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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How are Insurance Companies Adapting to Customer Demands?

It is interesting to see how the insurance sector is implementing digital transformation solutions, which requires the industry to innovate and adapt to changing customer expectations. Customers are increasingly seeking personalized and fast insurance options, which puts pressure on insurance companies to provide more flexible and customer-centric services. As a result, the industry must stay up to date with the latest technological advancements to meet the growing demand for customized insurance products and services.

Have you ever had to file an insurance claim and found yourself waiting on hold for what seems like forever? It can be frustrating, especially when someone is in urgent need of assistance. But, with the help of chatbots, insurance companies are changing the game.

Chatbots are like automated assistants that can quickly register customer requirements and guide them through the claims process. This not only provides customers with immediate assistance but also streamlines the entire process. For example, during a catastrophic event where many customers need to file claims at the same time, chatbots can handle multiple requests simultaneously, including registering customer details, filing claims, providing updates on the status of claims, etc. ensuring that everyone gets the help they need in a timely manner. Plus, chatbots provide a more systematic and organized way of managing claims, allowing employees to act faster, which ultimately leads to improved customer satisfaction.

Chatbots offer a range of benefits to the insurance services and solutions. They are available 24/7, providing instant customer support whenever customers need it. They can process customer inquiries and provide responses much faster than human agents, allowing for faster service. Additionally, chatbots can be programmed to offer personalized service, tailoring responses to individual customers’ needs and preferences. By handling a high volume of inquiries at once, chatbots can reduce the number of human agents needed, making them a cost-effective solution for insurance companies. All these benefits result in a more efficient and satisfying customer experience.

What are the different types of chatbots?

• Menu/Button-based chatbots: These are the simplest form of chatbots that use pre-defined options or buttons to guide users through a conversation. These chatbots are easy to set up and can provide quick and straightforward responses to basic questions.
• Linguistic-based (Rule-based) chatbots: These chatbots use pre-set rules and decision trees to understand and respond to user queries. They are programmed with a set of specific questions and responses and can only provide answers based on their programming.
• Keyword recognition-based chatbots: These chatbots are trained to recognize specific keywords and respond accordingly. They can handle more complex queries than rule-based chatbots and can provide more accurate responses.
• Machine learning chatbots: These chatbots are more advanced and can learn from user interactions to improve their responses over time. They use natural language processing and machine learning algorithms to understand and respond to user queries.
• Hybrid model chatbots: These chatbots combine the features of rule-based and machine learning chatbots. They use pre-set rules to provide accurate responses to basic questions and can also learn from user interactions to improve their responses over time.
• Voice bots: These chatbots are designed to interact with users using voice commands instead of text messages. They use speech recognition technology and natural language processing to understand and respond to user queries.

AI Techniques Used in Chatbots

Chatbots are AI applications designed to simulate human conversation, and they use different AI techniques to interpret and respond to user queries. The two most used AI techniques in chatbots are machine learning (ML) and natural language processing (NLP).

ML algorithms enable chatbots to learn from user interactions and adjust their responses based on feedback. NLP algorithms help chatbots to analyze unstructured data like text messages and social media posts to extract relevant information and respond accordingly.

The choice of AI technique depends on the complexity of the chatbot’s task, the volume of customer inquiries, and the level of customization required. By leveraging these AI techniques, chatbots can improve their accuracy and efficiency in understanding and respond to user queries.

Tips for Implementing a Successful Chatbot Strategy in Insurance

• Define Your Goals: Before implementing a chatbot strategy, define your goals and what you hope to achieve. This will help you determine what type of chatbot to build, what features it should have, and what metrics to track.
• Understand Your Customers: To build an effective chatbot, you need to understand your customers’ needs and preferences. Conduct customer surveys, analyze customer data, and use this information to create a chatbot that meets their needs.
• Use Natural Language Processing: Natural language processing (NLP) is essential for creating a chatbot that can understand and interpret customer queries accurately. Use NLP to enable your chatbot to understand natural language and respond in a conversational manner.
• Provide Easy Access: Make it easy for customers to access the chatbot. Place the chatbot on your website, social media platforms, and mobile apps, and promote it through marketing campaigns.
• Monitor Performance: Monitor your chatbot’s performance regularly to ensure that it is meeting your goals and providing a positive customer experience. Use metrics such as response time, customer satisfaction, and conversion rate to track your chatbot’s performance.

