Our mission is to help make sure that no one runs out of money in
retirement due to longevity risks and healthcare costs.
The HALO Planner (Health Analysis and Longevity Optimizer)
provides financial advisors with an elegant client-facing solution that
personalizes longevity and healthspan projections with the associated
health and eldercare costs.
Our software is designed to complement the relationship between
financial professionals and their clients, so they can provide a well-
planned and secure financial future.
The Science Behind Lumiant:
The HALO Approach
No one can see perfectly into the future, however, Lumiant uses innovative
methodology and powerful analytics based on millions of data points to
make personalized longevity, health, and eldercare cost projections possible
in the financial industry. This document provides an overview of our
methodology and approach. HALO offers a unique process for educating clients and prospects and
identifying planning opportunities by pinpointing where they have “gaps” in
their protection, resulting in moving clients towards informed decisions
much faster. This enables clients and advisors to make unbiased decisions
regarding risk mitigation strategies and provides personalized client
recommendations on how to live well longer.
Traditional approaches for planning for retirement ignore or marginalize the
effect of health on future finances.
However, studies show that health is a major factor in determining one’s economic wellbeing, and high health care costs are associated with bankruptcy1 and significant depletion of
retirement assets. Health determines many factors, such as out-of-pocket
care costs (e.g. hospital bill co-pays), length of life, and time spent in elder
care (e.g., nursing homes) that can affect retirement planning. Thus, any
financial advisor wishing to give their clients a full picture of their financial
future must take health into consideration
The Missing Opportunity
Unfortunately, there are very few data-driven resources available to
edu cate financial advisors and their clients on the relationship between
health and wealth. Furthermore, most tools available are difficult to use and
understand, and lack scientific grounding. The HALO Planner,
however, uses a heuristic decision-making model to create a solution that is
easy-to-use and based on years of scientific medical research
Risk Factors Considered
The HALO predictive model focuses on the most important
scientifically backed factors that affect longevity and years of
disability. Unlike other risk models that ask the user to complete long
and tedious questionnaires, which often include risk factors shown to
have a relatively small impact on mortality and morbidity, the Lumiant
approach covers all of the most statistically important risk factors
(some of which, ironically, are excluded by some of the longer
The model puts a strong emphasis on family health history, including
factors such as: Alzheimer’s/dementia, stroke, diabetes, heart disease,
obesity, cancer (bladder cancer, colon cancer, breast cancer, kidney
cancer, lung cancer, ovarian cancer, pancreatic cancer, skin cancer and
prostate cancer), and the overall longevity of parents and grandparents.
In addition, the most important lifestyle factors (smoking, exercise, diet,
alcohol consumption, BMI, and social support), as well as demographic
factors like age, gender, personal health history and ethnicity, are also
considered. Genomic data, if available, may eventually become factors
within the model.
Lumiant Data Sources
HALO’s projections are powered by over 100 million
scientifically relevant data points from more than 90 carefully vetted
and curated data sources including validated data from large studies
by the Center for Disease Control and Prevention (CDC), the SEER
Cancer database, the Kaiser Family Foundation, and Social Security
In addition, the Lumiant team has evaluated hundreds of studies in
high-quality, peer-reviewed academic journals, such as The Journal of
the American Medical Association and The New England Journal of
Medicine, to find the best parameters for inclusion in the HALO models.
As new clinical studies are published, the team reviews the data and
where appropriate, updates the models accordingly.
Lumiant Genetic Age
Lumiant’s concept of Genetic Age lays the foundation for our unique
analytic approach. Genetic Age is the age at which a person’s risk of
disease, based on their family health history, is comparable to an
average person of the same gender in the general population.
For example, if a 35-year-old woman has a health history of breast
cancer, her risk of breast cancer may be more comparable to a typical
45-year-old woman. The model would suggest that this woman with an
elevated risk of breast cancer has a Genetic Age of 45 years with
respect to breast cancer.
