Exploring the Actuarial Hurdles and Complexities in the Realm of Insurance Industry

The actuarial profession plays a major role in mitigating risks in the insurance industry by leveraging data analytics and financial modeling to stimulate the process of strategic decision making. Despite the crucial role played by actuarial professionals in their work, they face myriad challenges inherent in their processes.

This blog will shed light on the challenges faced by actuary teams in their day-to-day responsibilities.

Roadblocks of actuarial workflow

Actuaries must deal with various challenges while working in the insurance sector. It emanates from stringent regulations, laborious workflow and data complexity.

Let’s deep dive into each of the challenges in detail.

• Extracting data is not a piece of cake – We all know that insurance companies are offering various policies having distinct features, coverage limits and pricing structures. Actuaries working in these companies are faced with the burdensome task of collecting data from spreadsheets, databases, external market reports and various other documents. This cumbersome manual task is laden with errors causing inconsistencies in collecting data.
   
• The challenges in data cleansing and validation – Data cleansing and validation is the next major work after collection of data from various documents. Actuaries are involved in the tiresome work of identifying and rectifying inconsistencies, missing values and outliers, which are crucial for avoiding biased data risk assessments and pricing models. This task is subject to the possibility of numerous errors and are inherently time-consuming.
   
• Complexities in manual data handling – Manual handling of data is always prone to various errors ranging from typos, incorrect entries and miscalculations, and the actuarial workflow is not left out from these recurring errors. This error laden workflow aggravates the problems of misguided risk evaluations, faulty pricing decisions and susceptible financial projections.
   
• Lagging data extraction and reconciliation – As discussed previously, actuaries must extract data from a plethora of documents, and compiling this data takes a huge chunk of time. They contribute their time more on reconciliation and integration than to the vital process of consequential analysis, thereby hampering the overall efficiency significantly.
   
• Challenges from complicated calculations – Actuaries, while developing pricing models, risk assessments and financial projections, must deal with complex calculations to ensure efficiency in their responsibilities. Moreover, these calculations entail coding and scripting that is laborious and erroneous in nature that adversely affects the accuracy of outcomes.
   
• Dragging process of building and validating actuarial models – Building and validating actuarial models is one of the critical processes of actuarial workflow and requires careful attention, intensive testing and continuous refinements. As actuaries execute these tasks manually it delays the timely implementation of updated pricing and risk assessments strategies.
   
• Scalability issues to deal with huge data sets – In this data driven world, conventional modeling and analysis approaches often fail to handle complex and huge data sets. Actuaries often encounter these problems when they apply advanced statistical techniques when dealing with complex and larger datasets.
   
• Unrealiabilty in manual calculations – Actuarial workflows involve manual computations, and these workflows are basically erroneous in nature resulting in inconsistencies. This culminates in faulty risk assessments, misinformed pricing decisions and erroneous financial projections.
   
• Challenges underlying manual preparation and filings – Accurate and timely reporting is the key in the insurance Industry for superior compliance, decision making and transparency. Sometimes actuaries must work to meet deadlines in preparation of reports and compilation of data. This aggravates the potential risk of non-compliance.
   
• Uncertainity in timely submission of reports – Insurance industry operates within a strict regulatory framework that requires timely submission of reports. The actuaries must manually prepare the report that causes delays leading to failure in meeting the deadline requirements.
   
• Non compliance is the drawback of manual report preparation – Manual report preparation is prone to multiple errors that adversely affect regulatory compliance. Non compliance issues emanating from these errors may attract regulatory fines and a bad mark for the insurer.
   
• Lack of valuable insights from manual reporting – By solely focusing on compliance requirements, manual reporting fails in providing actionable insights for strategic planning to the actuaries. The possibility of data driven innovations is ruled out due to this limitation.

Streamlining Actuarial Operations by Harnessing AI, ML and RPA

The Actuarial departments of insurance industry is grappled with multiple challenges and AI, Machine Learning (ML) and Robotic Process Automation (RPA) plays a crucial role in mitigating these issues. AI optimizes the process of data processing, thus drastically reducing the time –intensive nature of conventional workflows. ML helps in scanning huge data sets and analyzes subtle patterns resulting in accurate predictions. RPA automates routine processes, which results in relieving the actuaries from the complex analysis tasks.

The synergy of these technologies identifies potential risks and enhances efficiency that helps actuaries to stay ahead of the curve in the insurance sector.

The way ahead for Actuarial workflow

The traditional actuarial workflow faces numerous challenges and intelligent automation comes as a savior in this regard. Streamlining actuarial workflows with intelligent automation enhances productivity, decision making capabilities resulting in the revamping of actuary profession in insurance industry. Embracing automation in the actuarial workspace in the insurance industry is necessary to stay ahead in the competitive landscape.