Cost Considerations for Implementing Chatbots in Your Organization

When it comes to implementing chatbots in your organization, the cost can be a big consideration. The cost of implementing a chatbot will depend on several factors, including how complex and customized you need it to be.

There are two main categories of cost: initial setup and ongoing maintenance. For the initial setup, you will need to consider expenses such as hiring a development team, purchasing software, and integrating the chatbot with your existing systems. Ongoing maintenance costs include regular updates, monitoring, and optimization to ensure the chatbot is functioning properly and providing accurate responses to users. While the cost of implementing a chatbot can range from a few thousand to tens of thousands of dollars, the benefits can be significant. Chatbots can improve customer service, increase efficiency, and ultimately save your organization money in the long run. As you consider implementing a chatbot, it is important to carefully consider your budget and business needs to determine the potential return on investment.

An Overview of Experion’s Chatbot

Experion’s domain-independent chatbot is a game-changer in the context of delivering excellent customer service. This highly configurable chatbot can be integrated into any project and can be used across multiple devices, including web and mobile platforms. The chatbot can be integrated into external systems, making it a versatile and valuable tool for businesses across the globe. Our chatbot offers a range of benefits, including lightning-fast customer support, personalized service, and cost-effective solutions. By handling a high volume of customer inquiries simultaneously, the chatbot frees up human agents to focus on more complex issues and provides a seamless customer experience.

By leveraging our chatbot, insurance companies can provide their customers with fast and enhanced services. Our chatbot is currently being offered as a value-added service to our clients at no cost. It is a low-code application that can be easily personalized based on our clients’ requirements by connecting the API to their existing applications, allowing the chatbot to access the necessary data from the customer database.

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Supervised Learning Techniques for Fraud Detection

Supervised learning is a common method of machine learning for fraud detection. In supervised learning, a dataset that has been labeled with the correct output for each example is utilized to train the model. This enables the model to understand the connections between the attributes and the label and to predict outcomes using brand-new, untainted data.

The decision tree is a common supervised learning algorithm type for fraud detection. The predictions made by decision trees are based on a succession of binary splits, with the leaf nodes serving as the ultimate prediction and each internal node representing a decision based on the value of a characteristic. Both numerical and categorical data can be handled by decision trees, and they are simple to grasp and analyze. However, they are sometimes prone to overfitting, particularly if the tree grows to be excessively deep.

Logistic regression is a different class of supervised learning technique that is frequently employed in fraud detection. A linear model called logistic regression is used to forecast a binary outcome, such as whether or not a claim is false. It operates by assessing the likelihood of the event and categorizing it as either “0” or “1” depending on whether the probability is below or over a predetermined threshold. Decision trees are more prone to overfitting than logistic regression, which is easier to execute and interpret. If the relationships between the features and the label are non-linear, it might not function properly.

Unsupervised Learning Techniques for Fraud Detection

Unsupervised learning is another machine learning technique that is useful for fraud detection. In unsupervised learning, the model is not provided with labeled examples, and must instead discover patterns and relationships in the data on its own. One popular unsupervised learning algorithm for fraud detection is the k-means clustering algorithm. This algorithm works by dividing the data into a specified number of clusters, based on their similarity. The assumption is that fraudulent cases will form their own distinct cluster, which can then be identified and flagged. K-means clustering is easy to implement and can handle large datasets, but it is sensitive to the initial conditions and may not always find the optimal solution.

Another unsupervised learning algorithm that is useful for fraud detection is the anomaly detection algorithm. This algorithm works by identifying cases that are significantly different from the majority of the data, and flagging them as potential fraud. Anomaly detection can be useful for detecting rare cases of fraud that may not be identified by other methods. However, it can also produce a high number of false positives, and may not be as effective at detecting more common types of fraud.

Semi-Supervised Learning for Fraud Detection

Another machine learning technique that combines aspects of supervised and unsupervised learning is semi-supervised learning. The model is trained on a partially labeled dataset in semi-supervised learning, and it is required to make predictions on both labeled and unlabeled cases. The support vector machine is a well-liked technique for semi-supervised learning (SVM). SVMs function by locating the hyperplane in a high-dimensional space that best segregates the various classes. They work effectively on a range of activities and are efficient at managing high-dimensional data. However, they might not scale well to very big datasets and their training can be computationally expensive.

