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Contactless Solutions in Insurance

3 minutes, 53 seconds read

Last decade was benchmark for contactless technology, which was mainly confined to payments. In 2014, with the launch of ApplePay followed by Android Pay and Samsung Pay, digital wallets played an important role in raising the bar for digital payment experiences. Another remarkable breakthrough in the contactless payments can be attributed to NFC-only debit cards introduced in 2016 by Erste Group Bank AG.

Now (the 2020s), we’re about to witness another disruption in contactless digital experiences, which will cover many different business spheres including insurance. 

However, prolonged lockdowns and the need for social distancing amidst the COVID crisis has shifted consumer preference towards digital. Consumers are now ready to adopt digital technologies — appreciating the contactless approach by Insurers.

Today’s consumers expect personalization, convenience, and greater levels of customer service satisfaction regardless of insurers, assets, and geography. Soon, we may resume socializing, but there sure will be a change in the way we interact with our environment. 

This article highlights the emerging contactless solutions in Insurance.

Claims Inspection

Going by the traditional physical inspection way, even a simple motor claim may take 5-7 working days. For instance, after a customer has intimated the insurer about the accident, the Insurer would assign a surveyor to assess the extent of damage/loss and authenticate the incident. 

This process is not only time consuming, but also requires the surveyor to visit the location, assess the damage, and process documents. 

Self-service claims portals can help customers register, inspect, and settle their motor insurance claims in a comparatively shorter time. It also eliminates field-visits for the surveyor.

The technology that is creating an impact here is Machine Vision. It can analyze damaged parts and the severity of damage through the photographs submitted by the customers. 

Trillium Mutual Insurance, Bajaj Allianz are already using contactless claims solutions for their policyholders.

[Also read: How Machine Vision can Revolutionize Motor Insurance]

Policy Distribution

Agents have been a predominant channel for insurance distribution for decades. In 2019, the new-age tech-savvy customers posed a threat to traditional agent-based selling in Insurance. The current COVID crisis has confused businesses as to which channel to opt. The elder generation, who preferred face-to-face communication while buying a policy, planning investment, etc. are reluctant to meet people. 

In this situation, multilingual/vernacular chatbots can handle pre and post-sales queries; thus, eliminating the need for agents/RMs to meet clients and prospects physically. 

Chatbots equipped with language processing capability can be a great contactless solution for policy distribution. They can eliminate human interaction in areas such as First Notice of Loss (FNOL) and customer support.

“The new normal is when people learn how to do contactless selling. Covid-19 has brought a change in universal behavior..everybody realizes the need for social distancing, the need to go digital and this is where people are more amenable to being sold to digital. Insurers who accomplish contactless sales today are the ones who will be able to make a difference going forward.”

K V Dipu, President — Operations, Communities & Customer Experience, Bajaj Allianz General Insurance

[Also read: ‘Digital’ Insurance Broker: The case for a digital brokerage]

Another aspect of this case is equipping agents with technical knowledge and they can help clients/prospects on “how to” situations through video chats.

API Integration

In the API-based business model, apart from traditional distribution channels, 3rd party apps allow customers to buy/renew insurance policies. 

Digital wallets like PayTM and PhonePe (in India) have updated their interface to allow essential payments to the fore including insurance premiums. The API-based approach in Insurance is gaining momentum as it allows contactless payments and adds convenience for the user.

[Also read: Four New Consumer-centric Business Models in Insurance]

Contactless Solutions: Field Survey using Drones

Drones carry the ability to extract accurate field information, which can fuel real-time analytics using artificial intelligence and machine learning. MarketsandMarkets estimates the Indian drone software market to reach $12.33 billion by 2022. Drones can fulfill two strategic objectives for Insurers:

  1. Risk management: through efficient field data collection, analysis, and actionable insights 
  2. Operational costs management: through effective claims adjudication, claims processing, and customer experience.

The Future

Gradually, the world will move towards a contactless ecosystem. Most of the processes will be automated and wearables and mobile devices will dominate business-to-customer interactions. 

Automotive business, which totally relied on the dealership and offline sales has adapted itself to operate online amidst this crisis. Companies like BMW, Hyundai, Volvo, and Peugeot have already introduced contactless online sales globally.

The point is — people are giving a thought to buying an expensive asset without physically examining it. Digital channels are giving almost similar experiences as physical channels to both consumers and businesses.

