Actuarial business functions like asset liability management (ALM), pricing, valuation, experience studies, capital adequacy assessment, re-insurance, catastrophe risk modelling, financial reporting, and PRT / LRT require consistent, accurate, and reliable data. Given the impact of COVID-19 on actuarial businesses and risks such as climate change, insurers are being forced to model the impacts without adequate historical insights. Additionally, insurers are required to be compliant with regulatory changes (such as IFRS17, LDTI, and SII) while embarking on transformation programs (like policy administration systems, tool conversion, and so on) which involve handling data from multiple legacy systems. Manual off-system adjustments, reconciliation, inconsistent reporting data, as well as missing data add to the problem.
TCS’ Actuarial Data Framework allows reconciliations and divergences of model points using automation. It helps organizations to consider new data requirements, compare with existing model points, and discover incremental additions to data attributes.
The framework provides insights on the data changes required to handle new risks and helps envisage a common process for model point files and single source of data for all business functions, starting from pricing and reserving to valuation and reporting.
By using the framework, insurers can automatically analyze a range of data attributes and values to compare all historic data against current actuarial data. Built on open source stack, the framework is compatible with various data structures used across various actuarial tools and allows for comprehensive actuarial data mapping.
While serving as an actuarial data assessment model, the framework can help insurers:
• Quickly identify incremental changes in model points
• Analyze gaps with respect to new actuarial data requirements
• Envisage solutions for consistent and common process for model points
TCS’ framework can also be used for model outcome testing, reconciliations during model conversions and PAS conversions.