Revolutionizing Underwriting: From Data Analysis to Risk Insights 

Traditionally, underwriting involved laborious manual analysis of diverse data sources, such as financial records, medical histories, and even social media accounts. However, AI is revolutionizing this process by rapidly analyzing vast volumes of data with unparalleled accuracy. Consider the case of an insurance company that utilized AI algorithms to analyze customer data and discovered a surprising trend: individuals with active gym memberships had significantly lower health risks and filed fewer claims. This innovative approach, backed by AI insights, enabled the company to provide tailored policies to health-conscious individuals, rewarding their dedication with lower premiums. In a study by McKinsey, it was found that AI-powered underwriting algorithms could reduce the claims payout time by up to 30%, leading to improved customer satisfaction. 

Streamlining Claims Processing: Speed, Accuracy, and Customer Satisfaction 

Claims processing is a pivotal area where AI is making significant strides. Insurance companies receive a massive influx of claims, and manual processing can be time-consuming, error-prone, and costly. Enter AI automation, which can dramatically expedite the claims process while ensuring accuracy and detecting fraudulent activities. Let’s consider an example: an insurance company integrated AI-powered image recognition algorithms into its claims processing system. This innovative approach allowed them to swiftly assess vehicle damage through uploaded pictures, enabling faster claim settlements and improving the overall customer experience.  

Personalized Policies: Catering to Individual Needs 

Gone are the days of one-size-fits-all insurance policies. AI’s ability to analyze vast datasets enables insurance companies to craft personalized insurance products and services that cater to individual needs and risk profiles. Imagine an AI-driven insurance platform that analyzes an individual’s travel history, social media activity, and preferences. Using this data, the platform recommends tailored travel insurance coverage with benefits specific to the traveler’s preferences and the risks associated with their intended destinations. By offering personalized policies, insurers can establish deeper connections with their customers, enhancing loyalty and satisfaction.  

AI’s Potential in Risk Assessment and Fraud Detection 

Harnessing AI technology in the insurance industry presents exciting opportunities for improving risk assessment and fraud detection. With its ability to analyze vast amounts of data and identify patterns, AI can revolutionize these critical areas. Consider the following possibilities: 

• Advanced Risk Assessment: AI algorithms can analyze diverse data sources to provide insurers with more accurate risk assessments, enabling them to make informed underwriting decisions and set appropriate pricing. 
• Fraud Detection: AI-powered analytics can detect anomalies and patterns in data that may indicate fraudulent activities, helping insurers proactively identify and prevent fraud. 

• Continuous Learning: AI systems can continuously learn and adapt, allowing insurers to stay ahead of emerging fraud schemes and protect their bottom line. 
• Cost Savings: Implementing AI-based fraud detection systems can lead to substantial cost savings by reducing fraudulent claims and improving overall operational efficiency. 

Addressing Challenges and Ethical Considerations 

While AI brings immense potential, it is crucial to acknowledge and address the challenges it poses. One significant concern is the potential bias that can arise from the data and algorithms used. Insurance companies must ensure transparency, fairness, and inclusivity in their AI models to avoid discriminatory outcomes. Moreover, the displacement of certain jobs due to automation necessitates retraining programs to equip workers with new skills for emerging roles in the evolving landscape. 

Conclusion 

Artificial intelligence is undoubtedly reshaping the insurance industry, offering unprecedented opportunities for growth, efficiency, and customer satisfaction. Through AI-driven underwriting, streamlined claims processing, and personalized insurance offerings, companies can unlock new levels of productivity and cater to individual needs like never before. By embracing AI responsibly, insurance companies can navigate potential challenges and create a future where AI is harnessed ethically, benefiting both the industry and its customers. As AI continues to evolve, it promises to be an integral force shaping the future course of the insurance industry.  

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Unleashing the Power of Advanced Generative Models

At the heart of Bard’s capabilities lies its sophisticated generative model, built on advanced techniques such as transformer-based architectures and deep learning. These state-of-the-art models enable Bard to comprehend natural language, discern context, and generate human-like responses. This breakthrough in generative AI has propelled Bard to the forefront of conversational assistants, as it enables dynamic and engaging interactions with users.