In the Insurance landscape, people are open to buying policies online, and at the same time, Insurers are ready to rely on technology for claims investigation, underwriting, and fraud detection. 

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The Million-Dollar AI Mistake: What 80% of Enterprises Get Wrong

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When we hear million-dollar AI mistakes, the first thought is: What could it be? Was it a massive investment in the wrong technology? Did a critical AI application go up in flames? Or was it an overhyped solution that failed to deliver on its promises? Spoiler alert: it’s often all of these—and more. From overlooked data science issues to misaligned business goals and poorly defined AI projects, failures are a mix of preventable errors.

Remember Blockbuster? They had multiple chances to embrace advanced technology like streaming but stuck to their old model, ignoring the shifting landscape. The result? Netflix became a giant while Blockbuster faded into history. AI failures follow a similar pattern—when businesses fail to adapt their processes, even the most innovative AI tools turn into liabilities. Gartner reports nearly 80% of AI projects fail, costing millions. How do companies, with all their resources and brainpower manage to bungle something as transformative as AI?

1. Investing Without a Clear Goal

Enterprises often treat artificial intelligence as a must-have accessory rather than a strategic tool. “If our competitors have it, we need it too!” they exclaim, rushing into adoption without asking why. The result? Expensive systems that yield no measurable business outcomes. Without aligning AI’s capabilities—like natural language processing or generative AI solutions—with goals such as boosting customer experience or driving operational efficiency, AI becomes just another line item in the budget.

2. Data Woes

AI is only as smart as the data it’s fed. Yet, many enterprises underestimate the importance of clean, structured, and unbiased data. They plug in inconsistent or incomplete data and expect groundbreaking insights. The result? AI models that churn out unreliable or even harmful outcomes.

Case in Point: A faulty ATS filtered for outdated AngularJS skills, rejecting all applicants, including a manager’s fake CV. The error, unnoticed due to blind reliance on AI, cost the HR team their jobs—a stark reminder that human oversight is critical in AI systems.

3. Underestimating the Human Element

AI might be powerful, but it does not replace human judgment.  Whether it’s an AI assistant like Claude AI or OpenAI’s ChatGPT API, Enterprises often overlook the need for human oversight and fail to train employees on how to interact with AI systems. What you get is either blind trust in algorithms or complete resistance from employees, both of which spell trouble.

4. Stuck in Experiment Mode

AI adoption often stagnates when businesses fixate on piloting instead of scaling. Tools like DALL-E or MidJourney may excel in proofs of concept but lack enterprise-wide integration. This leaves companies in an endless cycle of testing AI applications, wasting resources without realizing full-scale business value.

5. Ignoring Change Management

Transitioning to AI technology is as much about organizational culture as it is about deploying AI models. Mismanagement, such as overlooking ethical AI considerations or failing to explain AI’s impact on roles, leads to resistance. Whether it’s a small chatbot AI tool or full-scale AI automation, fostering employee buy-in is critical.

Source: IBM

How to Avoid These Pitfalls

  1. Start with Strategy: Define clear objectives for adopting artificial intelligence programs.
  2. Invest in Data: Build a robust data infrastructure. Clean, unbiased, and relevant data is the foundation of any successful AI initiative.
  3. Prioritize Education and Oversight: Train teams to work with AI and establish clear guidelines for human-AI collaboration.
  4. Think Big, but Scale Smart: Start with pilots but plan to expand AI in finance, healthcare, operations or other areas from day one.
  5. Focus on Change Management: Communicate the value of tools like AI robots or AI-driven insights to teams at all levels.

Graph of AI adoption across different countries

Source:IBM.com

Mantra Labs is Your AI Partner for Success

At Mantra Labs, we don’t just offer AI solutions—we provide a comprehensive, end-to-end strategy to help businesses adopt the complex process of AI implementation. While implementing AI can lead to transformative outcomes, it’s not a one-size-fits-all solution. True success lies in aligning the right technology with your unique business needs, and that’s where we excel. Whether you’re leveraging AI in healthcare with tools like poly AI or exploring AI trading platforms, we craft custom solutions tailored to your needs.

By addressing challenges like biased AI algorithms or misaligned AI strategies, we ensure you sidestep costly pitfalls. Our approach not only simplifies AI adoption but transforms it into a competitive advantage. Ready to avoid the million-dollar mistake and unlock AI’s full potential? Let’s make it happen—together.

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