InsurTech is transforming the life insurance sector in 5 ways

Published on Aug 7, 2018

Updated on Oct 16, 2019

The technology is overpowering the traditional business models, and each sector is gradually going the digital way to meet the evolving customer expectations. Life insurance is a sector that is still in the nascent stages of digitization due to the amount of complexity and sensitivity it involves. Insurance startups are hell-bent on leveraging the new technologies to remodel the design and delivery of the life insurance.

Insurance startups are making use of analytic and digital tools to develop life insurance products that are flexible and fast to deliver. The goal of these InsurTech innovations is to decrease the total time for the application process and create a comfortable setting for the customers. The key to implementation of these innovations is that they should be compliant with the insurance law and regulations.

The InsurTech innovations for life insurance will include:

1. RPA and AI for core processes:

The automation of core processes is essential as it helps to speed up the processing of the policies and servicing customer requests. RPA (Robotic process automation) and AI work together to process the structured and unstructured data respectively. AI backed Insurance chatbots can help the consumers to chat and converse with their providers and get solutions to their queries immediately. InsurTech as a service need to handle large volumes of data obtained from connected devices like the social media and other resources which can be easily done through automation. As there is a lot of paperwork involved with life insurance policies, automation is a great way to avoid human errors and save some time.

2. Smart contracts:

Blockchain has deeply impacted the technology sector and the blockchain based smart contracts are a game changer in automating the life insurance policy claims. It works on the concept of the decentralized ledger where each customer has a copy of the ledger, and he can commit to a transaction independently. The smart contract can be processed automatically based on a set of pre-defined conditions. It is a great way to enhance the operational efficiency and process the claims quickly.

3. Predictive analysis:

Predictive analysis plays an important role to analyze the needs of the current as well as future customers. Life insurance companies can make use of the actionable analysis to find the past as well as the real-time trends and accordingly plan out their strategy. It helps to design personalized offerings based on the inputs from the customers. InsurTech consulting services need this information for providing meaning consultancy to their customers.

4. Advanced analytics for fraud prevention:

The reports suggest that insurance companies suffer losses of at least 3% due to fraudulent activities. So, the insurance companies are determined to leverage the benefits of advanced analytics that is backed by AI for a more trusted, reliable and transparent environment with their customers. The customer data from various resources like mobile devices, social media channels are analyzed and monitored continuously for any behavioral patterns anomaly.

5. Cloud technology:

Life insurers are also leveraging the capabilities of the cloud for it is capable of handling huge volumes of data from varying sources like the wearables or the social media channels or any other electronic devices. The cloud is also beneficial when it comes to saving IT deployment costs due to the inflexibility of IT infrastructure, in cases of underuse and under capacity.

Technical innovation in the field of life insurance has just started to evolve. The above-mentioned technical aspects will form the foundation of InsurTech innovation and will even go far beyond it in the coming future. We can wait and see how it will transform the life insurance sector in the near future.

Know the Mantra Labs capabilities in InsurTech and reach out to us for any query.

References:

https://www.jdsupra.com/legalnews/insurtech-innovations-in-life-insurance-69458/

https://www.capgemini.com/wp-content/uploads/2017/12/life-insurance-top10-trends-2018.pdf

https://www.capgemini.com/2018/06/insurtech-opens-new-life-insurance-frontiers/

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

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.

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.

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.

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.

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

Tools and Platforms for Building Healthcare Chatbots

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

Dialogflow (Google): Offers pre-built healthcare intents and integrates with Google Cloud’s healthcare APIs.
Microsoft Bot Framework: A scalable platform that integrates with Azure services and offers AI-driven insights.
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

NLP in Healthcare: Opportunities and Challenges
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.