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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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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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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E-learning platforms have been around for many decades, but haven’t been able to create the kind of  ‘transformational’ impact that has long been promised. With the Coronavirus ‘black swan’ event, all of these remote learning platforms have gotten a sudden push into the mainstream, even in places where people could not envision such options earlier due to poor infrastructure and other socio-economic issues. 

The size of the E-learning market had already been pegged to cross USD 240 Billion by 2023, and now this projection is going to go up further. It is no wonder then, that in the coming days we will see newer companies entering the remote learning race utilizing emerging technologies like Artificial Intelligence, Big Data Analytics, Cloud Computing & Augmented and Virtual Reality to compete with established market leaders.  

Emerging technologies that are set to take e-learning solutions to the next level

Artificial Intelligence

According to Forbes, “Artificial intelligence can act as a virtual tutor and answer questions on the fly”.

Artificial Intelligence-powered E-learning platforms will be equipped to identify and assess learners’ levels of understanding and suggest customized paths of learning. Content analytics powered by AI makes personalized and learner-centered approaches possible – these would be based on the learner’s intellectual level, from kindergarten through higher education to even corporate training. Developing these solutions is no longer a daunting task with the advent of AIaaS or “AI as a Service” – now these solutions are easy to implement and come with enticing benefits. 

Augmented Reality (AR)

AR solutions bring more engagement, excitement, and innovation to e-learning platforms making content more consumable, enjoyable, and valuable. Augmented Reality techniques will be widely utilized to increase engagement for learners through activities, rather than the traditional read and write learning methodology. Augmented reality can also be used to offer customized learning options to suit differently-abled students with special needs to learn better, as AR includes gesture, voice recognition, and image rendering experiences.

Virtual and Remote Laboratories

Laboratories are some of the best places to learn – they allow students to acquire knowledge, experiment with different methods of arriving at answers, and experience real equipment, inculcating active learning and the development of skills based on solving challenges. Simulating labs virtually makes it possible for the users to stay connected through the internet to a remote physical system, and conduct web-based technical experiments. Students can extend their depth of knowledge in areas of control systems, programming, database, various other web-based applications, tools, and technologies. 

Virtual labs are also extremely beneficial to run experiments that are normally too expensive, unsafe to run, or unavailable.  They also allow students to repeat an experiment multiple times,  giving them the opportunity to see how changed parameters and settings affect the outcome without wasting resources.

The Internet of Things

The number of devices that will be a part of the Internet of Things (IoT) by 2025 will be around 75 billion in number. The rise in IoT is going to further improve the e-learning experience. Already we see how mobile learning allows learners to access E-learning courses with absolute freedom of time and place. In the future, we are going to see more content in the form of images and videos.  24X7 access to the internet across a number of wearables will make teachers redesign assessment from “remembering and writing down”, to “evaluate and elucidate concepts on the go”.

If you’re thinking of using these technologies to your advantage to build or improve your E-learning systems and solutions, reach out to the expert technology consultants at Black Rock IT Solutions. blackrock has built spectacular EduTech platforms for organizations across the globe – with our years of experience and know-how in the latest technologies such as AI and AR/VR, blackrock is certain to deliver the best solution with minimum time investment.

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From governments closing the borders and enforcing lockdowns to businesses struggling to meet the supply and demand, the novel coronavirus is changing the way businesses operate, collaborate and compete. Traditional business models are on the verge of being wiped out by their digital counterparts. Digital Transformation is something that has been predicted by leading analyst firms as the way forward for years now, and certain organizations have already implemented it, with a fast-tracked future now becoming a reality.  In this current scenario, the first movers will have the upper hand in the long journey ahead, and unfortunately, the laggards will struggle to tackle the situation. 

This article takes a predictive look at how various industries will flourish post COVID-19.

Healthcare

Unlike any other industry, COVID-19 has affected the healthcare industry the most. The current situation is an opportunity for healthcare stakeholders to utilize the available tools, software, and technologies such as AI, ML, IoT, Data Analytics, etc to offer better solutions.

Mobile healthcare, IoT, Virtual Assistants, and Robotics are some of the leading areas that healthcare providers can look into. Mobile apps to get in touch with doctors at your convenience, sensors/IoT devices to monitor real-time patient data, virtual assistants to guide healthcare professionals with complex procedures remotely, machine-learned robots to assist doctors in patient examination, etc. are some of the areas where digital transformation is going to make its mark.

