Mitigate risk in pension schemes with data-driven insights
Empowering pension schemes with
de-risking solutions
For trustees and scheme sponsors, pension de-risking is not just a strategy — it’s a necessity to secure long-term financial health.
At LexisNexis® Risk Solutions, we help pension schemes tackle key risks with powerful data insights, from buy-ins and buy-outs to marital status de-risking. With the right data, you can ensure more accurate actuarial assessments, reduce adverse insurer margins, and manage scheme costs effectively.
De-Risking for buy-ins and buy-outs
In buy-in and buy-out transactions, a detailed understanding of your pension scheme's demographic and financial profile is essential. Before a scheme is transferred or bought, insurers and trustees need to assess the financial viability and risks involved.
Our data-driven solutions offer an in-depth analysis of pension scheme members, helping insurers evaluate the quality of a scheme’s data and identify potential risks. Whether it's assessing life expectancy, age distribution, or reviewing the demographic, our tools provide clarity, allowing you to make informed decisions about your pension book.
By evaluating not just the value of pensions but also socio-economic variances, we provide the detailed insights necessary to manage and mitigate risks — ensuring a smoother and more profitable transaction.
Marital status prediction
Understanding the marital status of your pension scheme members is vital in determining future liabilities. A spouse or partner is often entitled to pension benefits after the member's death, which can significantly impact scheme costs.
Our Marital Status Prediction (MSP) solution helps pension schemes identify whether members are married, living with a partner, or single — providing crucial information for assessing the longevity risk and financial implications for the scheme. By predicting marital status, we help pension funds better estimate future liabilities and protect against unforeseen costs.
How it works
Using advanced algorithms and a comprehensive datasets, our solution classifies each member based on their likely marital status, including:
Married
Living as Married
Living as Family
Same Sex Cohabitee
Multi Household
Living Alone
Widowed
