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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

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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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