Ultimately, new business models will emerge, as all healthcare providers need to rethink their current business strategies and pave the way for the vast opportunities that will come along with this pandemic. 

Retail 

With people staying inside their homes and practicing social distancing, the impact of COVID-19 on retailers will be severe. Leading retailers have started adopting omnichannel digital transformation strategies rapidly to win the customer pie. Most traditional retail customers, who once swore by the in-store experience, have now been forced to experience the convenience of digital purchasing, and are likely to continue with the trend even once this phase of being locked in is over.  

Brick and mortar stores have noted reduced footfall during this situation.  In order to stay afloat, they must now look into newer and more innovative ways to do business – be it through online platforms or even just becoming available on social media platforms to take orders. We are yet to see how the retail industry will evolve post-pandemic but surely, COVID-19 has dramatically accelerated the digital transformation in the retail industry too. 

InsurTech

The insurance industry thrives on predictability and the uncertainty that COVID-19 has caused is unprecedented. Most of their customers will be devastated with the fear of losing their jobs, their businesses and even their lives and they will rely on the insurance industry to be a ray of hope. 

Collaboration with various governments and other industry players to collect data from all possible sources should be the priority. Be it in the form of sensors, IoT devices, weather reports, and drones, they must generate meaningful insights in real-time to offer pro-active and personalized solutions. Coupled with advanced data analysis, they can come up with data models that predict possible future outcomes like this. 

InsurTech players must lead the digital transformation journey in terms of leveraging the latest technologies, optimizing the processes, personalizing the solutions, and bringing in automation to reduce costs and improve efficiency and effectiveness. All the players in the ecosystem must embrace digital transformation at the heart of their strategy to be the front-runners in the new insurance era. 

FinTech

With people being asked to remain indoors and governments and central banks announcing economic relief packages, everyone is looking at FinTech to help implement their ideas.  The current situation brings a new sense of urgency to the need to examine ‘going digital’ by accelerating the rush to digitization. Digital payments have become an integral part of everyday life across the globe, opening up new avenues of innovation and business opportunities in the coming future. 

Digital onboarding, omnichannel retailing and eWallets will likely grow to new heights. Financial institutions must utilize these opportunities and go digital in their offerings. Technologies such as Artificial Intelligence, Blockchain and big-data analytics will play a crucial role in formulating FinTech strategies in the digital-only era. 

EduTech

The global learning population has been keenly impacted by the spread of the C0VID-19 virus. Though the infrastructure in terms of internet access, data costs, etc are yet to catch up with the rising requirements, the time is ripe for EduTech companies to make their mark. This will be the point of transition in the adoption of the online medium against the traditional educational model. 

Instead of focusing on replacing mundane administrative and managerial roles, which unfortunately most providers are doing right now, they must embrace and accelerate the digital transformation of education itself. EduTech companies need to collaborate with traditional universities and schools and offer an online-offline system. They must leverage the latest technologies such as online video-based learning, artificial intelligence, machine learning, advanced analytics to personalize the learning experience and ensure a more engaging and valuable offering to the user.

The change here must come from the educational institutes as well, as they need to take the step towards embracing this change – but the current pandemic has only proven that it is high time they understand that this is the future of education. 

Transportation & Logistics

Transportation and logistics players globally are trying to fix things with whatever resources they have because they are the lifelines that make sure the logistics of essentials stay in place. With limited manpower and severe restrictions, they must leverage technologies to overcome this hurdle. 

This is an opportunity for them to rethink their business practices. Instead of working and relying on outdated legacy software systems, they should embrace digitization and automation to stay competitive within the industry. They must accelerate their digital transformation process and partner with technology companies to develop tech-driven solutions to offer streamlined and personalized end-to-end services. They must harness the power of “digital” to handle any unforeseen events in the future. 

The challenges that COVID-19 poses cannot be generalized as it varies from industry to industry. Post epidemic, organizations should revisit and reinvent their business strategies with more focus and weightage on innovation, research and development. Inevitably across industries, digitization shall be embraced with an urgency more than ever before. 

Survival and recovery of core operations should be what organizations focus on post COVID-19. Collaborating with digital technology consultants like Black Rock IT Solutions can help you get the kick-start you need to successfully restart your businesses.