Conclusion

In conclusion, there are several different machine learning techniques that can be used for detecting insurance claims fraud. Each technique has its own strengths and weaknesses, and the best approach will depend on the specific characteristics of the dataset and the needs of the insurance company. It is important to carefully evaluate the performance of different machine learning techniques and choose the one that offers the best balance of accuracy, efficiency, and interpretability.

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Blockchain is considered to be a breakthrough technology as it has the capability to bring huge benefits to businesses across various sectors. In the insurance space, blockchain offers an acceptable standard format for securing contracts, transactions or any other details that can be maintained in the public domain. Apart from this, it has various other prospects and use cases that make the lives of insurance businesses easier. 

Let us take a deep look at how Blockchain benefits insurance and why businesses should consider it. 

How does Blockchain work?  

Blockchain is a distributed ledger technology that uses peer-to-peer sharing of information in an immutable manner. Also popularly known as Web3, it is a publicly verifiable and shared distributed ledger technology (DLT) used for securely exchanging data within a network and recording the history of transactions.  

As the name suggests, Blockchain is a chain of blocks that contains information inside a block, and each block is connected with a hash of its previous and subsequent blocks to create a chain. The data within each block is shared with multiple peers and each block has the same copy of data. This data once formed cannot be changed. For any change in data values, a new update needs to be made, which is again shared with all peers. It is not easy for any unauthorized parties to make changes to the data since all available copies need to be changed simultaneously, which is not practically possible. This makes the data in the blocks highly immutable, and hence there is an implicit guarantee as to the veracity of the data that is saved in the blocks.  

Why Insurance companies must look at Blockchain?  

The inherent features of blockchain that make it a useful technology for the insurance sector include the following. 

• Immutability and Security: Blockchain is a secure and transparent approach to store and process data using cryptographic functions. Once data is written and stored in the ledger, it cannot be changed or edited, which shelters it from unauthorized amendments and users. 
• Transparency: Blockchain requires validation and acceptance rules to be enforced to enable any parties to initiate and add transactions. Therefore, all transactions within the public blockchain network are made available to all participating nodes, while in private blockchain, all authorized nodes are given access to the transactions. 
• Authenticity: Every transaction that uses smart contracts in blockchain contains the digital signature of the creator and responder. Moreover, every block holds the hashed IDs of the previous and subsequent blocks, making the transactions legitimate. 
• Ownership and Accountability: All participants within the blockchain network know the provenance of a transaction. The immutability of transactions in the block and the connection among blocks empower accountability and ownership control in applications that use blockchain technology. 

On account of these features, blockchain has now come into the mainstream and is being used by insurance companies in multiple ways. 

High-Value Registry

For those in the high-value goods trading business, it is especially important to have proper records of ownership of such goods. From yachts to houses, everything needs to not only be registered to maintain correct records of ownership but also needs to be insured against losses. In this scenario, blockchain has become the go-to solution for keeping an up-to-date and accurate registry – as the data saved in it cannot be disputed and duplicated. As the value of the goods increases, so do the chances of fraud. However, with blockchain, the propensity to commit fraud is reduced to a significant extent due to the huge costs involved in committing such frauds.

Parametric Insurance  

One of the several ways the insurance industry is utilizing blockchain is through parametric insurance- which automatically delivers insurance claim benefits based on pre-defined events as measured by specific parameter or index. The parameters of the event are coded in advance and when the particulars of the event are met, the claim is automatically triggered and paid out. The benefit of this apart from the immutability aspect of blockchain is the huge savings in terms of labor costs as well as cascading litigation costs. 

Benefits for Reinsurance 

Another major user of blockchain is the reinsurer who can benefit from smart contracts. Blockchain helps with automating the storage and distribution of data and contracts among multiple players such as reinsurers, brokers and regulators. Cost benefits accrue from not having a human in the mix. Because the data is immutable, there is truly little chance of litigation and subsequent wastage of time and effort. Insurance companies also benefit from blockchain due to the improved clarity and lack of ambiguity. 

