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The 7 InsurTech Trends That Matter for 2021

The COVID-19 pandemic has triggered structural changes that have forced insurance players to become more competitive than ever. The pandemic has proved to be a catalyst, nudging insurers to prioritize their focus on improving customer centricity, market agility, and business resilience.

As per a report by Accenture, almost 86% of insurers believe that they must innovate at an increasingly rapid pace to retain a competitive edge.

‘Insurtech’, short for ‘insurance technology’, is a term being widely used these days to talk about the new technologies bringing innovation in the insurance industry. The digital disruption caused by technology is transforming the way we protect ourselves financially.

In this article, let’s explore the top insurtech trends for 2021 that will pave the way for the future of insurance. 

  1. Data-backed personalization

Insurance companies are increasingly drifting towards collecting data to understand customer preferences better. Using data collected from IoT devices and smartphones, insurance companies are trying to deliver customized advice, the right products, and tailored pricing. 

Personalization enables exceptional experiences for customers while offering them products and services tailored to their specific needs. The idea is thus to put customers at the core of their operations.

Some examples of data-backed personalization include the following –

  • Reaching out to customers at the right time. This involves pitching to customers when they are thinking of buying insurance like while making high-value purchases, during financial planning, or during important life events.
  • Reaching out to customers through the right channel. This involves reaching out to customers through appropriate platforms like a website or mobile app.
  • Delivering the right products to specific individuals. This involves delivering products to customers based on their specific needs like reaching out with auto insurance to a customer who travels often.

Take the example of the financial services company United Services Automobile Association. The organization collects data from various social media platforms and uses advanced analytics to personalize its engagement with customers. The company advises customers when they are buying automotive insurance or are looking to purchase a vehicle. The company also provides its customers tailored mobile tools to help them manage and plan their finances.

  1. Usage-based policies

One of the biggest trends in the insurance industry is the growth of usage-based policies. In the coming year, we are going to hear a lot more about the ever-growing popularity of short and very-short term insurance that needs to be activated quickly.

We are going to see the rise of dedicated apps that allow easily activating policies based on usage needs. For instance, one would be able to take insurance for a sports event or a travel plan.

  1. Robotic and cognitive automation (R&CA)

Both robotic process automation (RPA) and cognitive automation (CA) represent two ends of the intelligent automation spectrum. At one end of the spectrum, there is RPA that uses easily programmable software bots to perform basic tasks. At the other end, we have cognitive automation that is capable of mimicking human thought and action. 

While RPA is the first step in the automation journey for any industry, cognitive automation is expected to help the industry adopt a more customer-centric approach by leveraging different algorithms and technologies (like NLP, text analytics, data mining, machine learning, etc.) to bring intelligence to information-intensive processes. R&CA, therefore, encompasses a potent mix of automated skills, primarily RPA and CA.

In the insurance industry, there are vast opportunities for R&CA to ease many processes. Some of its use cases in the insurance industry include –

  • Claims processing – R&CA can help insurance companies gather data from various sources and use it in centralized documents to quickly process claims. Automated claims processing can reduce manual work by almost 80% and significantly improve accuracy.
  • Policy management operations – R&CA can help automate insurance policy issuance, thus reducing the amount of time and manual work required for it. It can also help in making policy updates by using machine learning to extract inbound changes from policy holders from emails, voice transcripts, faxes, or other sources.
  • Data entry – It can be used for replacing the manual data entry jobs, hence saving a significant chunk of time. There are still many instances where data like quotations, insurance claims, etc. is entered manually into the system.
  • Regulatory compliance – R&CA can be key in helping companies improve regulatory compliance by eliminating the need for human personnel to go through many manual operations that can be prone to errors. It helps reduce the risks of compliance breach and ensures the accuracy of data. Some examples of manual work that R&CA can automate include name screening, compliance checking, client research, customer data validation, and regulatory reports generation, etc.
  • Underwriting – It involves gathering and analyzing information from multiple sources to determine and avoid risks associated with a policy like health, finance, duplicate policies, credit worthiness, etc. R&CA can automate the entire process and significantly speed up functions like data collection, loss assessment, and data pre-population, etc.
  1. Data-driven insurance

Although insurance has always been driven by data, new technology means that insurers are likely to benefit from big data. Using valuable data insights companies can customize insurance policies, minimize risks, and improve the accuracy of their calculations.

