The world is currently grappling with COVID-19, with governments implementing lockdowns to ensure social distancing to confine the spread of infection. There is little indication regarding when this disruptive situation will subside.

Insurance firms are doing all they can to ensure business continuity while ensuring employees remain healthy. As the circumstances resulting from COVID-19 evolve, it is crucial for insurers to keep clients abreast of relevant developments and how their services are responding. With more staff working from home, insurers who have invested in digital capabilities will be better positioned to navigate the crisis short-term. Even so, insurers will be put to the test as they face huge surges in claims, dwindling investment returns, and reduced interest rates.

Covid-19 – a disrupting force of transformation for the insurance industry.

Advanced analytics offers relief

Data and analytics have been touted as key transformation agents in the insurance industry. The industry has refined the collection of huge volumes of structured and unstructured data from customer interactions. Actuarial tables, risk tolerance models, policy provisions, and pricing structures all rely on quality data that can be understood and applied. There are still significant challenges in creating actionable intelligence from this data.

Lack of digitization in processes is hurting insurers as they transition to WFH mode.

The sudden outbreak of COVID-19 and massive lockdowns associated with the virus have exacerbated these obstacles. The traditional insurance business model relies heavily on manual processes and call centers. Some insurers are restricted by legacy technology frameworks that require heavy investments in on premise infrastructure and upkeep. Some lack the employee talent with relevant skillsets. These barriers must be overcome if the industry hopes to enable a COVID-resilient business model.

How analytics supports business resilience

The outbreak of COVID-19 has underscored the importance of agility and resilience for insurers. A real-time, single source of truth approach across the organization is crucial for the whole business to effectively navigate through the crisis while catering customer needs and adhering to regulatory protocols. An organizationlevel integration enables all functions, including marketing, pricing, underwriting, and claims, to flexibly adapt to changing business scenarios. Machine learning and AI can help insurers to tap business opportunities to deliver superior customer experiences at low costs in areas such as reevaluating underwriting profiles, novel product designs, and intelligent claims management as the pandemic situation evolves. In the face of a changing economic outlook and more stringent regulations, insurers will need to veer from a batch processing mode to a more real-time, data-driven and proactive approach.

Four steps to a future-ready state

Data analytics and AI can help insurers navigate through unforeseen crises through the application of a resilient business model built for the future.

This should be done by using an approach incorporating four steps to a future-ready state.


The immediate focus of insurers is ensuring business continuity and keeping abreast of changes in business environment. A combination of in-house and external data sources enable insurers to decide how their business strategy can be adjusted as the economy opens up in phases.

Challenge:

Insurers need to monitor KPIs regarding operations and customer experience to ensure business continuity.

Analytics use cases


Challenge:

Virus infection led to surge in claims and disruption in business operations.

Analytics use cases


As the economy recovers from the pandemic, insurers will need to focus on digitized journeys for customers to reduce the dependence to call centers going forward. A significant portion of servicing needs can be fulfilled online using web or mobile app interfaces. Another shift for insurers will be to build more real-time pricing models to respond to evolving risk patterns due to job-losses or volatile demand scenario.

Challenge:

Working from home is going to be more commonplace post-COVID-19. Insurers need to focus on developing capabilities to service clients with limited call center presence.

Analytics use cases


Challenge:

  • Pricing models need to reflect unforeseen risks during a crisis.
  • Pricing requires a more agile approach, allowing analytics teams to focus on feature engineering, with automation to handle tasks such as data preparation and algorithm trials.

Analytics use cases


Challenge:

Manual document processing in sales or claims increases processing time and leads to multiple follow-ups. This can be challenging when client is facing a life-threatening situation like a COVID-19 infection or hurting from severe financial stress

Analytics use cases


COVID-19 has shifted a significant proportion of customers online to meet their needs for products or services. Insurers in post-pandemic world will need to develop intelligent processes that anticipate customer needs and help attain them. Insurers can collect customer data across journeys and leverage machine learning (ML) and AI to predict customer intent, whether connecting using mobile app portal or when they dial the call center. Along with a superior customer experience, this will also help to improve cost efficiency.

Challenge:

Post COVID-19 markets are likely to face pricing pressure due to economic slowdown. Enabling digital solutions in business-critical functions creates cost efficiency.

Analytics use cases


Challenge:

Increased levels of customer queries or claims are requiring insurers to ensure optimal usage of human resources to deliver exceptional CX.

Analytics use cases


The final leg of achieving a pandemic-proof insurance model is developing an almost completely digitized business model. This entails the full-blown adoption and implementation of analytics and AI across customer journeys. Insurers can personalize risk scoring and client servicing for individual customers. Increasing openness toward sharing personal data through telematics, wearables and other devices is another positive trend toward personalization for insurers.

Challenge:

Social distancing during COVID-19 will push more customers toward permanent state of digital selfservicing. Insurers should proactively assess customer needs across all digital touchpoints and deliver an effortless customer experience

Analytics use cases


Challenge:

Financial stress and increased digital interactions will open more opportunities for fraud in a post- COVID-19 scenario.

Analytics use cases


Challenge:

Wearable data will play a key role in detecting early signs for negative events and initiating proactive communication with clients to limit the severity the of hazard. This can also help insurers reward healthy lifestyles with cheaper premiums.

Analytics use cases


Conclusion:

Covid-19 has helped insurers identify key gaps in their operating models and built a strong case for making data architecture and analytics pivotal in the decision making process. For most insurers, analytics is currently limited to linear pricing models and business reporting. Insurers can reap much more from the volumes of customer data they collect every day. Data can help insurers to personalize risk to the most granular extent so that they can let the right risks come through at the most informed price. Real-time scoring can help insurers to effectively respond to competitor strategies and grow market share. Customer portals can be personalized based on customer profile and recent customer activities. A digitized claim solution can help a customer get a claim settlement in a short time rather than going through several communications.

As the economies across the world get more connected, the likelihood of large-risk events only seem to be increasing. Leveraging data analytics will be crucial for insurers to incorporate agility into decision making in response to this changed environment. Creating insights from customer data and external trends can also guide insurers to anticipate the evolving patterns of risk coverage and proactively develop strategies in response.

 

Written by

Shubham Jain,
Sr Engagement Manager,
EXL Analytics

Tamal Chandra,
Project Manager, EXL Analytics

Contributors

Siddharth Bhatia,
Director, EXL Analytics

Swarnava Ghosh,
Sr Engagement Manager,
EXL Analytics

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