Fraud Detection and Prevention 

One of the major benefits of blockchain is its ability to eliminate the limitations of client-server architecture to prevent fraud. Blockchain also prevents forgery and alterations in the contracts and documents since the distributed ledger is shared with all participants in an unchangeable and append-only manner. The contracts must be canceled, and new ones must be entered, which makes original statements, policies, and contracts difficult to mutate.  

Claim Submission and Processing 

Insurance companies often face several challenges when it comes to creating and processing insurance claims. The entire process can be very time-demanding, tedious, costly, and prone to human errors. Blockchain reduces the complexities of creating, accepting and processing insurance claims using smart contracts. It creates immutable statements that are easily accessible by all participants within the network and makes the process less costly and hassle-free.

Data storage management

With more IoT devices coming to use, the data generated will keep on increasing. This data is extremely crucial for insurers to develop accurate usage-based insurance models for their customers. Blockchain allows insurance companies to effectively manage the enormous amounts of data generated by offering a decentralized platform to store and process it. This saves insurers from investing in expensive data centers and fraud investigation measures. 

Who is it for?  

Blockchain is now widely used by both large and medium-sized insurance companies. The benefits of using blockchain include – improved efficiency of completing paperwork, ensured data security, real-time processing of claims, and use of smart contracts to store confidential information. As the use of blockchain becomes more widespread, the cost of using the technology will come down further. There are new companies coming along every day that are bringing modern technologies and frameworks to use, making it easier for everyone to implement blockchain for everyday business deals. 

How does blackrock help? 

blackrock has a dedicated blockchain team helping our clients design and implement blockchain-based projects quickly and efficiently. We have completed multiple projects as well as Proof-Of-Concepts using blockchain and our purpose is not just to put your business ahead of the curve, but also to maximize value propositions for your customers.  

We not only have technical expertise but also functional depth in the property and casualty (P&C), motor insurance, life and health insurance, and workers’ compensation domains. Whether your choice of blockchain is private (company/industry specific) or public – based on your specific business case, we can set up and implement the most apt blockchain solution for you. To explore opportunities for your business, get in touch with us at sales@blackrockdxb.com. 

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Blockchain:  

While bitcoin has always been a headliner, few recognize its driver technology known as Blockchain. It is an advanced technology utilizing a peer-to-peer ledger of records that is of virtually incorruptible safety. As it is designed to be self-managed, it is highly affordable, barring the initial capital requirement. It can tread great lengths in fixing the apparent intangibility customers feel towards their insurance plans and policies. Due to its unique and secure trademarks, it could increase processing speed, help establish customer trust, and expedite claim processing without falling prey to fraudulence and manipulation.  

Artificial Intelligence: 

While AI needs no introduction, its deployment in InsurTech brings to the table a host of unprecedented gains that can revolutionize the face of the financial domain. Moreover, with personalization now being the norm for everything, AI has much to offer in creating bespoke experiences, helping customers feel discerning, and bolstering their trust.  

With adoption levels and implementation still slow, experts believe that its initial impact will be in the automation of underwriting and claims processes. In tandem with machine learning, it will analyze risks and identify new sources of capital that could lead to new frontiers in checking fraud and money laundering.  

Gamification:  

This is undoubtedly one of the most peculiar and yet promising trends of InsurTech in 2021, showcasing the adoption of video-game-inspired strategies like level clearing and unlocking, performance bonuses, and so on. Although rare, some actuarial companies even deploy their games to promote their insurance-related products. It may also lead to a sense of brand loyalty to the firm.  

Younger clients often duck the insurance business due to its labyrinthine and precarious nature; with this customer category, gamification has yielded the most results. Moreover, firms can utilize the aspects of gamification at every juncture in the customer experience, unlike the other trends that limit themselves to technological aspects.  

Machine Learning: 

Although machine learning is related to AI (while being highly specific), it is only with its integration that InsurTech can achieve total efficiency. While artificial intelligence can improve and speed up claims processing, it is only with machine learning that we can automate it. The power of pre-programmed algorithms will be a great tool in automation using the digital files accessible via the cloud. And its utility is not limited to either P&C firms or their customers; it supports both entities almost equally. In addition, it has numerous other payoffs, like risk calculation, CLV, and PIE computation.  