Embracing a New Era of Interactions 

The advent of Bard brings substantial benefits to both businesses and users alike. For businesses, Bard offers the opportunity to elevate customer experiences through enhanced engagement and personalized interactions. Research indicates that companies leveraging AI-driven virtual assistants witness an average 15% reduction in customer support costs. Furthermore, Bard’s ability to handle complex queries and provide detailed information empowers businesses to streamline their operations and provide superior services, resulting in a significant boost in operational efficiency.

Paving the Way for Future Innovations

As Bard continues to evolve, the future of conversational AI looks promising. According to industry forecasts, the global conversational AI market is projected to reach a value of $17.2 billion by 2027, with a compound annual growth rate (CAGR) of 30.2%. This growth signifies the increasing demand for advanced conversational AI solutions like Bard, as industries recognize the transformative impact of human-like interactions on customer satisfaction and business outcomes. With ongoing advancements in language models and research, Bard will play a pivotal role in shaping the future of conversational AI, revolutionizing the way we engage with technology.

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The Role of Artificial Intelligence in Underwriting, Claims Processing, and Personalization in the Insurance Industry 

Underwriting and risk assessment are two of the major uses of AI in the insurance sector. Underwriting is the act of figuring out the right price for coverage and evaluating the risk of insuring a specific person or entity. This procedure has historically relied on manual analysis of data from sources including social media accounts, financial records, and medical records. AI, on the other hand, can examine this data more quickly and precisely and spot trends that can point to a larger or lower likelihood of a claim being filed. This can assist insurance businesses in managing their risk exposure and making better judgements about pricing and coverage. 

AI can also be used to automate claims processing, which is a key area of focus for many insurance companies. Insurance companies receive a large number of claims, and processing these claims can be time-consuming and costly. AI can be used to automate parts of the claims process, such as identifying fraudulent claims or determining the appropriate payout amount for a valid claim. This can help insurance companies to reduce the time and cost associated with claims processing, and to improve the customer experience by providing quicker and more accurate settlements. 

In addition to underwriting and claims processing, AI can also be used to personalize insurance products and services. For example, insurance companies could use AI to analyze a customer’s data and make recommendations for coverage that is tailored to the customer’s specific needs and risk profile. This could involve analyzing data such as the customer’s age, medical history, and lifestyle to determine the most appropriate coverage. Personalized insurance products and services can help insurance companies to differentiate themselves in a competitive market and to build stronger relationships with their customers. 

In the insurance sector, using AI has a variety of potential advantages. AI can assist insurance firms with improved risk management and more informed decision-making in addition to increasing efficiency and lowering expenses. Insurance firms may be able to provide more individualized goods and services as a result, which may increase client retention and satisfaction. 

Challenges of Using Artificial Intelligence in the Insurance Industry 

However, there are also some challenges and potential drawbacks to the use of AI in the insurance industry. One concern is that AI may be biased, either in the data that it is trained on or in the algorithms that are used to analyze that data. This could result in unfair treatment of certain individuals or groups, and could potentially lead to legal or regulatory issues. Another challenge is that AI may displace certain jobs within the insurance industry, as certain tasks are automated. This could impact the employment prospects of workers in the sector and may require the retraining of some workers in order to adapt to new roles. 

Overall, it is evident that AI will play a big role in determining how the insurance sector develops. It might increase productivity, cut expenses, and boost customer satisfaction. To make sure that AI is utilized ethically and responsibly, insurance companies must take into account all of the potential risks and difficulties that could arise from its use. It is anticipated that as AI use develops, it will become a more substantial component of the insurance sector and significantly influence the industry’s future course.

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What exactly is Hyper Intelligent Automation?

In a nutshell, Hyper Intelligent Automation (HIA) is the combination of a number of next-generation technologies, including Artificial Intelligence (AI), Robotic Process Automation (RPA), Intelligent Document Processing (IDP), and Process Mining, which enable businesses to improve their ability to automate workflows and achieve better business outcomes in less time. Where the terms Hyper Intelligent Automation and Hyper Automation are interchangeable. According to industry estimates, the market for automation technologies like RPA will reach $5 billion by 2024, growing at a 20 percent annual pace. HIA has been labeled a “strategic business game-changer” because it drastically decreased the need for human intervention in existing operations. It has brought in the fourth industrial revolution, significantly altering the digital world’s core characteristics. And has the potential to revolutionize customer experiences by reducing outcome variability and improving business decisions that lead to innovation. While this era is all about customer-centric businesses, companies that want to create a unified, aligned workplace must adopt intelligent automation that is integrated and linked with a deep understanding of processes.

