A lot has been made about the recent success with retirement plans, which includes:

 

  • Higher participation due to autoenrollment features
  • Increasing contribution rates due to autoescalation features
  • Improved investment allocations and improved returns due to better default investment choices, in particular through the use of target-date funds (TDFs).

Of these three developments, the use of TDFs has notably been the most visible. Since 2006, TDFs have become one of the biggest recipients of defined contribution (DC) plan assets. Plan sponsors readily recognize their value as a qualified default investment alternative (QDIA) with built-in automation and derisking features. From a plan design perspective, TDFs offer a one-size, simple glide path option that generally appeals to most participants (at least initially).

As participant wants and needs become more complex, TDFs may not provide the ideal solution. Indeed, questions are being raised about whether TDFs could be disrupted by emerging solutions that apply simple data from recordkeepers (let alone big data sources) and potentially incorporate other inputs to create more personalized retirement investment advice. (note that there may be some issues with trust to overcome).

 

How real is the disruptive threat from personalization?

 

This article examines the question from a number of angles: the limitations of TDFs, the growing consumer demand for personalized products and service that “know them,” and foundational notions of trust and intrusiveness.

 

Download the full article to read more.

 



 

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