Hello everyone, this is Praveen. Today my topic of discussion is how artificial intelligence and machine learning are creating transformative impact in the life and nanotech space and how these life insurance companies are using these two advanced technologies in predicting consumer behavior. And also using different data and doing consumer analysis. So without any further delay, let's get started So first, let's talk about what is life and annuities industry So the life and annuities industry is a significant component of the u. s economy and also the u. s financial sector Which actually provides long term risk management services and also investment options to the consumers So in this way, the consumers can actually invest their own money into these life insurance policies or different indexes. And after a certain period of time, they get investment returns. Which in turn they can plan for different reasons. It might be retirement or it might be For some college tuition expenses for the decades or for a different set of reasons So they have different plans which are actually sponsored by these life insurance companies And the consumers can actually invest their money Spying these policies and investing into stocks and indexes which are tied to this particular policies what are the critical areas of a life and energy space? So there are two different areas, which are very critical in terms of Directly impacting the operational efficiency and customer loyalty of a particular process in a life insurance company So the first day is the claims processing. The second day is the customer experience. So these two are the two critical areas which have a direct impact on the operational efficiency and customer loyalty of a particular life and entities carrier or life insurance company, within the U S so our next topic is, what is the role of artificial intelligence and machine learning in modernizing insurance? So artificial intelligence and machine learning are reshaping the L& A space. by automating so much of, repetitive tasks, which requires human interactions in the day to day operational processing of different patterns of data and predictive analytics. So by using of AI and ML, we are having to achieve an operational efficiency by automating so much of tasks, which actually. It helps these industries in optimizing the process, removing any manual and human intervention. And also it also helps in detecting anomalies and analyzing customer behavior. So this is, this actually will cause, a different approach of how we have the perspective of looking at particular things, right? So by introduction of these two technologies. The shift is changing from being a reactive process, right? Instead of being respond, instead of responding to something will be turned on the proactive mode of management, which is basically it enables a more responsive insurance model and these two advanced technologies, which is artificial intelligence and machine learning also helps insurers and. to optimize their operations and create personalized user experience, thereby enhancing both efficiency and customer satisfaction. let's take a case of claims processing wherein we used artificial intelligence and machine learning to do automation and efficiency. there are different tasks or there are different steps in the claims management process, right? once after you get a claim, you are, you have to, Go through a lot of documentation, which is received on the claim, right? And you have to review the documentation for accuracy You have to review the documentation to see if there is any fraud involved and you also need to do a different types of data analysis and Check to make sure that whatever information is received on a claim is correct before it is processed, right? So all these different steps, right the step one is basically We can automate all these four different steps of claims processing using artificial intelligence and machine learning. So first, the first step is we can actually automate the whole document handling, meaning the AI will actually extract and processes information from claims documents, which will actually reduce the, excuse me, the manual data entry. The second step is anomaly detection. So the machine learning models Are very efficient in identifying patterns that may indicate that if it is a fraudulent claim or it's actually a genuine claim, if it's a fraud fraudulent claim, then it allows insurance companies to take early intervention steps from being from the claim being processed and the money going out of the door and then predictive analytics. So artificial intelligence also predicts claims resolution times, right? Because we have different requirements For different states and how we process these claims. So by using the automation and AI We can make sure that all these requirements are met enabling efficient allocation of claims processing within a stipulated period of time thereby increasing consumer satisfaction and then the impact right the faster the claims resolution is the lower we have overhead cost or operational cost because We are if the process is all automated and then there is also reduced risk of fraud and you know the And there is also improved customer experience because of all the efficiencies which we gain By using the ai and machine learning models, you know in the claims processing for a particular life and energy company In this way it directly impacts the consumer satisfaction thereby improving customer loyalty for a particular company. Okay, how do we use? Ai and ml in identifying consumer behavior analysis, right? So it is actually used across four different topics. One is the data analysis Second is the proactive engagement And Third is the personalized offerings and fourth is the result how do we use the data what we get from the customer, right? So the machine learning algorithms what they do is like they analyze large data sets to identify What is the pattern of consumers? What they are wanting to purchase the different customer preferences and the behavior patterns, right? So this will actually help all the life and analytic Companies who are using this machine learning and artificial intelligent tools to predict consumer behavior you know, this actually helps them to tailor insurance products and Go to market strategies to make sure that they are meeting individual customer needs And this also in fact That turns up into the proactive engagement of the companies because all this is predictive analytics, right? This is all predictive in nature. So all the insights, what we receive from the data processing using the algorithms actually help the insurance companies anticipate and address potential customer issues even before they get, escalated. And as a result of all this, we see that there is an increased customer satisfaction. we have better retention rates and higher conversion of the personalized product, offerings from the customers to the company, ultimately leading to an increase in sales and customer loyalty for a particular insurance company from the customers. So what are the benefits and challenges of, Adoption of AI or ML, right? So the benefits are as we know as we already spoke about It actually enhances operational capability. It improves operational efficiency in a particular process by automation and the accuracy of the claims processing and the fraud detection is also improved because We are using artificial intelligence and machine learning In detecting and reviewing all these incoming documents and information Thereby if there is any fraud detected or if there is anomaly, which is detected We can it gets reported immediately and the insurance company can take