Before we get into the significance of HIA. Let’s take a quick glance at the technical terms that are being discussed.

• Automation in general

Automation is a term that refers to technological applications that automate operations and processes to eliminate human involvement.

• Robotic Process Automation

RPA stands for robotic process automation, which promotes uniformity and transparency in corporate processes. Due to pre-programmed scripts and APIs, RPA solutions can perform repetitive manipulations or handle structured data inputs. Even the most basic RPA systems can save employers a lot of time and effort.

• Intelligent Automation

Intelligent Automation (IA) is a level up from RPA software that is based on rules. Intelligent automation technology, which is powered by machine learning and artificial intelligence (AI), can perform a broader range of tasks that require baseline analytics and conditioning logic.

Why Hyper Intelligent Automation?

When RPA and AI work together to allow applications that go beyond the mundane to the innovative, the power of intelligent automation shines through, from data collection and processing to analysis and contextual decision-making. According to a Harvard Business Review poll, 48% of respondents say they haven’t considered or implemented an AI-based intelligent automation plan. Another 36% are implementing artificial intelligence into their company strategy, but not on a major scale. Currently, just 11% of firms are embracing AI-based solutions.

In an era of unparalleled disruptions, organizations are turning to Hyper Intelligent Automation (HIA) to achieve antifragility and short-circuit digital transformation. HIA enables seamless customer experiences, optimizes workflows, and drives business outcomes with a shorter turnaround time. In fact, according to recent Zinnov research, 75% of Fortune 250 companies have already made considerable investments in HIA. Furthermore, both federal institutions and small and medium companies (SMBs) are paying close attention in order to generate significant value in the next years. Hyper automation’s capacity to loop humans into the process is one of its primary differentiators. Technology and humans collaborate via collaborative intelligence. Employees can now start learning how to use automation and other software where they can achieve AI-assisted decision-making through machine learning. Companies can begin to reinvent labour that is normally done by employees using hyper automation.

The future is HIA

Hyper Intelligent Automation (HIA) is the new strategic enterprise game changer that aids in the effective attainment of every business goal a company wishes to reach, such as cost savings, time savings, improved employee and customer happiness, and increased efficiency. During the period 2020-2027, the global Hyper intelligent Automation market is expected to grow at a CAGR of 18.9%, with increasing digitization of traditional industrial plants driving growth. According to estimates, hyper intelligent automation would have a global market worth of $9.98 billion by 2022.

Healthcare, insurance, travel, and tourism, as well as the government, are among the industries most likely to be disrupted by such technologies. These industries have a lot of different legacy systems, a lot of intermediate players, and processes, and they all need some kind of intelligent cognitive input for decision-making and delivery. These features combined make these industries very appealing for Hyper Intelligent Automation.

Hyper Intelligent Automation has tremendous potential as digital twins can solve problems that humans couldn’t solve yesterday. Automation technologies will expand exponentially, just as the complexity of electronic circuits has doubled in a given length of time (Moore’s Law). We anticipate a similar merger in the future, as improvements in many technologies ended up complimenting each other and generating hyper automation. Now it’s time for your business to evolve too! With the world heading toward digitization, Hyper-Intelligent Automation solutions based on technologies such as RPA, AI, and others have the ability to make your business processes smarter and keep you ahead of the competition.

About Experion

Black Rock IT Solutions is a global IT solutions provider with strong Product Engineering DNA and an unwavering focus on delivering Customer Value. blackrock has been recognized for its core expertise in digital product engineering services, that drive new revenue streams, digitize business processes, and help improve operational efficiency and productivity. As a digital transformation service provider to 350+ global customers across 35 countries, blackrock has helped its customers to transmute their businesses from within, become more future-aligned, and successful. To know more please visit: Data & Analytics – Black Rock IT Solutions (blackrockdxb.com)

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All great cultures are empowered and metamorphosed by the wisdom of generational planning. Native Americans, for instance, believed that one must act keeping in mind the welfare of the next seven generations following their time: a tenet of homeopathic practice and avowed even today by modern science. These cultures understood the pitfalls of analytical myopia and the need to think ahead of time to realize subsistence and more.