Here are a few use cases of how insurance companies use big data – 

  • Shaping policyholder behavior – IoT devices that monitor household risk help insurers shape the behavior of policyholders.
  • Gaining insights on customer healthcare – Medical insurance companies are drawing insights from big data to improve recommendations in terms of immediate and preventive care.
  • Pricing – Companies are using big data to accurately price each policyholder by comparing user behavior with a larger pool of data.
  1. Gamification

Gamification is turning out to be a very interesting and promising strategy that may get a lot more popular in 2021. It involves improving the digital customer experience by applying typical dynamics of gaming like obtaining prizes, bonuses, clearing levels, etc.

Gamification has shown promise in increasing engagement and building customer loyalty. For example, an Italian insurance company was able to observe a 57% increase in customers (joining the loyalty program) due to a digital game created by the company.

  1. Smart contracts

Smart contracts are lines of code that are stored on a blockchain. These are types of contracts that are capable of executing or enforcing themselves when certain predetermined conditions are met.

The market for smart contracts is expected to reach a valuation of $300 million by the end of 2023.

The insurance sector can benefit from smart contracts because these can emulate traditional legal documents while offering improved security and transparency. Moreover, these contracts are automated, so companies do not need to spend time processing paperwork or correcting errors in written documents.

  1. Other key trends

Some other key trends that may be relevant in 2021 include – 

  • Extended reality – Although it’s still in its early days, extended reality can benefit the insurance industry by making data gathering much safer, simpler, and faster by allowing risk assessment using 3D imaging.
  • Cybersecurity – Since insurance companies are migrating towards digital channels, they also become prone to cyberattacks. That is why cybersecurity will remain a trend in 2021 as well.
  • Cloud computing – The year 2021 could witness cloud computing become more essential than ever before. 
  • Self-service – It allows customers to have an alternative path to traditional agents as per their need and convenience, and thus looks to pick up pace in 2021.

Conclusion

It can be concluded that the pandemic has accelerated the shift towards digital in the insurance industry. As for the trends for 2021, there seems to be a general inclination towards personalization, data mining, and automation in the industry.

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Conversational UI in Healthcare: Enhancing Patient Interaction with Chatbots

As healthcare becomes more patient-centric, the demand for efficient and personalized care continues to grow. One of the key technologies that have gained traction in this domain is Conversational UI (CUI) — a user interface where interactions occur through natural language, often with the help of chatbots. For developers, building a robust CUI in healthcare requires a balance of technical proficiency, understanding of the healthcare landscape, and empathy toward patient needs. Let’s explore how CUI can improve patient interactions through chatbots and what developers should consider during implementation.

Why Conversational UI is Gaining Popularity in Healthcare

From scheduling appointments to answering medical queries, healthcare chatbots have become vital tools for enhancing patient engagement and streamlining healthcare workflows. Conversational UIs enable these chatbots to interact with patients naturally, making them accessible even to non-tech-savvy users. By incorporating AI and NLP (Natural Language Processing), chatbots can now simulate human-like conversations, ensuring patients receive timely, relevant responses. 

Image credit: https://www.analytixlabs.co.in/blog/ai-chatbots-in-healthcare/ 

Key Areas Where Chatbots Are Revolutionizing Healthcare

  1. Appointment Scheduling and Reminders – Chatbots can automatically schedule appointments based on patient availability and send reminders before the visit, reducing no-show rates. For developers, this feature requires integration with hospital management systems (HMS) and calendar APIs. The challenge lies in ensuring secure and real-time data transfer while adhering to healthcare compliance standards like HIPAA.
  1. Medical Query Resolution– Chatbots equipped with NLP can answer common patient questions related to symptoms, medications, and treatment plans. This reduces the burden on healthcare providers, allowing them to focus on more critical tasks. Developers working on this feature need to consider integrating medical databases, such as SNOMED CT or ICD-10, for accurate and up-to-date information.
  1. Patient Monitoring and Follow-ups – Post-discharge, chatbots can monitor a patient’s condition by regularly asking for health updates (e.g., vital signs or medication adherence). Developers can integrate IoT devices, such as wearable health monitors, with chatbot platforms to collect real-time data, providing healthcare professionals with actionable insights.
  1. Mental Health Support – Chatbots have shown promise in offering mental health support by providing patients with an outlet to discuss their feelings and receive advice. Building these chatbots involves training them on therapeutic conversational frameworks like Cognitive Behavioral Therapy (CBT), ensuring they offer relevant advice while recognizing when a human intervention is required.