An overall Genetic Age is calculated for each person individually by
taking a weighted average of the disease-specific Genetic Age for each
of the fifteen most common disease conditions. HALO focuses
on the most critical and common health conditions in the model, like
common forms of cancer and diabetes (it is estimated that heart
disease, cancer, and diabetes account for 7 of every 10 deaths in the
Most current metrics of risk (e.g., relative risk, odd
s ratios, etc.,) are difficult to understand by the average person, but the Genetic Age
metric translates risk into familiar terms of age. When explained using
the well-understood concept of age, the user can intuitively grasp how
their personal family health history is affecting their own risk of disease,
and, ultimately, their longevity.
Limitations of Genetic Tests and Actuarial Tables
Genetic tests typically cannot be used alone to accurately predict
longevity. Genetic tests are extremely useful in certain families with a
very strong family health history of disease, especially Mendelian
conditions, to understand potential inherited risks. However, for most
individuals with families that include multiple common conditions like
diabetes, stroke, and heart disease, family health history and lifestyle
are better predictors of risk, as compared to genetic tests alone.
Actuarial tables are based on data from broad populations of people
and can be useful in some situations, such as determining the average
life expectancy for people of certain age and gender.
However, actuarial tables are not personalized to each individual and their
particular life journey or circumstance. The tables fail to consider
detailed family health history information and many important lifestyle
factors such as smoking and exercise.
To understand the life expectancy of a specific individual, such as a 35-
year old man with a family health history of diabetes, BMI of 24, and a
smoking habit, an actuarial table based on a broad swath of individuals
will not be sufficient to provide the accuracy and fidelity of a unique,
In a complex environment where there is a lot of information and
uncertainty about the future, a heuristic model for decision making is
superior to regression or probabilistic models because of the
demonstrated less-is-more effect. The reliance on rules based on
previous knowledge (a heuristic) to guide the result is more accurate
than a complex series of statistical tests incorporating all the data
points to cover all the bases across all possible outcomes.
People’s health, wealth, and family are inseparable and complex.
Employing a heuristic model to the relationship between family health
history, personal lifestyle and financial planning is the most effective
tool for empowering people to make good decisions about their health,
wealth, and financial goals.
The algorithm and science behind Lumiant was developed by Emily Chang,
Ph.D. Dr. Chang completed her doctoral work in theoretical, computational
chemistry at Stanford University with post-doctoral work in computational
genetics at Stanford Medical School. After that, she was a health scientist at
the consumer genetics company, 23andMe. Using her experience in
statistics, algorithm design, data analysis, and scientific study, she has
created a unique research-driven approach for understanding risk of
disease, projecting longevity, and using that for financial planning.
A few of Dr. Chang's Contributions to Scientific Research:
1. Rains, Emily K., and Hans C. Andersen. "A Bayesian method for
construction of Markov models to describe dynamics on various time-
scales." The Journal of Chemical Physics 133.14 (2010): 144113.
2. Rains, Emily K., and Hans C. Andersen. 2009. A Bayesian approach for the
construction of Markov models. Abstract for poster presentation for the
World Molecular Kinetics Workshop 2009, Berlin, Germany.
3. Rains, Emily K., and Hans C. Andersen. 2009. Constructing Markov
Models for Protein Folding Simulation. Abstract for poster presentation for
the World Molecular Kinetics Workshop 2008, Berkeley, California.
4. Marusak, Rosemary A., Kate Doan, and Scott D. Cummings. Integrated
Approach to Coordination Chemistry: An Inorganic Laboratory Guide.
Hoboken, NJ: Wiley-Interscience, 2007. Print.
1. 23andMe’s Health Content Scientists. 23andMe Blog. June 28, 2013. Web. https://blog.23andme.com/23and...
 Dobkin C, Finkelstein A, Kluender R, Notowidigdo MJ. The Economic
Consequences of Hospital Admissions. Am Econ Rev. 2018;102(2):308-352.