To dilettantes with only a passable knowledge of this subject, the term ‘predictive genomics’ may seem obsolete since all endeavors in the field of genetics are predictive in their intent. However, it is a discipline at the crossroads of various fields like personal genomics, phenology, and bioinformatics alongside many more. Its relevance lies in the understanding of the human phenotype (expressed traits in an individual) as a function of the person’s genotype and his/her environment and being able to integrate the two variables of the function in a population for generations using advanced computational modalities like AI and Machine Learning.

Much of today’s predictive genomics is a result of Genome wide Association studies (these studies were possible due to the rise of biobanks, safekeeps of data sets demonstrating great genomic and trait variation) done over the course of the last two decades, occluding further doubts on the inheritance of complex traits, narrowing them down to the thousands of genetic mutations that are known as SNPs (commonly pronounced ‘snips’). These SNPs are the fundamental unit of biological evolution of all organisms, according to neo-Darwinian theory; mutations in their myriad iterations can make or break a species in the long run as they can be at the core of the inheritance of new survival traits and speciation or the accumulation of traits that lead to extinction. They are random and continually occur around us.

Predating these discoveries, the human genome project was completed, about 20 years ago, bequeathing scientists with a huge amount of data; experts estimate that research in genomics could yield up to 40 exabytes of data. With such a staggering volume of complex data to navigate, AI and ML techniques become obvious candidates for enhancing the efficiency and accuracy of predictive genomics.

Today, pharmacogenetics powered by deep learning algorithms make for an excellent use-case of digitally powered predictive genomics. A study that seeks the correlation between drug response and the individual’s genotype, its research labs sequence and genotype millions of people to understand drug delivery and inhibition across different populations. Such data is groundbreaking in demonstrating the relevance of individual gene expression in the efficacy of various medicines.

Consider the gene CYP2D6, expressed primarily in the liver and is crucial for the metabolization of codeine, one of America’s most popular pain relievers. The gene codes for the synthesis of proteins that converts xenobiotics like codeine to water-soluble morphine through demethylation; it is worthy of note that codeine has no analgesic action by itself. Now, we know through statistical analysis that 1-5% of Americans is poor metabolizers of codeine to the point it elicits no response, while another 1-21% are ultra-metabolizers, leading to huge spikes in blood morphine levels but effective for a very short interval of time. Similar trends can be noted for hundreds of drugs as they are all metabolized by CYP2D6, with its 161+ recognized haplotypes(alleles) dictating the effectiveness of these drugs.

Today, we are struggling to fully comprehend the effects of these known gene combinations, let alone those of the numerous rare haplotypes formed through mutations (SNPs) found across the world further convoluted by, ironically, the uniformity of variation across ethnicities. it is however important as predicting the functions of these novel alleles is key to improving the drug responses of these patients.

It is at this juncture that deep learning becomes relevant as the next machine learning paradigm. By implementing techniques that combine CNN image analysis and transfer learning, we can build deep neural networks that can generate voluminous sequences with known variations spiked into these sequences. Once we generate scores for each allele, we can train a model to assign these scores, forcing the CNN to ‘learn’ key sequence features. The experimental data on rare variations can be used to refine the final layers of the network, which can then predict and assess the outcomes of using codeine in individuals with rare alleles of CYP2D6.

Looking ahead

One must not overlook the fact that ML in genomics is in its infancy; a field that is less than a decade old. The reason is that the 3-D relationships shared by genes are much more complex than pixels and their interactions; as mentioned earlier, image recognition and analysis have great potential in the ML world. Today, breakthroughs in research have led to the union of both these techniques: ML devices like Deep Gestalt can accurately diagnose( up to 91%) over 200 disorders through image analyses by observing facial phenotypic manifestations of genetic aberration(note that the utilization AI/ML in genomics corresponds to deep learning and that remains so for the scope of this discussion, as well).

Deep learning neural networks are of 4 types broadly and they use different inputs; while fully connected networks use k-mer match metrics, convolutional and recurrent networks can use DNA sequence data, as well as image and time-series measurements respectively. The fourth type, graph convolutional, utilizes protein-to-protein interaction and structure. These are often manifest as modalities that can identify sequence context features capable of predicting transcription factor binding, decoded networks revealing differential gene expression, and more.

