Revolutionising legacy insurance purchasing with automated data cleansing models in the due diligence process

In the ever-evolving landscape of insurance, the acquisition of legacy insurance portfolios presents both opportunities and challenges. One of the critical aspects of this process is due diligence – involving a thorough examination of the target portfolio’s data. Traditionally, this has been a labour-intensive task, but with the advent of automated data cleansing models, is transforming the way due diligence is conducted.

Understanding legacy insurance purchasing

As we are all aware legacy insurance purchasing involves acquiring existing insurance portfolios, mainly from companies looking to divest non-core assets or exit certain markets. The goal is to manage these portfolios more efficiently, leveraging economies of scale and specialised expertise.

The role of due diligence

The critical step in the acquisition is the due diligence process involving  a comprehensive review of the target portfolio’s data to assess its value, risks, and potential for integration and within that process an evaluation of the policyholder information, claims history, underwriting practices, and financial performance. Needless to say an accurate and complete data set is essential for making informed decisions and negotiating fair terms.

Challenges in data quality

One of the biggest challenges within the whole process is data quality.

Due to many of the legacy portfolios being aged and often previously managed less so than ‘live’ data they are incomplete, inconsistent, or outdated. Notwithstanding the relevance of the various factors involved, such as mergers and acquisitions, changes in IT systems, or simply poor data management practices over the years the poor data quality may well lead to inaccurate valuations, overlooked risks, and ultimately, costly mistakes.

Automated data cleansing models: A game changer

Automated data cleansing models are revolutionising the due diligence process by using advanced algorithms and machine learning techniques to identify and correct errors within the data.

The various stages of operation include:

1. Data integration . . . from various sources into a single, unified dataset involving mapping data fields, resolving discrepancies, and ensuring consistency across the dataset.
2. Data validation . . . either by with pre-defined rules and standards and / or DPR (Data Pattern Recognition) techniques including checking for missing values, duplicate records, inconsistencies and a whole host of other data corruption possibilities.
3. Data enrichment . . . can be achieved by incorporating proprietary data sources e.g. demographic data for missing policyholder information or industry benchmarks to validate claims data.
4. Error correction . . . models such as the LDSL DRP programme automatically correct errors identified during the validation process by imputing missing values, standardising formats, and resolving duplication as well as highlighting other less obvious irregularities within the data set.
5. Continuous improvement . . . Machine learning models continuously learn from new data and feedback, improving their accuracy and efficiency over time.

Benefits of automated data cleansing

The benefits of using automated data cleansing models in the due diligence process are significant:

• Accuracy: Automated models can process large volumes of data with high accuracy, reducing the risk of errors and omissions.
• Efficiency: Automation speeds up the due diligence process, allowing for quicker decision-making and faster integration of acquired portfolios.
• Cost savings: By reducing the need for manual data cleansing, companies can save on labour costs and allocate resources more effectively.
• Risk mitigation: Improved data quality leads to better risk assessment and more accurate valuations, reducing the likelihood of costly surprises post-acquisition.

Conclusion

The integration of automated data cleansing models into the due diligence process is a game changer for legacy insurance purchasing. By ensuring data accuracy and consistency, these models enable more informed decision-making, streamline the acquisition process, and ultimately, drive better outcomes for insurers and reinsurers alike. As technology continues to advance, the role of automation in due diligence will only grow, further transforming the insurance industry.

If you would like to understand more about our automated data cleansing services, contact our team on 020 7971 1141 or email data@legacydatasolutions.co.uk