How to address new FCA pricing practices

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The Three Main Data Challenges in the Insurance Industry

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We live in an age that is data- and technology-driven, in which data is turning into the key driver of success in any organization, no matter the industry. Data has the distinctive ability to substitute assumptions with knowledge, provided it is supported by thorough processes. To quote Alexis Carrel, “A few observations and much reasoning lead to error; many observations and a little reasoning to truth.”

Our work with clients has revealed certain recurring challenges in the insurance industry that we’ll explore in-depth today.

With opportunities comes obstacles

In this thriving data-rich universe, insurance companies have to collect huge amounts of data with the goal of performance optimization, risk mitigation and meeting the rising expectations of consumers, and hence, data is key to remaining competitive. For ages, insurance companies have been aggregating a great deal of data, but face challenges, preventing them from making the most of the power offered by analytics strategy and data governance frameworks.

Legacy systems and broken data, data management and analytics at the product level, and a deficiency of coherent data are just some of the challenges explored here.

Multiple Sources of Data

Over the years, insurance companies have created different channels of communication with a view to managing conversations internally and externally: between employees, customers, business partners, and policyholders. For instance, in order to meet the expectations of customers, insurance organizations have established call centers, forms (offline and online), and more recently, self-serve portals, mobile apps, live chat systems, and chatbots. While customers are provided with alternate choices to suit their needs, a range of difficult-to-reconcile data sources is also created.

Besides this, insurance companies (like most established financial firms) have huge repositories of data and various teams running analytics functions. Information sharing and communication with one another is something that such companies’ departments sorely lack. Moreover, insurance professionals admit to an absence of business intelligence when it comes to “standard business practices” where each business unit has its own ways for data retrieval. What’s the result of this?

Key terms may not be defined consistently, forming bottlenecks for smooth integration. A more or less hybrid approach to building analytical solutions is undertaken, and this (naturally) causes inefficient organizational processes while creating data silos and hindering healthy communication, preventing insurance companies from gaining from the full gamut of potential that data and analytics brings. Inconsistencies and confusion across lengths and breadths of organizations ensue, causing inefficiency and curbing growth.

Inconsistency

Advanced systems to process data and build models have been evolved by actuaries and data scientists. Models such as these generate numbers and calculations. Nonetheless, this is only successful if the data entry process is accurate to start with.

In conventional systems, data is input manually by different data operators. For instance, several claims are fed manually into policy systems, often with a lack of attention to detail, such as missing descriptions and inaccurate categorization related to a common classification. Big data volumes generated by the applications of IoT and other technology require strategies based on enterprise data management. While combining new and old data, such as customer and policy records, becomes an important aspect of managing data. 

Lagging Data

In the insurtech industry today, a great deal of unstructured data gets scattered as it is accommodated in various systems.

Every product of an insurance company has its individual process for how to garner, manage and utilize data about customers. As data comes from different systems, it needs to be aggregated, cleaned, formatted, synthesised, shared, explained, discussed, and so on. Without an efficient process to bring this data to the relevant people in the way they understand, the information gets outdated as it is very difficult to share. A redundant IT infrastructure is an additional bane of such a setup.

The challenges that are posed here take the form of an inability to recognize the same customer across products, as well as/or at various stages of a policy life-cycle. Agency and direct channels may end up in competition for the same customer. Since departments seldom work in a cohesive manner, the period between data retrieval and reconciliation may be long, and by the time analysis does take place, weeks may elapse. Decision-making processes suffer, given the unwarranted data lag, not to mention unwarranted expenditure and a bulkier than required marketing budget. Consequently, erroneous data is stored in a range of formats and systems. All this undermines the quality of data and gives rise to miscommunication and flawed information.

Conclusion

Fragmented data with a legacy system that is inaccessible prevents insurance companies from drawing out the value and making data work through actions.

On its own, data possesses no value whatsoever, unless it is managed and processed vigorously. The insurance industry is rife with a plethora of challenges that adversely affect its seamless management, but most professionals recognize that big data will only get bigger, and a common platform to house and integrate existing and new data and tools for analysis is vital for success. For data to become a source of knowledge and support decision-making across all units of business, from underwriting to marketing, and pricing to policy servicing, it has to become viable so that it can be utilized effectively. 

Process automation is the use of digital technology to perform processes with the result of accomplishing a workflow/function and used in insurance firms, this could eliminate inconsistencies in data and streamline knowledge generation from data. Learn how Conversational Process Automation can aid in this and meet data challenges head-on.

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Discover Conversational Process Automation

CPA allows end-to-end processing through chatbots and leverages advanced analytics to continuously improve both the underlying process and the conversation with the end-user.

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