Applying Deep Learning to arrive at this profound understanding of gene mutations can be pivotal in understanding the origins of tumors and the development of gene therapy for cancer prevention and the prevention and treatment of many genetic disorders. It can cut down on years of painstakingly slow research and help us arrive at possible solutions much faster, or it can help us choose the most promising solutions – potentially saving us millions of dollars that could be wasted by going down the wrong road.

Yet, these technologies have miles to go before achieving mainstream acclaim and widespread professional implementation; for now, scientific incubation in research labs under the eyes of experts is the best mode of development for predictive genomics.

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Quantum computing has a variety of applications in revolutionizing the automobile industry such as improving battery performance, avoiding traffic congestions, preventing car accidents and mishaps by machine learning and analysis etc. that can greatly benefit the sector when they come to fruition. Machines in quantum computing work with physical properties of matter, such as superposition or entanglement, which means that calculations can be performed on multiple states of matter at the same time, drastically reducing computation time.

Advantage of Quantum Computing The brainchild of Nobel laureate Dr Richard Feynman, quantum computing has progressed to enormous levels of growth, finding a variety of applications in different fields and sectors. Quantum computing involves simulation of the physical nature of objects at subatomic sizes while allowing them to exist in more than one state. This allows rapid simulation and processing of data than conventional systems, making quantum computers much more powerful, efficient, and faster. It has been applied in fields like Cryptography, Medicine, and material sciences to accommodate multiple variables or molecules in simulations to reach the desired end product or solution. Various automobile companies like BMW (CNET) and Hyundai (Eetasia) have started working with quantum computing systems to solve various issues like cost optimization, development of new batteries, optimization of components to improve cost-effectiveness etc.

Quantum Computing in Battery Technology

Quantum computing has been applied to develop effective solutions in improving the battery technology in cars and automobile systems as it can simulate multiple molecules of compounds simultaneously in different states, conditions, and environments to help identify the ideal combination of variables. Hyundai Motor Co. has partnered up with quantum computing experts to develop a robust battery that can function with improved capabilities and durability when used in electric vehicles. They aim at reducing the cost of battery development and production to reduce the overall cost of the vehicles, improve affordability and progress towards sustainability. A quantum computer of sufficient complexity—for example, enough quantum bits or “qubits”—could theoretically achieve a quantum advantage, allowing it to solve problems that no classical computer could ever solve. In theory, a quantum computer with 300 qubits fully dedicated to computation could perform more calculations in an instant than the visible universe’s atoms Quantum computing has also been applied in the development of novel technologies that can improvise the functioning of EV batteries by incorporating advanced technologies to cool them. It is applied by compartmentalizing big issues into individual parameters that are simulated using quantum computing to be later integrated into the conventional systems as a hybrid model or to fashion a completely new model by combining the solutions offered by quantum computing(EENewsEurope).

Quantum Computing in Autonomous Driving Quantum computing can facilitate the design and development of powerful operating systems to produce self-driving cars, simplifying transportation and reducing the chances of human errors in road traffic accidents. Artificial intelligence and machine learning require the real-time analysis of vast amounts of data to produce optimal responses to changing environmental conditions and quantum computing with its excellent computational features

can lend a hand in facilitating the requirements. Volkswagen(Prescouter) has experimented in the design and development of computational systems to optimize traffic control and regulation in the city of Beijing and has found great success in this venture. It also has applications in improving vehicle to vehicle and vehicle to cloud communications in next-generation cars that are expected to have the ability to communicate with cloud computing systems to regulate driving data. This will help in traffic and fuel optimization in cloud-connected cars while providing a safe environment for decentralized communication between them.

Conclusion

Quantum computing has unlimited potential and practical applications across different fields and sectors and can make a path for enormous progress in the automobile sector. Companies and enterprises in the automobile sectors would greatly benefit by working with quantum computing as it is a leap towards greater sales and a greener environment. Recently, quantum computing has gained a lot of traction in both general society and the private sector. Companies have been pouring huge sums of money into quantum computing research, with the last few years being the busiest for this innovation.

