Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(6)

Manufacturing(4)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(41)

Insurtech(67)

Product Innovation(59)

Solutions(22)

E-health(12)

HealthTech(25)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(154)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

Insurtechs are Thriving with Machine Learning. Here’s how.

Modern Insurance is only around 250 years old, about when the necessary statistical and mathematical tools to underwrite a business venture came to be. But statistical models, even the most advanced ones, need a very specific type of enriched data-diet for it to work optimally. Since then, the industry has always had to rely on data for ensuring its long financial health. For insurers to take on considerable risk, regardless of size, it draws on the reassurance of statistically-sound data that underpins the coverage needed (for issuance) to a fixed number. This ‘number’ will influence the amount of coverage (or claim) provided to the insuree and consequently the amount of premium to be collected.

Such is the reliance on data, that even the slightest erroneous mistake in the underwriter’s predictions could bankrupt, at times, even the economy. We’ve seen it before — when banks took on unqualified risks and approved subprime mortgage loans to borrowers with poor credit, creating the imploding housing bubble of ‘08.

The nature of risk simply evolves and devolves; while Insurers learn progressively with each individual case, adsorbing enormous amounts of data into their carefully crafted risk-models. These models then naturally aid in the manual effort of several hundred data scientists (in the case of large insurers) poring over immense amounts of psychographic, behavioral and environmental attributes for evaluating an entity’s risk profile. Yet, even with these measures, the risk is unquantifiable if the data scientist doesn’t have a large or clear enough picture to make sense of all the inbound information. 

In the age of machine intelligence, data is prime fodder for these advanced algorithms. They are designed to thrive on large datasets — in fact the larger the size, the better the system learns. How could it not? An AI system is decidedly 1000x faster than human computing, raising accuracy levels to near perfection and improving straight-through processing to nearly one in every two decisions made without human intervention, today.


Source: Accenture Report — Machine Learning in Insurance

20.4 billion things will be connected by 2020 creating an unprecedented level of data handling & insight derivation capacity, as BFSI companies alone will spend US$25 billion on AI in 2020 (as reported by IDC research). Since 2012, more than $10 billion has been invested in insurtechs.

For 2020 and beyond, customers will come to expect better personalization from their insurance policies, especially millennials and younger. While the incumbent, slow-moving giants of traditional insurance should surprise no one as being the last to innovate — new insurtechs like Flyreel are changing the paradigm by piloting Machine Learning projects that directly translates to critical business goals.

According to McKinsey, digital insurers are already achieving better financial and efficient go-to-market results compared to traditional players.

Here are three ways, insurtechs are gaining ground with Machine Learning (specifically where learning from data is involved):

  1. Risk Prediction
    Predicting and evaluating risk is insurance’ oldest use case, and research reveals it will continue to be so. With ML and advanced algorithms, insurers can process big data from multiple data points such as policy contracts, claims data, weather parameters, crime data, IoT and sensor data.
    By Analysing existing data, identifying anomalies, tracking recurring usage patterns and then delivering accurate predictions and diagnosis through vertically-tuned algorithms — ML-based platforms can identify risk ratios and risk profiles that enable insurers to customize policies for individual customers in real-time. This differs from ‘off-the-shelf’ platforms which can only be utilized to solve a narrow set of problems.

  2. Customer Lifetime Value (CLV) Prediction
    CLV is a complex metric that represents the value of a customer to an organization as the difference between the revenue gained and expenses incurred – all projected onto the entire relationship with a customer, including the future.
    Insurers can now predict CLV using customer behavior data that allows them to assess the customer’s potential profitability for the insurer. Behavior-based learning models can be applied to forecast retention or cross-buying, all critical factors in the company’s future income. ML tools also help insurers to predict the likelihood of particular customer behavior – for example, their maintenance of the policies or surrender.

  3. Personalization Insights Engine
    User data from AI, machine learning and behavioral and social sciences can provide actionable insights in real time. For example, simulation and learning capabilities allow companies to discover new customer groups, to help companies personalize customer engagement, risk assessment, and forecasting by combining data from multiple sources.
    A common challenge is capturing data from multiple sources and turning the data into insights that can inform business decisions across many functions. With machine learning, insurers will be able to underwrite, adjust customer journeys, resolve claims and adapt offerings.