Key Considerations for Developers

1. Natural Language Processing (NLP) and AI Training

NLP plays a pivotal role in enabling chatbots to understand and process patient queries effectively. Developers must focus on the following:

Training Data: Start by gathering extensive datasets that include real-life medical queries and patient conversations. This ensures that the chatbot can recognize various intents and respond appropriately.

Multi-language Support: Healthcare is global, so building multi-lingual capabilities is critical. Using tools like Google’s BERT or Microsoft’s Turing-NLG models can help chatbots understand context in different languages.

Contextual Understanding: The chatbot must not just respond to individual queries but also maintain the context across the conversation. Developers can use contextual models that preserve the state of the conversation, ensuring personalized patient interactions.

2. Security and Compliance

Healthcare chatbots handle sensitive patient information, making security a top priority. Developers must ensure compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) in the U.S. and GDPR (General Data Protection Regulation) in Europe. Key practices include:

  • Data Encryption: All communication between the chatbot and the server must be encrypted using protocols like TLS (Transport Layer Security).
  • Authentication Mechanisms: Implement two-factor authentication (2FA) to verify patient identity, especially for sensitive tasks like accessing medical records.
  • Anonymization: To avoid accidental data breaches, ensure that the chatbot anonymizes data where possible.

3. Seamless Integration with EHR Systems

For chatbots to be truly effective in healthcare, they must integrate seamlessly with Electronic Health Record (EHR) systems. This requires a deep understanding of healthcare APIs like FHIR (Fast Healthcare Interoperability Resources) or HL7. Developers should aim to:

  • Enable Real-time Updates: Ensure that chatbot interactions (e.g., new appointment schedules, and symptom checks) are instantly reflected in the patient’s EHR.
  • Avoid Data Silos: Ensure that all systems (EHR, chatbot, scheduling system) can communicate with each other, eliminating data silos that can lead to fragmented patient information.

4. Scalability and Performance Optimization

In healthcare, downtime can be critical. Developers need to ensure that chatbots are scalable and capable of handling thousands of patient interactions simultaneously. Using cloud-based platforms (AWS, Google Cloud) that offer auto-scaling capabilities can help. Additionally, performance optimization can be achieved by:

  • Caching Responses: Store frequently used responses (such as FAQs) in memory to speed up interaction times.
  • Load Balancing: Implement load balancers to distribute incoming queries across servers, ensuring no single server is overwhelmed.

Tools and Platforms for Building Healthcare Chatbots

Several tools and platforms can aid developers in building healthcare chatbots with conversational UIs:

  1. Dialogflow (Google): Offers pre-built healthcare intents and integrates with Google Cloud’s healthcare APIs.
  2. Microsoft Bot Framework: A scalable platform that integrates with Azure services and offers AI-driven insights.
  3. Rasa: An open-source NLP tool that provides flexibility in creating highly customized healthcare bots.

Conclusion

Conversational UI in healthcare is transforming patient care by offering real-time, scalable, and personalized interactions through chatbots. However, for developers, building these systems goes beyond programming chatbots — it involves understanding the unique challenges of healthcare, from regulatory compliance to seamless integration with hospital systems. By focusing on NLP capabilities, ensuring security and privacy, and integrating with existing healthcare infrastructure, developers can create chatbots that not only enhance patient interaction but also alleviate the burden on healthcare providers.

References

  1. NLP in Healthcare: Opportunities and Challenges
  2. HIPAA Compliance for Chatbots

About the Author:

Shristi is a creative professional with a passion for visual storytelling. She recently transitioned from the world of video and motion graphics to the exciting field of product design at Mantra Labs. When she’s not designing, she enjoys watching movies, traveling, and sharing her experiences through vlogs.

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