AI can help bridge customer gaps for microinsurers

Published on Jul 30, 2019

Updated on May 18, 2020

Microinsurance targets low-income households and individuals with little savings. Low premium, low caps, and low coverage limits are the characteristics of microinsurance plans. These are designed for risk-proofing the assets otherwise not served by traditional insurance schemes.

Because microinsurance comprises of low-premium models, it demands lower operational cost. This article covers insights on how AI can help bridge customer gaps for microinsurers.

Challenges in Distributing Microinsurance Policies

Globally, microinsurance penetration is just around 2-3% of the potential market size. Following are the challenges that companies providing microinsurance policies face-

Being a forerunner in a competitive landscape.
Making policies accessible through online channels.
Developing user-friendly interfaces understandable to a layman.
Improving the organization’s operational efficiencies by automating repetitive processes.
Responsive support system for both agent and customer queries.
Quick and easy reimbursements and claim settlements.

Fortunately, technology is capable of solving customer support, repetitive workflow, and scalability challenges to a great extent. The subsequent section measures the benefits of AI-based technology in the microinsurance sector.

Benefits of Technology Penetrating the Microinsurance Space

#1 Speeds up the Process

Paperwork, handling numerous documents, data entry, etc. are current tedious tasks. AI-driven technologies like intelligent document processing systems can help simplify the insurance documentation and retrieval process.

For example, Gramcover, an Indian startup in the microinsurance sector uses direct-document uploading and processing for faster insurance distribution in the rural sector.

Gramcover - automated document processing for faster microinsurance distribution

#2 Scalable and Cost-effective

Because of scalability, technology has also enabled non-insurance companies to distribute insurance schemes on a disruptive scale.

Within a year of launching the in-trip insurance initiative, cab-hailing service — Ola, is able to issue 2 crore in-trip policies per month. The policy offers risk coverage against baggage loss, financial emergencies, medical expenses, and missed flights due to driver cancellations/ uncontrollable delays.

AI-based systems are also cost-effective in the long run because the same system is adaptable across different platforms and is easily integrated across the enterprise.

The microinsurance space is in need of better customer-first policies that are both convenient and flexible to use. ‘On & Off’ microinsurance policies for farmers, especially when they need it, can bring about a change in their buying behavior. The freedom to turn your insurance protection off, when you are not likely to use or benefit from it can give customers the freedom to use a product that maximizes their utility.

At the same time, insurers will be able to diffuse their products with greater spread across the rural landscape because the customer is able to derive greater value from it.

#3 Easy and Customer-friendly Claims

Consumers want faster reimbursements against their plans. Going with the traditional process, claim settlement may take several months to approve. Through distributed ledgers and guided access, documents or information can be made available in a fraction of seconds.

MaxBupa, in association with Mobikwik, has introduced HospiCash, a microinsurance policy in the health domain. It has identified the low-income segment’s needs and accordingly takes cares of out of pocket expenses (@ ₹500/day) of the customers.

Mobikwik wallet ensures hassle-free everyday money credit to the user.

MaxBupa X MobiKwik Hospicash policy covering out-of-pocket expenses during hospitalization

Another example of easy claim settlement is that of ICICI Lombard motor insurance e-claim service. InstaSpect, a live video inspection feature on the Lombard’s Insure app allows registering claim instantly and helps in getting immediate approvals. It also connects the user to the claim settlement manager for inspecting the damaged vehicle over a video call.

Real-time inspection and claims can benefit farmers. In the event of machine or tractor breakdown, they need not wait for days for the claim inspector to come in-person and assess the vehicle. Instead, using Artificial Intelligence and Machine Learning models, the inspection can be carried out within seconds via an app, following which the algorithm can determine (based on trained models) to approve or reject the claim.

#4 Automating Repetitive Tasks

Entering data manually is subject to human error, whereas, data entered through scanners, document parsers, etc. are up to 99.94% accurate.

Microinsurance sector is also a victim of self-centered human behavior, where agents consider personal profit before the benefit of the user. Automating the customer/agent onboarding journey can improve the distributed sales network model too.

MaxBupa uses FlowMagic for processing inbound documents, for enterprise-wide flexibility and fit. With AI, they are able to halve the manned human effort for gains in operational accuracy.

Automation can bring down the challenges of mis-selling, moral hazard, and distribution costs to level zero with agnostic digital systems.

#5 Operational Efficiency

Where human employment calls for dedicated working hours, with chatbots, a large number of queries can be handled anytime during the day, weekends, and holidays. It is even convenient for customers also.

Religare, India’s leading insurance provider has introduced AI-based chatbots that can handle customer queries without needing human intervention. It is capable of helping a customer to buy or renew a policy, schedule appointments, updating contact details, and more. This technology has helped Religare to increase sales by 5X and increase customer interaction by 10X.

The microinsurance sector can also take advantage of chatbot technology to improve response time.

Final Thoughts

As more microinsurance products continue to surface in the market, insurers need to place the rural customer upfront and center of their strategic efforts. By understanding and fulfilling the rural insuree’s needs, cutting down operational costs through process automation such as adding AI-powered chatbots to handle general queries or quickly settling claims without the need for unnecessary human intervention — microinsurers can realize better market penetration and adoption for these policies.

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.

AI can help bridge customer gaps for microinsurers

Challenges in Distributing Microinsurance Policies