2023 Best Practices: Mastering Buy-ins and Buy-outs for Pension Schemes

In this article, we discuss the significance of having a solid foundation of trustworthy data for pension schemes and the crucial role that data accuracy plays in the de-risking process.

Any effective pension buy-in or buy-out transaction depends on accurate data.

Accurate Data

Accurate and trustworthy data is crucial for pension schemes considering buy-in or buy-out transactions. It is the foundation for determining obligations, gauging the scheme's financial status, and choosing the correct financing levels. Reliable data helps pension plans assess the financial effects of shifting pension obligations, identify possible hazards, and make educated judgements on de-risking techniques. Accurate data is also necessary for insurers to estimate the liabilities they will take on and establish the premiums and pricing for buy-ins and buy-outs.

It's critical that the information known to the pension scheme and the insurer is accurate, transparent, and up to date so that premiums can be fairly calculated.

The consequences of holding incomplete and poor-quality data

Poor-quality and incomplete data are viewed unfavourably in all pension scheme management aspects, which is no different for insurers. Inadequate data equals higher transaction costs, delays and uncertainty.

15 Hidden Dangers of Incomplete and Poor-Quality Data

Holding incomplete and poor-quality data can have various consequences, spanning from operational inefficiencies to severe financial and reputational damage. Here are some of the potential consequences:

Inaccurate Decision-Making: Decision-makers rely on data to make informed choices. Poor quality data can lead to misguided decisions, which could seriously affect an organisation.
Operational Inefficiencies: Incomplete data can cause processes to be delayed or halted, requiring manual checks or interventions. This can result in increased operational costs and resource wastage.
Decreased Customer Trust: If customers experience mistakes resulting from poor data quality (e.g., incorrect billings, miscommunication), their trust in the company can quickly erode.
Reputation Damage: Mistakes or oversights stemming from poor data can result in public relations challenges and tarnish a company's reputation.
Financial Losses: Inaccurate data can lead to wrong financial predictions, misallocation of resources, fines, and lost revenue.
Compliance Issues: For sectors where data accuracy and completeness are legally mandated, holding incomplete or poor-quality data can result in legal penalties and fines.
Increased Costs: Correcting poor-quality data post-facto can be expensive in terms of both money and time.
Missed Opportunities: Incomplete data might mean missing out on patterns or insights that could lead to new business opportunities or improvements.
Low Employee Morale: Continuously dealing with data-related issues can frustrate employees, leading to lower job satisfaction and increased turnover.
Reduced Data Usability: Inconsistent or poor-quality data can make it difficult to effectively utilise modern data analytics tools, limiting potential insights.
Poor Forecasting: Reliable data is critical for predicting future trends, whether in sales, inventory, finance, or other areas. Only complete data can lead to good forecasts.
Security Risks: In some cases, poor-quality data can also increase vulnerability to cyber-attacks if it interferes with the efficacy of security measures.
Loss of Competitive Advantage: In the age of data-driven decision-making, companies with high-quality data have a significant advantage over those with poor data.
Integration Challenges: Incomplete or inconsistent data can make integrating new systems or software difficult, leading to technological stagnation.
Stakeholder Mistrust: Not just customers but also investors, partners, and other stakeholders might lose trust in a company that consistently needs to demonstrate better data management practices.

In the information age, data quality and completeness have never been more crucial. Ensuring data integrity, accuracy, and completeness is critical for businesses that wish to succeed in the current and future landscape.

Getting Ready for a Successful Pension Plan Buy-in and Buy-out

Discover our Pension Buy-In and Buy-Out Solutions

Does my scheme require accurate data?

Yes, it does. Data accuracy and meticulous calculations hold utmost importance for both pension schemes and insurers when considering buy-ins and buy-outs. Accurate data is the foundation upon which all pension calculations and valuations are based.

Why is my data so vital to the insurer?

Insurers require accurate data to assess the liabilities properly. They will need to make assumptions based on your data that are used to calculate the premiums and pricing accordingly.

What about Guaranteed Minimum Pensions (GMP’s)?

If the scheme has not already done so, then GMP’s will need to be equalised as part of the buy-in or buy-out processes.

"We specialise in tracing pension members who have moved or passed away overseas so that insurers can accurately calculate their liabilities. Undercounting members who have died can result in significant financial risks for insurers."

Danielle Higgins, Managing Director of the Tracing Group

So, how can the Tracing Group help improve my data for transaction purposes?

Here are 7 solutions to how the Tracing Group can help you avoid poor quality data:

Find Missing Members - Over time, data decays, and pension schemes may lose track of members who have changed addresses and names, or have passed away. TTG use various methods, including automated, forensic and genealogical, to find these missing members. Identifying all scheme members is pivotal to accurately valuing the liabilities during buy-ins or buy-outs.
Update Contact Details - TTG can update and append accurate contact information for scheme members, which is essential for communication during a buy-in or buy-out process.
Confirm Beneficiary Details - TTG can identify and verify beneficiaries, ensuring that benefits are distributed correctly.
Improve Data Accuracy - TTG employs techniques to correct inaccuracies and inconsistencies (both current and historical) in the scheme’s data. Clean and correct data is the key to assessing the scheme's financial position, calculating liabilities, and determining appropriate funding levels.
Confirm Life Alterations - TTG can confirm life changing events like marriages, deaths and divorces, ensuring accurate and live information for scheme members.
Efficiency - TTG can provide accurate and organised data which will ensure simplification when transferring information from the scheme to the insurer.
Adherence to Regulations - TTG can assist in ensuring that the pension scheme data management is compliant with the relevant regulations.

Real Life Examples

We have extensive experience in preparing schemes for buy-ins and buy-outs. We are specialists in:

Checking and updating address information for all members, including those that haven't been in touch with the scheme for a number of years, so that the scheme can communicate with them about the buy-out or buy-in.
Identifying all deceased members. Actuarial calculations performed during the scheme’s lifetime will usually be cautious when predicting the volume of members that have died and the scheme is not aware of within the deferred member base. When a scheme performs a root and branch review of its data followed by thorough remediation, we will find deaths that no others will find. This includes deaths where the original data is nothing like the information on the death registration information. We also specialise in identifying deaths that happen overseas – these are missed in automated mortality screening.
This thorough, forensic approach delivers liability savings of £460.55 per member.
The thorough remediation involves engagement with a large proportion of the member base. We capture email addresses and mobile telephone numbers each time there is engagement to maximise future digital engagement.

Book a free consultation with one of our experts in data quality.

"The Tracing Group have helped considerably with an extensive Data Overhaul, which has considerably improved the scheme data of the pension scheme over the last three years. Danielle and her team are friendly and approachable and have always provided a timely and concise service to the scheme with a detailed de-brief upon project completion. I would recommend The Tracing Group for any scheme data improvements that may be required.”

Mark, Bank Pensions Manager

The Tracing Group is proud to partner with some of the biggest names in the pension and financial industries.

You can read more about us at www.thetracinggroup.co.uk.

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