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Here are the top 5 trends which will revolutionize healthcare

AI and Deep Learning

AI and Deep learning (often used interchangeably) will bring some of the biggest innovations in the healthcare industry. AI systems are being actively pursued for drug development, early diagnosis, treatment protocol, personalized medicine and patient monitoring and care. AI has been extensively used in the field of Radiology as evidenced from the New York Times story. Some of the notable industry leaders who are developing AI/ Deep learning-based healthcare solution include:

• IBM’s Watson:- It can diagnose, analyze scientific papers and even help in drug discovery
• Microsoft’s Hanover project:- It analyses medical research and predicts cancer drug treatment approaches for patients
• Google’s DeepMind:- Image analysis for cancer recognition and early health risk detection

The use of AI will help decrease the overall healthcare cost, as accurate diagnosis and preventive medical intervention using technological solutions become mainstream. Countries with very little access to experts can use similar technologies to democratize healthcare for the masses.

IoT/ Wearables

The tech industry has seen many devices being launched to satisfy even the most diverse of consumer needs. Devices now are so sophisticated, that it is almost as accurate as a professional ECG machine. Interestingly, IoT devices in the future will have much more potential than the off the shelf wearable tech available today.

Future devices could enable remote patient monitoring using smart sensors and integrate the data generated with the larger healthcare infrastructure wirelessly. 5G technology will certainly be a great boost to the infrastructure needs needed for the IoT ecosystem. When 5G becomes more common, IoT devices will get a lot more common and mainstream.

Let’s look at some of the examples of how IoT can influence the future of healthcare.

• For Patients: – Preventive medicine enabled by wearables will be the big buzz of the future. Wearable devices would be able to monitor blood pressure, heart rate and glucose levels with very high accuracy that it would be classified as a healthcare device.
• For Physicians: – Physicians can monitor and intervene more effectively by analyzing the data from wearable devices. Data collected can also be used to identify the best treatment approach for patients.
• For Hospitals: – Hospitals can use IoT enabled systems for monitoring equipment, tracking pharma inventory among other things. Post-operative monitoring of patients is also a new area of research.
• For Insurance companies: – Insurance companies are piloting programs which reduced health insurance premiums for people sharing data from their wearables and health apps. This will become common. Data from multiple devices can be used to calculate individualized risk profile and then corresponding premiums will be calculated according to a person’s risk profile.

Cloud Innovation

Even though the advantages of moving to cloud ecosystem are many, healthcare systems have been the slowest to transition due to the sensitivity of data and more importantly privacy legislations. As cloud becomes more and more acceptable in our day to day life, it is but natural that healthcare data would also transition to the cloud.

As HIPAA/ GDPR compatible cloud solutions become more common, it will become an essential part of the healthcare landscape.  Some of the use cases of Cloud solutions in healthcare are:

• Data Visualization tools would represent health parameters of an individual into usable forms for self & professional evaluation. Data for this can be collected from various sources including medical devices, wearables and even connected weighing machine.
• Counterfeit protection of drugs, medical equipment, and other consumables.
• EHR or Electronic Health Record could be used to track a patient history between physicians, hospitals and insurance companies.
• Mobile apps can be used extensively in the healthcare industry for pre, during and post hospital care.

 Blockchain

Blockchain’s main advantage is that applications built using blockchain algorithms will have very high data privacy and security. Blockchain technology will primarily be used in healthcare to store and transfer data securely.

If blockchain technologies are introduced to pharma supply chain, it could effectively reduce instances of fake and counterfeit drugs within the system. Areas which will see blockchain technology implemented are:

• Medical data management
• Supply chain management

AR/VR

Telemedicine has been one of the greatest successes of technology in the healthcare sector. With solutions available now, patients can access the services of a physician from anywhere in the world. This means that patients can get access to doctors who are otherwise not available to them. These technologies, even though great, has its limitations. AR and VR technologies can greatly augment these capabilities and improve the final user experience.

With the advent of AR (Augmented Reality), VR (Virtual Reality) and related technologies, Physicians can touch, diagnose and even conduct surgeries remotely using these technologies. AR and VR will enable the doctor to see the patient without being present near the patient. Some of the application of AR/ VR technology in healthcare include:

• Telemedicine
• Robotics and remote surgery
• Training Physicians

 Conclusion

The focus of the healthcare industry is slowly shifting from treatment to preventive care. This means the industry also is moving from products and services model to solutions model. To make this transition, it is important to adopt and implement technologies such as AI and AR. The advantage of using technology is that while it improves the quality of care for patients, it also reduces the overall healthcare cost. Using technology is a big win for all the stakeholders. Healthcare industry as a whole must rethink and transform their strategies to stay ahead of the curve.

blackrock has extensive experience working with partners and clients to implement AR/ VR, mobile, IoT and cloud technologies in the healthcare domain. We have worked with hospitals, ISVs and insurance companies to transform and aid their digital transformation strategies.

