In recent years, Asian insurers have increasingly turned to data and analytics tools to drive growth and improve operational efficiency. Many have invested in starting up analytics teams or setting up insurtech partnerships to launch pilots. To date, some of the most popular use cases revolve around increasing new business by suggesting the next product to buy, improving claim-fraud detection, and increasing the straight-through processing rate of new life insurance policies.

In the wake of the COVID-19 pandemic, insurers’ interest in data and analytics has only intensified, and several insurers are looking at fast-tracking the build-out of these capabilities. These insurers must first focus on how data and analytics can help solve some of their immediate challenges amid COVID-19 so they can turn to capturing new opportunities in a world that is more connected and digitized.

During COVID-19: Personalize response to immediate needs. Although the full impact of the crisis continues to unfold in many parts of Asia, the pandemic has already led to a broad economic slowdown. As a result, an increase in late premium payments and policy surrenders has pushed many insurers to rush into quick fixes, such as offering partial refunds to all customers. While such broad actions may boost customer retention, a more targeted approach could use analytics to prioritize such offers for customers most in need. This could be more cost effective and strengthen the insurer’s long-term relationships with those customers. Companies with this capability can also determine the size of the premium relief based on the predicted customer lifetime value.

Insurers that have already deployed data and analytics use cases have observed that some of their analytical models have become less accurate as a result of COVID-19 because changes in underlying customer behaviors have occurred at a much faster pace. For example, segments that were previously deemed safe started to see increasing loss ratios. To address these effects, insurers have focused on increasing their agility to react quickly: if it took months to launch a new analytical model in the past, it might now take less than a week.

After COVID-19: Capture the spiking insurance demand. In China, demand for term life and health insurance showed a significant increase after the SARS outbreak in 2002. COVID-19 will likely create a similar uptake from consumers across Asia. Of course, the world now is much more digitized—and widespread physical-distancing and lockdown measures have pushed Asian insurers to accelerate their digital-transformation efforts. For example, AXA recently launched a digital enrollment system for tax-deductible products in Hong Kong and further digitized its claims and customer-service operations in mainland China.

Data and analytics capabilities can underpin a wide variety of initiatives to provide the personalized and convenient experience that will define insurers’ competitive advantage:

• Microsegment marketing. Create microsegments based on customer and A/B-experiment data and couple segmentation with a data management platform to personalize offers across channels.
• Timely human assistance. Identify customers struggling in real time and provide agent assistance—over chatbots, social media chat tools, outbound calls, or in person—to improve customer engagement and minimize drop-off.
• Agent recommender. Recommend agents to new customer leads based on profiles, product expertise, customer lead- behavior, availability, and so forth to maximize the conversion rate.
• Omnichannel integration. Capture customer preferences and inputs in real time and feed this information to all available channels to create a seamless experience.

Streamlined and automated underwriting. Remove questions with little impact on the underwriting decision and, where possible, automate those decisions to enable instant policy approvals.

From single use cases to industrialization. Typically, data and analytics use cases are developed from the bottom up to address specific points of the customer journey. A more top-down approach requires more than shiny algorithms and a team of data scientists. An industrialized process involves going beyond discrete use cases and scaling technology capabilities, such as data extraction and performance monitoring, to reduce set-up overhead. It also requires business alignment on areas such as leadership, team buy-in, and resource commitment to support rapid development and facilitate an end-to-end strategy for data and analytics.

Governance and management. Mechanisms for impact assessment and prioritization. The finance function should develop an impact-estimation methodology to assess all data and analytics projects on key business metrics. Informed by this assessment tool, a governance structure with participation from both business and technology units can prioritize initiatives, form a delivery road map, and respond to abrupt changes such as those caused by COVID-19.

• Business-led agile delivery. Industrialized use-case delivery is predicated on agile, cross-functional teams and business ownership. Such ownership means there is a sponsor accountable for delivery outcome and for guiding teams in execution, which is critical to ensure business relevance of any new data and analytics enhancement. And agile teams are crucial for rapid iteration between business and technology functions and frequent feature releases (that is, faster time to market).
• Organization-level buy-in and a culture of adoption. The whole organization, from senior management to frontline staff, should receive training about the value of data and analytics to minimize resistance to using them. Internal communication and training should encourage a controlled adopt-and-learn approach and evidence-based evaluation.
• Partnerships and ecosystems. Insurers should double down on alternative acquisition channels and partnerships, such as ecommerce partners, affinity players, utilities, telcos, and banks. Insurers can position themselves as partners that help prevent losses and support customers through challenging times by developing new analytical models that identify the best prospects in terms of risk, value, and customer needs. Through close partnerships, insurers will generate new insights on customer behavior and opportunities with newly expanded and available data sources.
• Technical capabilities. Live model performance monitoring. To identify new trends and opportunities, data engineers, data scientists, and machine-learning engineers need to build performance monitoring into production data and analytics models. Input tracking for an automated underwriting model would include tracking the share of policy applicants by key demographic segments, sources of applications, and so forth. Output tracking would include tracking the rate of automated policy approvals in key segments. Major changes in the input or output pattern may mean the model requires an update.
• Modular and scalable data pipelines. Data engineers should follow standardized guidelines on how to develop data pipelines to ensure regular, low-latency data extraction and reusability in multiple use cases, including built-in flexibility to scale up processing. For instance, an insurer with a regularly refreshed pipeline to extract the raw data for a product recommender would save time when it needs to develop other use cases, like customer purchase intent detection or an omnichannel engine.
• Scalable architecture. The data-architecture team should equip the analytics-delivery team with easily scalable development and production environments to enable accelerated development, testing, and deployment. For example, some insurers are deploying data and analytics directly on next-generation cloud infrastructures. These environments allow greater flexibility to quickly scale up analytics use cases and ramp down computing capacity.

Even for insurers that have successfully deployed data and analytics use cases, developing more comprehensive, top- down capabilities and scaling to the rest of the organization will not be easy. Industrialized delivery takes time to organize up front. However, it can result in seamless, integrated releases and competitive differentiation in the months and years to come.