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Like many other industries, supply chains, which are the backbone of national economies, also had to change their strategies to stay efficient and meet the new market requirements. As a result, they have embraced digital solutions to cope with our current times’ uncertainties, challenges, and restrictions.  

The supply chain ecosystem became all the more critical once the vaccines for COVID-19 were developed – it was the need of the hour to get the vaccines to the public quickly and in the right quantities.    

Challenges and roadblocks in the COVID-19 vaccine supply chain    

The biggest problem in any supply chain management is matching demand & supply efficiently.  

Supply chain management companies are currently facing issues due to the disparity in demand and supply for COVID-19 vaccines from region to region. In addition, the lack of raw material and human resources has caused delays in the supply of COVID-19 vaccines.  

Inequalities regarding vaccine distribution have become a global problem, as well. For example, reports show that financially stable countries are getting vaccinated 30 times more than those with lower incomes.   

Vaccine management issues such as storage capacity, handling management, effective distribution between supply chain levels, and shipment procedure are also of rising concern as two doses are required for most vaccines.    

Security issues such as theft and mishandling are other grave concerns, especially considering the demand for COVID-19 vaccines is higher than the supply. Time delay and lack of visibility into supply are also issues researchers have noted.   

Digital solutions reshaping Supply Chain Management  

Digital solutions have played a crucial role in helping retailers, suppliers, and distributors drive the transformational changes required to address the challenges posed. Therefore, the accelerated adoption of these solutions in supply chain models is essential not only for the present but also for the future.  

Here are some of the ways digital solutions have made an impact on supply chain ecosystems:   

Machine Learning: 

Shifting supply chain dynamics, changing ways of working, and increasingly volatile demand has been a concern for suppliers, distributors, manufacturers, and retailers globally when it came to an efficient distribution of Covid-19 vaccines.  

McKinsey predicts that machine learning’s most significant contributions will be providing supply chain operators with significant prescriptive insights into how supply chain performance can be improved by anticipating anomalies in logistics costs and performances before they occur. In addition, machine learning models and techniques can ensure streamlined production planning, inventory management, and anomaly detection and can offer an exceptional customer experience.  

Artificial Intelligence: 

AI-based tools can help understand which geographic regions to target for vaccine supply to flatten the curve of the pandemic sooner, provide insights in customizing the supply chain management system to ensure maximum vaccination in the least amount of time, and ensure the processes are being followed as designed.  

Artificial intelligence tools can also be leveraged for capacity planning, predicting the demand for raw materials, work-in-progress components, and post-vaccination surveillance. With AI, supply chain management companies can improve responsiveness to vaccine demand, minimize risk, and increase visibility & transparency across the supply chain.  

Data Analytics: 

The race to vaccinate the global population is a daunting task and needs data-driven strategies and action plans to optimize the supply chain. Data analytics tools capture inventory, demand, capacity, and other related data across the distribution chain, to create a strong distribution strategy to help supply chain management companies handle the fluctuating demand and supply.  

Data analytics tools like predictive analytics have also helped distribution companies predict vaccine demand in any geography and streamline production and distribution accordingly. In addition, using advanced data analytics technologies, governments can identify and create priority populations in different geographic locations and formulate a vaccination policy that maximizes vaccination rates and minimizes wasted dosages.  

IoT: 

IoT sensors are used to keep track of the temperature in storage facilities and vehicles during transportation. Armed with real-time alerts, IoT solutions let stakeholders be aware of any system failures & let them monitor and optimize the vehicle routes. IoT systems can also track vaccine stocks in financially stable countries to ensure a smoother redistribution of any surplus vaccines to developing countries across the globe.  

Blockchain: 

Blockchain-based solutions have been recognized as the backbone in developing a reliable and transparent supply chain management system to manage COVID-19 vaccine rollouts.  

With its unique capabilities, Blockchain technology can help supply chain companies to track the transportation and storage of vaccine batches in real-time, verify vaccines’ provenance and authenticity, quick detection and identification of faulty products, and identifying and blocking counterfeit vaccines from entering the supply chain. Blockchain-based solutions ensure accurate traceability, enhanced security, and greater transparency in vaccine distribution. 

In Conclusion   
COVID-19 has revealed the fragility of existing supply chain management systems and has proved the potential of digital solutions to cope with unprecedented disruption effectively and efficiently. Advancements in digital technologies such as machine learning, the internet of things (IoT), blockchain, artificial intelligence (AI), and data analytics will pave the way for agile, reliable, and efficient supply chain management systems capable of handling dynamic supply and demand.  

