very key important role in anomaly detection, in predicting analytics and also in automated document handling. So let's move on to the next slide. Okay. So how does AI and machine learning are creating impacts and operational efficiency, right? So they basically reduce manual intervention, meaning wherever there is a human factor involved, right? Like data entry or document classification and claim assessment. All of this can be actually automated using AI. And by doing this, we actually have benefits of getting few errors and the processing is much faster. And in a way, by implementation of AI in the claims processing, the life insurance and annuity companies can save money, which they actually spend on operations, which they actually spend on different overhead cost of, hiring people to do stuff like document classification and data entry. And this is actually leading to an overall reduction in the cost of claims handling. So basically, and it also helps the companies to fight against fraud mitigation, right? Because AI's ability to detect anomalies in real time means that the fraud can be caught earlier. So basically, as you can see on this slide, we have three important key benefits on operational processing of any company by using AI, which is nothing but it reduces manual intervention. And it gives us cost savings and it also helps us in, fighting with fraud claims and, detect anomalies in real time very much earlier in the process than towards the end of the process. Okay. AI and ML and consumer behavior analysis. So how do we use AI to drive and understand the nature of consumer behavior, right? So basically AI actually helps us with three important factors, what I call as pattern recognition. Personalization and proactive engagement. So what is pattern recognition? So whenever there is a change in consumer behavior, right? So the data, there are different data points because the AI processes, large volumes of data, it will be easily able to identify trends and what kind of buying behavior and patterns the consumer is shifting to, right? And what are the different payment patterns and product differences the consumer is actually asking for. So all these, the AI would be able to leverage the information, existing information and future information, and create data points which will help us in creating a pattern or a trend analysis. In that way, we can clearly see that, this is a consumer behavior trend from, Point A in time to point B in time how the tastes have changed and how the preferences have changed towards, Purchasing of a particular policy or towards purchasing a particular product and then with the help of these data points What we can do is, as life insurance companies have these data points for review and analysis, right? And they should be able to personalize the products, what they built in that way, the consumers will be easily able to pick the right products for them. And it also helps to bring the cost of insurance down because now they don't have to spend too much of money on marketing or to acquire a consumer because most of these products, depending on the data insights, what we achieve from pattern recognition, it will help us. Customize the products for consumers in such a way that you know, it's catering to a mass market at the same time making sure that you know we are taking into account their individual preferences and different data points proactive engagement So this also helps us in predicting the proactive engagement and future needs from consumers, you know How the consumer would predict five years from now, how the consumer trend would change 10 years from now So all this, you know will be done using ai and machine learning algorithms Which the system will automatically be able to recognize using different data points and trends Based on the historical data and consumer behavior So that is how we use AI and ML in predicting consumer behavior analysis. So moving on to the next slide, which basically speaks about customer satisfaction. So simple thing, we have three main points for increasing customer satisfaction. Whenever we create a product, whenever we create a service, it should be customer centric and customer focused. So by using AI and machine learning, we make sure that we hit three main important nodes of customer satisfaction. One is we are creating a tailored product, right? Because AI will allow the companies to design personalized policies, which will reflect their unique preferences and needs and financial situations. And it will also improve the engagement with customers because AI can offer solutions 24 by seven with customer support like chatbots And if in case the customer has some sort of inquiries to make, he doesn't have to wait the next business day to talk to a person, but instead, talk to a bot, chat bot or a virtual assistant and get responses accurately immediately. And then we can also improve increased retention. So by delivering more relevant and personalized services. The companies can enhance customer satisfaction Thereby leading to higher retention rates and lower churn factor. So moving on to the next slide So what are the key benefits of ai and ml integration? So the key benefits are basically, it helps us, as I spoke earlier, with efficiency gains, right? Because we are automating much of this process and claims and underwriting and fraud detection. Everything is more streamlined now. the operations processes are more streamlined and much more faster and more efficient. Fraud prevention. So the real time anomaly detection powered by AI will significantly reduce the risk of fraud because it will help us detect fraud much earlier in the game than towards end of the process. Thereby safeguarding the company's financial interest and most of the decision making is data driven, right? So we use something called as predictive analytics, which actually improves decision making by forecasting consumer needs, risks, and trends based on the historical data. So these are the three different benefits of, using artificial intelligence and machine learning integration. So moving on to the next, which is basically case studies and success stories, right? So one of the case studies is at a large life insurance company. I would not like to take names, but, there was an AI driven claims automation system, which was actually implemented. And then, looking at the metrics and looking at the tangibility of this, implementation of system of using AI, right? They were able to reduce the claims processing time by almost 40%. Okay. So let's say if before the AI implementation automation system was implemented, if the claims processing time took like almost two days, it was actually drastically reduced to few days to process a claim after an AI automation system was implemented. Same thing at a different company, AI was used to build personalized product recommendations based on utilizing the last customer historical data, including the payment history, the demographics and purchasing patterns. So by doing this, they were able to personalize and create new product offerings. And then the company saw a 20 percent increase in customer retention. So what is the road? So this is very important for us. So what is the roadmap for AI and ML adoption? Because even before we get started, we need to understand what is the key benefit for the need of implementing an AI in a particular process or a company, right? So there are different steps, as you can see, first, we need to identify the key areas for AI implementation. So we have to start with something called as claims processing and customer analytics, where the highest impact can be achieved. And then we have to develop a solid data strategy, right? Because any AI model or ML, machine learning model, is as good as the data, right? Because, and we have to make sure that the right data is collected and managed, right? And it needs to follow a compliant and ethical manner to make sure that we are powering the, AI or ML tools, in a compliant manner And then the step three is creating or building AI or ML capabilities, right? Whether it might be in house development or we can partner with so many vendors outside in the market Who actually provide us this AI services, right? We can build the necessary AI or automation Into a particular company. And then the fourth step is continuous improvement, right? So any AI system must be monitored, tested, and optimized regularly to make sure that they are delivering the desired results and adapt to changing needs. According to the business objective of our company, it should be parallel and supporting the company's business objective of implementing an AI into a particular system. So these are the four different steps for four basic steps, In general, what we need to understand before actually, implementing an AI model in a particular process or a company and in conclusion. So I know we spoke a lot about, artificial intelligence and machine learning, so to my, to my, experience and so far the data, what I've seen in the market, The artificial intelligence and machine learning, it actually represents a very big transformative opportunity for insurers, to streamline their operations. And to provide a more personalized and efficient customer experience So by automating key processes like claims handling and leveraging data driven insights, we can create and predict consumer behavior We can create personalized products and we can reduce operation costs, you know improve fraud detection and overall, improve the customer satisfaction service delivery. So these are some of the key, benefits of using AI and machine learning in the life and annuity space. thank you for giving me this opportunity, for presenting and the transformative impact of, artificial intelligence and machine learning in life Thank you, everyone.