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5 Deep Learning Use Cases for the Insurance Industry

4 minutes, 9 seconds read

In 2010, with the launch of the Image Net Competition, a vast dataset of about 14 million labeled images was made open-source to inspire the development of cutting-edge image classifiers. This was when Deep Learning technology got its a real breakthrough and since then there’s been no looking back for advancements in this field.

Different industries are actively using Deep Learning for object detection, features tagging, image analysis, sentiment analysis, and processing data at extremely high speeds. The bigger benefit that differentiates Deep Learning from other AI and ML technologies is the ability to train vast amounts of unstructured data in near real-time. Organizations with a strong focus on data are already about 1.5 times more likely to invest in Deep Learning for actionable insights — Forrester Predicts.

What makes Deep Learning Technology so sought after?

Let’s take a look at 5 Deep Learning use cases from an insurance perspective.

5 Noteworthy Deep Learning Use Cases in Insurance

Deep Learning (DL) is a branch of Machine Learning, which is based on artificial neural networks. DL techniques are specifically useful for determining patterns in large unstructured data. It is highly beneficial for assessing damages during an accident, identifying anomalies in billing, etc. that can eventually help in fraud detection and better customer experiences.

The insurance industry can leverage Deep Learning technology to improve service, automation, and scale of operations. 

1. Property analysis

Typically, insurers analyze a property only once before quoting an insurance premium. However, a customer may remodel the property, for instance, install a swimming pool. 

Under such instances, Insurers can proactively modify the insurance coverage with the help of deep learning technology. In fact, with DL technology, Insurers can help their customers with predictive maintenance, fault analysis, and real-time support. 

For example, Enodo provides underwriting for multifamily properties. It allows users to analyze historical rent, concession data, and market values. Such data-driven tools are also a great aid for insurers.

2. Personalized offers

Insurers are seeking different ways to enhance the customer experience. Deep Learning can vividly improve interaction experiences at different customer touch-points. Take for instance — marketing outreach. Through personalized recommendations and dynamic remarketing strategies, insurers can achieve better conversions. McKinsey states that personalization can reduce customer acquisition costs by up to 50%

At the core of these strategies lies Deep Learning technology. DL technology can make logical classifications of unstructured data through unsupervised learning. We’ve already seen product recommendations based on our own preferences, browsing/search patterns, and peers’ interests. The same applies to the insurance industry, especially when insurers endeavor profits through bite-size and on-demand insurance products.  

3. Pricing/Actuarial analysis

Actuarial analysis and evaluation are both time-consuming and error-prone processes. Insurers can considerably improve policy pricing through automated reasoning. Deep Learning techniques combine statistics, finance, business, and case-based reasoning and can assist actuaries in better risk assessments. Accenture reports — Insurers are leveraging machine learning for underwriting in P&C (56%) and life (39%) insurance sectors

  1. Explainable AI (XAI) is capable of adopting and implementing AI across all capacities of the actuarial profession. 
  2. Pattern recognition from historical data can help assess the risk and understand the market better.
  3. Deep Learning can help in pragmatic actuarial solutions to make effective decisions on large actuarial data sets.

4. Deep Learning Use Cases in Fraud Detection

In Norway alone in 2019, there were 827 proven fraud cases, which could have caused a loss of over €11 million to insurers.

Insurance fraud usually occurs in the form of claims. A claimant can fake the identity, duplicate claims, overstate repair costs, and submit false medical receipts and bills. Mostly because of disconnected information sources, Insurers fall victim to fraudulent activities from customers. Now, here’s the challenge. How to unify different data sources, which, to date, even include offline receipts and manually scanned documents. 

Deep Learning can help in fraud detection by-

  • Finding hidden/implicit correlations in data.
  • Facial recognition, sentiment analysis on submitted claims application.
  • Supervised learning to train the fraud detection models using labeled historical data.
  • Eliminating the time lag in the verification of documents, which raises the potential for data breaching.

5. Claims

Deep Learning incorporates two-fold benefits to insurers in terms of claims. One — with a connected information ecosystem, it helps insurers with faster claims settlement (thus, customer experience as well). Two, deep learning predictive models can equip insurers with a better understanding of claims cost. 

For example, Tokio Marine — the largest P&C insurance group in Japan uses a cloud-based document processing system to process handwritten claims from the time of the first intimation. Many insurers are looking forward to end-to-end claims processing systems with deep learning and other AI capabilities. 

The Crux

Today, Deep Learning technology is able to mimic an infant’s brain. The research is on for developing new neural network architectures (e.g. Siamese Network, OpenAI’s GPT-2 Model, etc.) that will be capable of performing complex functionalities of a mature human brain. Deep Learning technology, in the near future, will be leading the development of cognition-based insurance systems.

Also read — The Cognitive Cloud Insurer is Next!

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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.

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