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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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Sales Applications Are Disrupting More Than Just Sales

Sales success today isn’t about luck or lofty goals—it’s about having the right tools in your team’s hands, wherever they go. Following our earlier in-depth exploration of sales technology, we will now examine how cutting-edge sales apps are becoming the backbone of modern industries, transforming complex workflows into seamless, growth-driving machines.

From retail to healthcare, logistics to real estate, businesses are deploying sales applications to enhance operational transparency, cut redundant tasks, and build intelligent sales ecosystems. These tools are not only digitizing workflows—they’re driving growth, improving engagement, and redefining how field teams operate.

Lead Ecosystems: Unified visibility across channels

One app. Five workflows. Zero friction.

A leading insurance brand relaunched their app—a sleek, powerful sales companion that’s turning everyday agents into top performers.

No more paperwork. More time to sell.

Here’s what changed:

  • Every visit is tagged, tracked, and followed through. Renewals? Never missed. Leads? Fully visible.
  • Attendance and reimbursements went on autopilot. No more manual logs. No more chasing approvals.
  • New business and renewals are tracked in real time, with accurate forecasting that sales leaders can finally trust.
  • Dashboards are clean, configurable, and useful—insights that move the business, not just report on it.
  • Seamless Integrations. API connectivity with Darwin Box, IMD Master Data, and SSO authentication for a unified experience.

The result? A field team that moves faster, sells better, and works smarter.

Retail: Taking Orders from the Frontline—Smartly

Field sales agents in retail, especially FMCG, used to rely on gut instinct. Now, with intelligent sales applications:

  • AI recommends what to upsell or cross-sell based on previous order patterns
  • Real-time stock availability and credit status are visible in the app
  • Geo-fencing ensures optimized route planning
  • Built-in payment collection modules streamline transaction closure

Healthcare: Structuring Sales with Compliance and Precision

Healthcare leaders don’t need more reports—they need better visibility from the field.  Whether it’s engaging hospital networks, onboarding clinics, or enabling diagnostics at the last mile, everything needs precision, compliance, and clarity. 

Mantra Labs helped a leading healthcare enterprise design a sales app that integrates knowledge, compliance, performance, and recognition, turning frontline agents into informed, aligned, and empowered brand advocates. 

Here’s what it delivers:

  • Role-based onboarding that keeps every level of the field force aligned and accountable
  • Escalation mechanisms are built into the system, driving transparency across commissions and performance reviews
  • A centralized Knowledge Hub featuring healthcare news, service updates, and training modules to keep reps well-informed
  • Recognition modules that celebrate milestones, boost morale, and reinforce a culture of excellence

Now, the field agents aren’t just connected—they’re aligned, upskilled, and accountable.

Real Estate: From Cold Calls to Smart Conversions

For real estate agents, timing and personalization are everything. Sales applications are evolving to include:

  • Virtual site tour integration for remote buyers
  • Mortgage and EMI calculators to increase buyer confidence
  • WhatsApp-based lead capture and nurture sequences
  • CRM integration for inventory updates and automatic scheduling

Logistics: From Chaos to Control in Field Coordination

Field agents in logistics are switching from clipboards to real-time command centers on mobile. Modern sales applications offer:

  • Live delivery status and route deviation alerts
  • Automated dispute reporting and issue resolution tracking
  • Fleet coordination through integrated GPS modules
  • Customer feedback capture and SLA dashboards

What’s new & what’s next in Sales Applications?

Here’s what’s pushing the next wave of innovation:

  • Voice-to-Text Logging: Agents dictate notes while on the move.
  • AI-Powered Nudges: Apps that suggest next-best actions based on behavior.
  • Omnichannel Communication: In-app chat, WhatsApp, email—unified.
  • Role-Based Dashboards: Different data views for admins, managers, and field reps.

What does this mean for Business Leaders?

Sales Applications are not just tactical tools. They’re platforms for transformation. With the right design, integrations, and analytics, they:

  • Replace guesswork with intelligence
  • Reduce the cost of delay and manual labor
  • Improve agent accountability and transparency
  • Speed up decision-making across hierarchies

The future of field sales lies in intuitive, AI-driven applications that adapt to every industry’s nuances. At Mantra Labs, we work closely with enterprises to custom-build sales applications that align with business objectives and ground-level realities.

Conclusion: 

If your agents still rely on Excel trackers and daily call reports, it’s time to reimagine your sales operations. Let us help you bring your field operations into the future—with tools that are fast, field-tested, and built for scale.

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