Insurance Agent – Would INSURTECH Complement or replace them?

Published on May 22, 2018

Updated on Oct 16, 2019

I remember my young days when one of my close relatives started working as an Insurance Agent, and it was considered to be a precious job. A few days back when I met him, I asked him – Has the latest technology affected your work? I was expecting a negative answer, but instead, he showed me the flip side of how insurtech is helping him make a more personalized and long-term relationships. While some of the mundane jobs like data entry, reminders, quotes comparison, underwriting has been taken up by the insurtech technology, individuals, business owners or firms are yet inclined to interact with a human being and close the deal.

He mentioned a couple of analogies to strengthen his case – he said e-commerce industry in India that is worth US$125 billion as of April 2017 was not able to displace the brick and mortar industry. Or has the automobile industry removed pedestrians from the road?

Or the most important one – has the digitization removed the government agents that help citizens in getting their vehicle registered or apply for the PAN card?

Or has the financial inclusion initiative detached Banking agents or business correspondents from the chain? With a big smile on his face, he was sure digital move will only enhance his powers as insurance agents and not displace them.

Let’s take a look “HOW INSURTECH WOULD STRENGTHEN INSURANCE AGENT”

CHATBOTS HELPING IN CONVERSATION

Let’s accept it, on the one hand, people are busy and may not be available for sales pitch even it’s a tedious job for the insurance agent to repeat the same introduction to every prospective client, and that’s why – chatbots are handy. Start a conversation at your ease, and get full information handy. If you are satisfied, book an appointment with the agent, and he would come and guide you further.

DATA DATA EVERYWHERE

InsurTech is helping the insurance firms in churning the data that supports them in analyzing their needs and preferences. The same data could be used by insurance agents to grab more clients. Just for example if an individual is a wanderlust, he might be more interested in getting customized travel insurance rather than home or term insurance. Such kind of data when provided to an insurance agent, he could use his expertise to present a sales pitch to the client and help him in also getting customization if needed.

VALUE ADDED SERVICES

While play store may be filled with apps that provide dos and don’ts of insurance, individuals expect a human touch that offers “personalization with freebies.” Insurance agents could tap into this domain by also offering value-added services to make their client feel special. Just for example – using a robo advisor to generate a financial planning portfolio for your prospective client could help in trusting the agents and building a long-term relationship.
Undoubtedly technologies’ like Artificial Intelligence[AI], Natural Language Processing [NLP] would help in improving the financial decision, but they are also strengthening the agents in providing value-added services and making it a more engaging experience.

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References:

https://www.insurancebusinessmag.com/asia/news/breaking-news/tech-to-complement-not-replace-insurance-50228.aspx

https://dailyfintech.com/2017/11/30/insurtechisagentspinach/
https://www.insurancejournal.com/magazines/mag-features/2017/04/03/446012.htm

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.