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Digital disruption has invariably affected the health insurance industry as well.  Several players in the industry are now leveraging the latest digital technologies such as Artificial intelligence (AI), Big Data Analytics, and the Internet of Things to customize their offerings, reduce cost, and enhance their customer experiences. As predicted by Accenture, the majority of insurance executives believe that artificial intelligence (AI) will significantly transform the industry, and McKinsey estimates that AI and Analytics will have a total potential annual value of up to $1.1 trillion if AI tech is utilized to its full potential in the Insurance industry.

In this article, we will look at how and where the healthcare insurance market is adopting Artificial Intelligence, and the multitude of value-adds it makes possible within the domain. 

Claim management

Reviewing and processing submitted claims is a time and labor-intensive task.  With Artificial Intelligence, insurance providers can make the claims management process faster and better, with fewer errors. Artificial Intelligence reduces human intervention by identifying and routing claims with the incorrect information for further evaluation by actual people, and routing submissions with correct information towards fully automated background processing.

AI-based claim management enables automated report generation, database updation for future reference, and communication with the end-user, ensuring seamless customer experience. A modern claim management platform leveraging artificial intelligence can help insurance providers optimize the claim management process.

Fraud detection & prevention 

Combating insurance fraud has always been a nightmare for health insurance providers. Artificial Intelligence is the perfect solution to this problem. Handling insurance fraud is a costly, time-consuming process if insurance providers rely on traditional methods. With the introduction of AI-based solutions, it is possible to analyze a humongous amount of data within the shortest possible time. AI-enabled systems can analyze entire medical histories without any hassle and identify patterns a human might overlook. When the system comes across any possible anomalies, it will be marked for closer scrutiny. With more data, AI-based systems will become more intelligent to identify which parameters or instances might be a fraud and which might not. However, even though artificial intelligence-based solutions are the best way to handle the quantity and complexity of data, human intervention is likely to remain a key element in making the final decision. 

Operational efficiency

The insurance industry is generating a massive amount of data, thanks to accelerated digitization. Health insurance companies have been storing this data on legacy systems that are often incompatible with each other. Stakeholders within the organization may work in silos, and this leads to substantial manual dependency as well.

Implementing AI-based systems addresses this issue by standardizing the data structures and reducing the manual effort for mundane, repetitive tasks. AI-based systems handle external data (client-focused) and internal data (process, operations, etc.) for insurers. By analyzing the available internal data, these systems help insurers develop risk mitigation and cost reduction strategies by optimizing their processes and standards. These systems can also be leveraged to assess the company’s performance and predict possible undesirable outcomes even before they occur. 

Personalized product offerings

Traditionally, insurance companies have designed their offerings based on the risk pools determined by statistical sampling. With dynamic consumer behavior, this “one-size-fits-all” approach for product offerings may not be efficient or sustainable. Though companies provide personalized offers such as critical illness coverage, heart disease coverage, etc., customization at an individual level is yet to be seen. 

AI can disrupt the insurance ecosystem by providing consumer behavior-based personalized product offerings. AI systems can analyze individual medical records ( age, gender, job nature, family health history) to personalize the product, the premium, and the coverage list. If integrated with IoT devices like smartwatches, fitness bands, and other wearables, insurance providers can predict and personalize insurance coverage for each unique customer.

Conclusion

The insurance industry is still in the nascent stage when it comes to utilizing the vast possibilities that artificial intelligence offers. With accelerated digitization and change in consumer behavior, the health insurance industry will witness increased adoption of AI-based systems to reduce costs, improve operational efficiency, and provide superior customer experience. The future of AI in healthcare insurance is to create a better health experience for all stakeholders. 

Black Rock IT Solutions has been providing AI-based solutions to enterprises across the globe to accelerate their digital transformation journey – we have built AI-based solutions such as automated claim detection using deep learning, automatic information extraction from claim forms and real-time emotion detection through facial expression analysis. Our team of experts in the insurance and healthcare industry enables organizations to craft better customer experiences and introduce innovative business models. 

To know more about our AI solutions, write to sales@blackrockdxb.com

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