intervention steps as a precautionary measure in being productive in nature rather than being reactive And it also elevates customer satisfaction because we are creating products which are custom made or which are mostly here Tailored products for customer needs and behavior patterns. So these are the main different benefits what we achieve by using artificial intelligence and machine learning and adopting them, in predicting customer behavior and patterns. So what are the challenges? So the data privacy concerns and compliance with different regulations is a key challenge because the way we accept data, the way we gather data, the way we process data, so these are all driven by so many regulations. So we have to make sure that all the data privacy concerns are up to the mark, and we are following all the state regulated or the federal regulated, policies or standards for the process and gather data. And it's always a challenge because whenever we introduce a new technology in an existing ecosystem, there are a large legacy systems dependencies, which we have to take care before we get introduced to this new technologies. Because. The solutioning of these new technologies and integration with legacy systems might be a big challenge because there might be huge chunks of data which are actually processed through these legacy systems. And since they are built and these are age old systems, we have to make sure that, all these systems are working from the same component or the same integration plane. So that there is smoother processing of data. And, as we all know, another big challenge is finding talent who can actually support the AI or ML technologies in this market is a little bit, challenging because, these are new technologies and finding resources with ample experiences who, skilled people. To develop deploy and manage these technologies will be a true challenge let's now look at few industry case studies So i'll be walking you guys through a few examples. The first example is there was this particular company a which actually implemented Ai in their claims processing system and it outrightly You know reduce the claims processing system by 60 percent and it also decreased the fraud losses by 30 percent Something similar, excuse me. Something similar is was used at company B Where we used machine language to analyze the customer data and personalize their offerings resulting in a 20 percent increase in policy renewals So the key takeaway here is these are real world examples, which are highlighting the importance of AI and ML You And their potential to deliver the measurable business outcomes for a particular insurance company or a particular insurance carrier So let's talk about the road map of ai or ml integration and life insurance. So before we actually have a Have an idea of implementing Artificial intelligence and machine learning in a particular company. I would think we need to go through four different phases So phase one is basically assessment and strategy development and Phase two is what I call pilot projects. Phase three is full scale implementation. Phase four is continuous optimization. So in the phase one, we need to identify what kind of artificial intelligence and machine learning use cases We need to implement these models so that, these are aligned with the business goals of a particular company. In the phase two, we might have to pick a small sample from the identified use cases across different capabilities within a company in order to validate effectiveness. And, we have to make adjustments because this is more of a proof of concept model before we go full scale, right? So the phase two for me is more of a proof of concept model, where we have to pick a particular couple of business use cases, and then we have to make sure that we are building the artificial AI or ML models, and we have to deploy them to see if it works and if they are aligned with the business objectives of the company. So once that POC is successful, then we move on to phase three, which is the full scale implementation. So now the full scale implementation is going big bang, right? So we have to roll out the AI or ML across the organization. We have to integrate this with different systems, the core insurance processing systems, and there's the big part of the big bang approach or the full scale implementation is a training aspect of it. So training the people to use this AI or ML is a key aspect because even though we have the systems in place. If the people or the resources who are using the systems are not skilled enough, are not trained enough, then I think we can't really get the efficiencies out of these systems. And the phase four is what we call as continuous optimization. So we have to make sure that these models or the AI or ML models are trained regularly, refined regularly, all these algorithms are clarified and refined regularly. In that way, it gives us the scalability to expand the AI or ML. As technology advances and we gather more data So these are the four different steps wherein we integrate AI or ML in the life insurance company So what are the future trends and opportunities? So I think there are a couple of topics which are very interesting here to talk about One is what I call is conversational AI, right? So we can leverage AI powered chatbots for customer services To resolve queries quickly and efficiently So gone are those days wherein people have to wait For the customer, consumer hours, right? To call into the call center from nine to five or the regular business hours today by using AI. The chatbot is available 24 by 7 and 95 percent of the time all the questions are answered using the chatbot and it's very user friendly. And it's, since it's AI powered, the customer service factor also increases because they, all the customers who are using this chatbots are getting their answers immediately without having to actually wait to speak to a person. And real time risk assessment. So we can use AI to assess risks on the fly and adjust the policies dynamically. Thank you. And AI underwriting, AI plays a big part today in automating the underwriting process, which in turn is reducing the turnaround times to issue a particular life insurance policy. And how can we expand AI or ML applications? We can continue to explore new use cases, such as dynamic pricing or consumer sentiment analysis and intelligent automation. In that way, we can actually predict consumer behavior and we can create products which are tailored to consumer needs and preferences. So just to conclude about what we have talked about so far, the artificial intelligence and machine learning are in fact creating waves in life and analytics industry, addressing the longstanding challenges while creating new opportunities for growth and innovation. Especially in the claims processing, the companies, the life of nanotech carriers can significantly improve operational efficiency, they can reduce the cost and mitigate fraud. On the other hand, AI and ML's impact on consumer behavior analysis is transforming the way insurers engage with their customers. By leveraging this advanced data insights and data analytics. The companies can gain a deeper understanding into what the customers are wanting, what are their preferences and what are the needs enabling them to create personalized products. This proactive approach leads to be better customer retention, higher satisfaction, stronger brand loyalty, and also will place a particular life insurance company to be the market leader for decades. thank you for giving me this opportunity to talk to you guys. If you have any questions, please email me on my email address mentioned. Thank you.