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/

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

By : Sampada

Published on Nov 26, 2024

AI Artificial Intelligence Data Science

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.

How to Avoid These Pitfalls

Start with Strategy: Define clear objectives for adopting artificial intelligence programs.
Invest in Data: Build a robust data infrastructure. Clean, unbiased, and relevant data is the foundation of any successful AI initiative.
Prioritize Education and Oversight: Train teams to work with AI and establish clear guidelines for human-AI collaboration.
Think Big, but Scale Smart: Start with pilots but plan to expand AI in finance, healthcare, operations or other areas from day one.
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

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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