Genivity’s HALO is an important risk model for financial advisors and their clients. Our HALO risk model offers personalized reporting on health and wealth factors that can affect retirement planning. These factors are used to predict one’s time spent in retirement and the expected cost of maintaining their health in their older age.
To give you an in-depth understanding of our risk models, we asked our Chief Data Scientist and co-founder, Emily Chang, to explain the science. Here is what she had to say on the topic.

Our Chief Data Scientist and Co-Founder

Emily Chang’s focus revolves around “data and science aspects of the company, such as creating and updating algorithms for predicting longevity and years of disability, evaluating scientific literature and data sources, and communicating the scientific and technical aspects of Genivity to others.” She loves to see the world through numbers, which as she states, makes the world “richer, deeper, and more captivating.” What draws her to this field are the challenges like lifespan algorithms and the potential benefit her work has toward individuals. “Giving people numbers and guidance as they face the most important questions surrounding retirement is rewarding, knowing that people who use our tools will be better prepared for their futures” states Emily.

The Process of Algorithms and Genivity’s Data Practice

Emily explains that the creation of algorithms depends on the “needs and wants of customers, and responding to changes and developments in the research world.” As she puts it, “customers may request more detailed and personalized estimates for health care premium payments. Such an update would involve a review of the literature surrounding different healthcare premium plans, creating a model for capturing the various plan options, and writing specifications for the developers on how that model can be enfolded into the preexisting algorithms.”  
However, Genivity does not only focus on creating new algorithms. We also update our existing risk models. As Emily says, “When a new and important paper relating to any of the many parameters in our algorithms is published, we evaluate the study. If the study is indeed sound, we update the parameters and model as needed.”
That leads us to ask: How do you analyze data and determine its value? Emily states that there are a variety of ways to analyze data. One type of process is evaluating the data herself. “In choosing a study to use, I look for large sample sizes, good study design, and the tier of the journal of publication (or quality of government or other data source).” Another process Emily uses is to evaluate the quality of the risk model projections. This involves “comparing the distribution of our estimates and intermediate results to published findings, as well as graphing the generated data and doing simulations.”

Risk Model Limitations

Risk models have limitations. Science and data can only predict to a certain extent. As Emily puts it, “The future lifespan of a healthy athlete may be short (think of the proverbial bus), whereas an obese smoker may live to age 100 for reasons we don’t understand.” However, that does not mean that all hope should be lost. Every individual varies from general information, but with personalized input, a better estimate can be created. Emily puts it better: “What we can do is provide estimates, averages, and likelihoods that are as personalized as possible. These probabilities and likelihoods are a starting place for financial planning, and are more customized and powerful than the estimates provided by standard actuarial tables.”


Emily’s work with Genivity has created new tools to improve and help plan the lives of many individuals during their time in retirement. The time and effort placed into our risk models under the direction of Emily will have a great impact on the lives of your clients.
“I hope that the work I am doing helps people, with the help of their financial advisors, approach this question with more confidence and certainty.  With early and careful financial planning, my hope is that individuals will have less stress and uncertainty around what their old age will bring.”