Historic Bordereaux Data Challenges: Top Tips to Make the Most of Your Data
Historic bordereaux often reside in disconnected spreadsheets and legacy systems. Inconsistent templates, siloed repositories and missing context can turn valuable premium and claims data into a compliance headache rather than a strategic asset. By addressing these challenges head-on, you’ll unlock insights that drive smarter underwriting, improved reconciliation and more accurate portfolio analysis.
Top Tips to best manage Bordereaux Data
Standardise and cleanse at the source
Align incoming bordereaux to a unified template and validate critical fields—policy numbers, coverage codes, premium figures—before ingestion.
Define clear, robust processing data standards
Bordereaux processing technology data warnings & errors should be clearly defined against key fields against a defined data standard.
Process implementation and management is key
Defining clear data standards will only work if implemented properly over time. The warnings and errors triggered need to be at a manageable level, striking a balance between processing meaningful data without grinding processing to a halt trying to process perfect data.
Ensure key business stakeholders are bought into the process.
Underwriter/Subject Matter Experts buy in is key, ultimately the data is there to serve the business’ needs, decisions on what good data looks like must be agreed with key stakeholders within a wide array of business areas.
Prioritise core data fields, consider validations against key binders.It is crucial to understand what reports each data item is utilised for, if any, do you need to spend time validating a field or hold up processing of a bordereaux for a field with little to no use? If an important field is constantly triggering these validations is it across all coverholders/DCA’s or one or two repeat offenders where data can be improved at source?
Whilst these top tips address how to improve your current processes to support data quality, this often means that firms are left with historic data that is deficient and unusable. Good news is this can be addressed through exception-based analysis and data fixes which can address some of the main issues with data to ensure improved accuracy and reliability of your datasets.
Analyse field by field and identify trends with poor data
Identify trends with poor data quality, such as missing values, inconsistencies, and inaccuracies. By pinpointing these problematic areas, you can develop targeted strategies to rectify them. For instance, you might need to standardise formats, correct erroneous entries, or fill in missing information using reliable sources. This meticulous approach helps in building a solid foundation for your data, making it more trustworthy and useful for future analyses. However, work on an exception basis using the 80/20 rule, focus on key fields and/or contracts to get the most significant gains.
Determine the validation logic that would support accurate and complete data
Once the problematic areas are identified, the next step is to determine the validation logic that would support accurate and complete data. This involves setting up rules and checks that ensure data integrity and would prevent the data from being ingested into the system. For example, you can implement validation rules that prevent the entry of incorrect data types, enforce mandatory fields, and cross-verify data against known standards or reference datasets. These validation mechanisms act as a safeguard, preventing the introduction of new errors and maintaining the quality of your data over time.
Work with your data team
Collaborating with your data team is crucial, as their expertise can help in designing robust validation processes and ensuring that the data meets the required standards. They are also central to fixing key datasets within your environment and implementing best practices for cleansing and maintaining data standards in BAU.
By addressing historic data issues and implementing thorough cleansing processes, you can enhance the overall quality of your data, making it a reliable asset for your organisation.
Transform Your Data with Greenkite!
Are you struggling with historic bordereaux data challenges? Turn your compliance headaches into strategic assets with Greenkite. Our expert team can help you standardise and cleanse your data, ensuring accuracy and reliability for smarter underwriting, improved reconciliation, and more accurate portfolio analysis.
Don’t let poor data quality hold you back. Get in touch with Greenkite today and unlock the full potential of your data!