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Resume parsing was introduced to simplify the recruitment process using automation, by leveraging Machine Learning & Natural Language Processing. It helps HR professionals intelligently manage resume information, by removing the headache of manually handling each resume. Incorporating resume parsing helped enterprises streamline the entire recruitment process, helping them hire the right talent for the right job efficiently.

What is resume parsing?

Resume parsing is a technology that enables automatic processing of resumes by extracting available data from resumes and organizing in a structured manner. In addition to extracting standard fields such as name, work experience, email, contact number, education skills, etc., custom fields can be added to include other information that might not have included in the traditional resume format.

Why Resume Auto Parser (RAP)?

Combing through resumes in search of specific requirements covering multiple parameters has always been a nightmare for recruiters. This becomes additionally inefficient as the experience level, and skills of candidates keep improving, and there are only few tools with which recruiters can keep track of these dynamics without the active involvement of candidates.

As part of our recent engagement with a large enterprise, blackrock developed a customized version of Resume Parser solution which helps the client’s HR department add or update the candidate profile automatically whenever a candidate presents a new or an updated resume.

How does the Resume Auto Parser (RAP) Solution work?

The RAP solution is based on pattern recognition, encapsulating the patterns commonly observed in resumes of technical professionals, especially in the IT domain. The pilot version of the solution developed by blackrock can capture the following candidate information from a large repository of resumes:

1) Name 2) Telephone number 3) Email 4) Years of experience 5) Technical skills 6) Languages spoken 7) Hobbies, etc.

The solution can examine any number of resumes and generate a report with the above details. The input file format can be DOC, PDF, or JPEG.  The output format is CSV, which enables the report to be viewed and formatted using MS Excel.

Challenges Identified

1) The diversity of resume design and file-formats coupled with lack of annotated data means that any data-intensive approaches become impractical.

2) It has been observed in various instances that name (candidate names) recognition using tools such as SPACY was not yielding accurate results as the model (‘en_core_web_sm’) did not prove to be accurate in identifying Indian names and surnames. An attempt to train the model using an available dataset of Indian names, though improved the performance, also fell short of desired accuracy level.

Document Parsing

Resumes are saved mainly in DOC or PDF format. Rarely, resumes could have an image format as well, especially while dealing with screen-prints. The RAP solution is designed for parsing resumes in DOC, PDF, JPG and PNG formats.

RAP Architecture

The overall architecture of Experion’s RAP solution combines pattern recognition, expert systems, and regular expressions to undertake intensive text analytics.

a) Pattern Recognition

Pattern recognition is used for capturing the name of a candidate. A pattern for capturing candidate names is identified after observing a considerable number of resumes. This pattern can identify names to a large extent accurately. Similarly, another pattern was identified and followed for isolating the overall work experience.

b) Expert System

An expert system is used on a smaller scale, mostly in isolating the text for fetching the candidate names and overall years of experience.

c) Regular Expression

Python regular expression (Regex) is used for extracting the email ids and contact numbers of the candidates. A certain level of formatting is also performed over the captured phone numbers so that they appear in a consistent format.

For example:

• 1234567890 is transformed to 123-456-7890
• 91 1234567890 is transformed to +91-123-456-7890 etc

The RAP solution is also capable of extracting more than one contact number and email ids.

How did  Experion’s Resume Auto Parser (RAP) solution benefit recruiters?

• Examine any number of resumes to extract and summarize required information in easily readable tabular format.
• Handle most file formats such as DOC PDF, JPG, PNG, etc.
• Fetch candidate information from screenshots.
• Provide information in sorted order of overall years of experience, which enables recruiters to map their requirements with a profile accurately.

Areas of improvement

The pattern recognition, though displays appreciable performance, still needs fine-tuning to be perfect. There are rare cases in which the expert system fails to isolate the text while recognizing a candidate name.

Reference & Courtesy

https://github.com/bjherger/ResumeParser

Our code is a profoundly improved/ refurbished version of the above.

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