ML-based solutions bring back real value to insurers — either delivered as a standalone product or as a part of an embedded process/service. The key for insurers is to pilot ML projects of smaller scale that can bring about cost and time savings across the organization almost immediately and then improve in easier iterative sprints for more future-ready permanence, rather than taking on the task of a complete enterprise makeover from day one!

For more information about how we can help enterprises begin their ML transformation, reach us on hello@mantralabsglobal.com

Cancel

Knowledge thats worth delivered in your inbox

NPS in Insurance Claims: What Insurance Leaders Are Doing Differently

Claims are the moment of truth. Are you turning them into moments of loyalty?

In insurance, your app interface might win you downloads. Your pricing might drive conversions.
But it’s the claims experience that decides whether a customer stays—or leaves for good.

According to a survey by NPS Prism, promoters are 2.3 times more likely to renew their insurance policies than passives or detractors—highlighting the strong link between customer advocacy and retention.

NPS in insurance industry is a strong predictor of customer retention. Many insurers are now prioritizing NPS to improve their claims experience.

So, what are today’s high-NPS insurers doing differently? Spoiler: it’s not just about faster payouts.

We’ve worked with claims teams that had best-in-class automation—but still had low NPS. Why? Because the process felt like a black box.
Customers didn’t know where their claim stood. They weren’t sure what to do next. And when money was at stake, silence created anxiety and dissatisfaction.

Great customer experience (CX) in claims isn’t just about speed—it’s about giving customers a sense of control through clear communication and clarity.

The Traditional Claims Journey

  • Forms → Uploads → Phone calls → Waiting
  • No real-time updates
  • No guidance after claim initiation
  • Paper documents and email ping-pong

The result? Frustrated customers and overwhelmed call centers.

The CX Gap: It’s Not Just Speed—It’s Transparency

Customers don’t always expect instant decisions. What they want:

  • To know what’s happening with their claim
  • To understand what’s expected of them
  • To feel heard and supported during the process

How NPS Leaders Are Winning Loyalty with CX-Driven Claims and High NPS

Image Source: NPS Prism

1. Real-Time Status Updates

Transparency to the customer via mobile app, email, or WhatsApp—keeping them in the loop with clear milestones. 

2. Proactive Nudges

Auto-reminders, such as “upload your medical bill” or “submit police report,” help close matters much faster and avoid back-and-forth.

3. AI-Powered Document Uploads

Single-click scans with OCR + AI pull data instantly—no typing, no errors.

4. In-the-Moment Feedback Loops

Simple post-resolution surveys collect sentiment and alert on issues in real time.

For e.g., Lemonade uses emotional AI to detect customer sentiment during the claims process, enabling empathetic responses that boost satisfaction and trust.

Smart Nudges from Real-Time Journey Tracking

For a leading insurance firm, we mapped the entire in-app user journey—from buying or renewing a policy to initiating a claim or checking discounts. This helped identify exactly where users dropped off. Based on real-time activity, we triggered personalized notifications and offers—driving better engagement and claim completion rates.

Tech Enablement

  • Claims Orchestration Layer: Incorporates legacy systems, third-party tools, and front-end apps for a unified experience.
  • AI & ML Models: For document validation, fraud detection, and claim routing, sentiment analysis is used. Businesses utilizing emotional AI report a 25% increase in customer satisfaction and a 30% decrease in complaints, resulting in more personalized and empathetic interactions.
  • Self-Service Portals: Customers can check their status, update documents, and track payouts—all without making a phone call.

Business Impact

What do insurers gain from investing in CX?

A faster claim is good. But a fair, clear, and human one wins loyalty.

And companies that consistently track and act on CX metrics are better positioned to retain customers and build long-term loyalty.

At Mantra Labs, we help insurers build end-to-end, tech-enabled claims journeys that delight customers and drive operational efficiency.
From intelligent document processing to AI-led nudges, we design for empathy at scale.

Want a faster and more transparent claims experience?

Let’s design it together.
Talk to our insurance transformation team today.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot