Challenges Prevailing in Insurance Industry

Insurance industries go through some of the most difficult challenges, such as Legacy systems, workforce reduction and remote working and inconsistency.

FREMONT, CA: We live in a data-driven, technology-driven age, where data is becoming the key to success in any organization, regardless of industry. When supported by thorough processes, data has the unique ability to replace assumptions with knowledge.

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Obstacles come with opportunities: Insurance companies are collecting huge amounts of data in order to optimize performance, mitigate risks, and meet consumers' rising expectations to remain competitive in this data-rich universe. As insurance companies accumulate an increasing amount of data, they face challenges that prevent them from taking full advantage of analytics strategies and data governance frameworks.

The challenges discussed here include legacy systems and broken data, data management and analytics at the product level, and a lack of coherent data.

Data from multiple sources: Insurers have created different channels of communication over the years to manage conversations internally and externally: between employees, customers, business partners, and policyholders. Insurance companies have established call centers, forms both offline and online, and, more recently, self-serve portals, mobile apps, live chat systems, and chatbots to meet the expectations of their customers. Alternate choices are provided for customers, but there is also a range of difficult-to-reconcile data sources.

Additionally, insurance companies (like most established financial firms) have large data repositories and analytics teams. In such companies, departments lack information sharing and communication with one another. A lack of business intelligence has also been acknowledged by insurance professionals when it comes to "standard business practices" in which each business unit has its own way of retrieving data.

The definition of key terms may not be consistent, causing bottlenecks in the integration process. In order to build analytical solutions, a hybrid approach is taken, which naturally results in inefficient organizational processes, data silos, and a lack of communication, preventing insurance companies from taking advantage of the full potential of data and analytics. Due to inconsistencies and confusion across lengths and breadths of organizations, inefficiency and growth are stifled.

An inconsistency: Actuaries and data scientists have developed advanced systems for processing data and building models. These models generate numbers and calculations. This is only successful if the data entry process is accurate from the start.

Data is manually entered by different data operators in conventional systems. Many claims are manually entered into policy systems, often without detail, such as missing descriptions or inaccurate categorizations based on a common classification. The volume of big data generated by IoT and other technologies requires enterprise data management strategies. Combining new and old data, such as customer and policy records, becomes an important aspect of data management. 

Data that is lagging: As unstructured data is accommodated in multiple systems in the insurtech industry today, much of it gets scattered.

Every insurance product has its own process for gathering, managing, and utilizing customer data. Since data comes from different systems, it needs to be aggregated, cleaned, formatted, synthesized, shared, explained, discussed, etc. In the absence of an effective process for bringing this data to the relevant people who understand it, the information becomes outdated as it is very difficult to share. Such an arrangement is also plagued by redundant IT infrastructure.

Direct channels and agencies may compete for the same customer. The period between data retrieval and reconciliation may be long, and by the time analysis occurs, weeks may have passed. Due to unwarranted data lags and unwarranted expenditures, decision-making processes suffer.