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5 InsurTech Trends for 2023

3 minutes read

For 2023, we believe that InsurTech will be used to supplement the rising concerns of inflation, arrested economic development, and heavily burdened pension schemes by catering to customers with greater attention to detail. 

# Digitally Enabled CX 

Insurance models in the present context have become bloated and complicated to the point where customers feel alienated. Customer needs are also converging across a wide range of areas: health, retirement, and investment management, to name a few. Simplifying the existing delivery model is key, and one such model that is likely to emerge is that of being a ‘distribution specialist’.

These firms are predominantly client-centric and extremely capital-light as they do not take on balance sheet risks. These firms will invest heavily in client-facing technology, and those that curate a delectable insurance discovery and delivery experience will have a huge leg-up over their peers. These developments are in line with Gartner’s predictions for the InsurTech industry, where digitally enabled CX is listed as a key success factor for InsurTech in the coming years.

# InsurTech native Telematics

Analysts and experts alike have been citing usage-based insurance programs as the next big thing in the world of insurance for nearly two years now. But how effective can usage-based programs be if they rely entirely on the customer to predict their decisions and make purchases accordingly? 

This is where telematics systems come in. As cars become increasingly ‘smart’, it will become easier and cheaper to integrate telematics into the insurance plan to implement a real-time ‘pay as you go’ plan. Telematics will be crucial for developing markets in Asia as societies become increasingly digitized and people start to get comfortable with the idea of insuring themselves and their vehicles separately. 

# Algorithmic Risk Assessments

Research has shown that with the application of machine learning models to the risk assessment strategies employed by risk analysts, Insurance companies can decrease the time taken to evaluate customer profiles by allowing faster servicing and thereby leading to greater customer loyalty and satisfaction. This will allow companies to process claims swiftly and accurately, thereby allowing risk assessment professionals to focus on refining their models.

Some firms have already demonstrated success by incorporating AI into their workflows. Lemonade, an insurance company that is ‘digital first’ has seen massive success by using AI to facilitate claims, quotes, and personalizing prices and interactions with individual customers.

# Broadening capabilities in the Metaverse

With over $25Bn dollars having been invested into it by Facebook alone, Metaverse is here to stay for the long run. And for Insurers, the possibilities offered by metaverse are hard to ignore. This means they finally have a tool to combine the efficiencies of AI-powered chatbots, with the warmth of face-to-face interactions. Internal training, conducting sales pitches, and using NFTs to verify personal documents are some of the most highly anticipated use cases.

Max Life insurance, a leading Indian insurance player has already started to think about how best to use the metaverse to boost employee engagement and morale.

# Disruptors will strive to stay afloat

Much of what made new-age insurers attractive to customers was the way they structured themselves (tech-first, expedited claims, etc.) that were antithetical to running an insurance business at scale. Kimberly Harris-Ferrante of Gartner predicts that the coming year will see a lot of new Insurtechs pivot to more traditional operating models, with the successful ones being acquired and the others being forced to shut shop.

Some have already closed down, such as GoBear (Asia Pacific) citing increasing regulatory and compliance pressures as the primary reason. Other examples include Kinsu (from Latin America) and Coverly for small businesses.

Conclusion: 

2023 is likely to see the beginning of the final stretch of digital transformation in the insurance industry as many have already caught on to the basics that are required to run a robust digitally-enabled sales and servicing operation. Conservatism will go hand-in-hand with novel, disruptive technologies as incumbents will lap up all existing software capabilities to bolster direct distribution, simpler delivery mechanisms, and a narrower focus on servicing the customer. Expect greater use of APIs, hybrid cloud architectures, and ‘headless tech’ in the coming year.

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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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