IoT: 

With the ever-rising number of connected devices around the world, insurers can use these tools to harness data; the open-source data available from smartwatches, homes, and even automobile sensors can help collect pivotal data that can assess the customer psyche. A simple use case is the collection of health data from a client’s fitness smartwatch, which could furnish the P&C insurers with information that would help in policymaking for the said client. Though it has come under the scanner for potential infringements of customer privacy and corporate fraud, this trend continues to thrive and grow with some of the top insurance companies in the US today.   

If you are looking for a Digital Product Engineering Services partner who not only keeps up with emerging trends but takes giant strides in the creation of their own, blackrock is the right partner for you. Browse our website to see more on our FinTech products.  

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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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Here are some of the new avenues InsurTech players need to focus on going forward, to ensure they stay on top of their game in an ever-evolving playing field. 

Artificial Intelligence

McKinsey reports a total potential annual value of up to $1.1 Trillion for AI adoption across industries. Though industry experts have been urging insurance players to embrace digital transformation solutions to offer better, faster, and cheaper services for quite some time, organizations were moving at a snail’s pace. Artificial Intelligence can transform the insurance industry by replacing frustrating, time-consuming, bureaucracy-dependent systems with fast and tailor-made solutions. Organizations can leverage AI to customize their offerings such that customers can avail them whenever or wherever they want. Personalized pricing is another area where AI can be utilized. This will allow customers to pay only for the package they wish to avail rather than opting a comprehensive package. AI will revolutionize the way companies operate in a post-COVID-19 world. 

Predictive Analytics

A data-driven predictive analytics system predicts the behavior of the insured, delivers better insights, and enables the insurance providers to make better decisions. Predictive analytic systems can be leveraged to personalize and optimize offerings. For instance, by analyzing historical consumer behavior, customer preference, risk exposure, etc., Predictive analytics can also be utilized to anticipate and prevent fraudulence, predict customer churn and application manipulation, and ensure superior customer engagement. In a nutshell, predictive analytics will be the ‘insurance package’ that insurance providers must opt to continue offering their services in a highly competitive market.

IoT

Market research firms are expecting the IoT insurance market to be worth around US$ 192 Billion by 2025 at a CAGR of 65.5%. Real-time collection and analysis of data from IoT devices or wearables enable the insurers to improve efficiency, reduce costs, and optimize resources. With proper data analysis and meaningful insights, insurers would be able to offer customized solutions for each customer and establish strong and ongoing personal relationships. They must monetize the data generated to develop new services and business models with an objective to create additional revenue. The success of insurers depends significantly on their preparedness to embrace technology advancements and innovations to create value for their customers in the long term.

Chatbots

Like in any other industry, customers in insurance would love to compare different packages without having to speak to an agent and make the purchase of their customized insurance services online. Chatbots offer superior customer assistance instantaneously rather than having a customer wait to talk to a customer service representative, ensuring faster and more efficient services. Insurers must utilize chatbots to offer a higher level of personalization while crafting policies for customers. Along with guaranteeing prompt customer responses, chatbots must be used for recommending new policy suggestions based on customer behavioral traits. If designed and implemented correctly, chatbots can enhance the relationship between customers and providers. 

Telemetry

With connected devices going to rule in the future, Telemetry provides a great opportunity for insurers to maximize their value propositions.  Telematics can change insurance equations and disrupt the business as well as operations models – from risk assessment to customized and competitive pricing, improved fraud detection, reduced claims resulting in superior customer experience, and increased customer retention. They must position the societal and environmental benefits of having telematics in place in collaboration with the government and other regulatory bodies.

The world is going through a very challenging and uncertain time. The pandemic has caused the global economy to contract sharply in the near to medium term. With a global recession looming on the horizon, it is natural to adopt a cost optimization strategy. But we believe that time is ripe for InsurTech companies to invest in innovation and digital transformation initiatives. InsurTech companies need to adapt to new technologies and technology-led business model innovations to remain competitive.    

Black Rock IT Solutions has a long history of working with InsurTech companies to build solutions that enable insurers to craft better customer experiences, navigate regulations faster, reduce manual processes, and introduce innovative business models. To know more about our InsurTech offerings please contact sales@blackrockdxb.com.

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