The Modeling the Future Challenge is using an open-theme for the 2021-22 season where students can define their own project! As part of Phase One of the Challenge, your team will submit a Project Proposal to help solidify your ideas for the project topic you would like to pursue in Phase Two when you complete your full Project Report. Click the button below to download our Project Proposal Template to start researching your report topics.
To help get started, we are providing example Data Sources and example project proposals below. Explore the data sources to get a sense for what topics you are most interested in, review the example Project Proposals below to see how to submit your one-page proposal to the challenge, and use the Actuarial Process Guide to help you step through the process of defining and conducting your own MTFC research project!
Project Proposals Examples
Agriculture Project Proposals
Sample 1
Part 1
Problem Statement
Climate change is expected to increase the number of severe storms in the Midwest. Iowa’s corn farmers are especially susceptible to crop losses due to these severe storms. This project analyzes how the state government of Iowa could implement new policies to help mitigate these losses for their corn farming industry.
Part 2
Data Sources
We plan to use data from the USDA’s Risk Management Agency to identify how many crop insurance claims were a result of severe storms in Iowa. We also will use data from the US Global Change program and the NOAA NASS information to help identify how the severe storms are projected to change in the coming decades.
Part 3
Mathematical Modeling
We expect to do a number of regression analyses on the climate change information we gather and correlate the change in potential severe storms with the change in how large the crop loss claims on the RMA database are.
Part 4
Risk Mitigation Concepts
Sample 2
Part 1
Problem Statement
Cattle ranchers in New Mexico have seen outbreaks of bovine respiratory diseases in the past. These outbreaks have cause ranchers to lose up to 33% of their herd in one season. This project analyzes past data on New Mexico cattle production and makes recommendations to the cattle ranchers on how to best mitigate the risks due to potential future outbreaks of bovine respiratory disease.
Part 2
Data Sources
We will use the ISDA NASS data on cattle yields in New Mexico as well as other research we gather on the prevalence of bovine respiratory diseases. We will also gather information on the average head of cattle lost during these outbreaks to help identify a total severity of loss.
Part 3
Mathematical Modeling
We will analyze the size and distribution of losses due to each outbreak of bovine respiratory disease in cattle herds throughout New Mexico for the past 20 years. We will evaluate the distribution of loss to see if there are patterns in years with high outbreaks. We will also model government programs to institute vaccines for these diseases to evaluate how they could save their ranchers and the state government money.
Part 4
Risk Mitigation Concepts
Sample 3
Part 1
Problem Statement
Organic farms are not allowed to use pesticides to protect their crops putting them at increased risk of losses due to disease and pests. Particularly, Japanese beetles are shown to be very destructive to crops grown in the Mid-Atlantic seaboard of Maryland and Virginia. This projects provides recommendations to the state governments of Maryland and Virginia on how they could help protect organic farmers in their states from these losses.
Part 2
Data Sources
We will use the USDA Risk Management Agency’s information on crop insurance losses. We will identify the losses by the “Cause of Loss” in their spreadsheets and filter on just those by insects and pests. We will also research additional information on the common pests in our region from state agriculture websites.
Part 3
Mathematical Modeling
We will analyze the size and distribution of losses due to pests and insects. We will evaluate the distribution of loss to see if there are patterns in years with high losses. We will also model the savings if our interventions were implemented to see how much money the state could be saving or making in additional crop yield.
Part 4
Risk Mitigation Concepts
Climate Change Project Proposal
Sample 1
Part 1
Problem Statement
Flooding on the upper Mississippi and Missouri rivers is becoming increasingly likely and increasingly severe. Property within the flood plains of these two rivers in Missouri and Iowa is at increasing risk due to these events. We evaluate how the National Flood Insurance Program will need to modify future programs to best protect these communities without making insurance prohibitively expensive.
Part 2
Data Sources
We plan to use the FEMA NFIP insurance data and filter it by losses in Missouri and Iowa due. We will combine this with information from the Global Change program to evaluate how climate change is affecting new floods in the future.
Part 3
Mathematical Modeling
We will take the NFIP data and conduct a random walk analysis to explore how likely it will be for extreme losses to happen in the future as climate change makes the severity and likelihood of flooding higher. We will then connect this with the values of loss on the NFIP insurance claims.
Part 4
Risk Mitigation Concepts
We will examine how the the NFIP might adapt to the additional risk of flooding in these regions, we will think about recommendations for state and national government insurance and policy interventions.
Sample 2
Part 1
Problem Statement
As the ocean climate warms, traditional fisheries are moving northward putting fisherman in lower latitudes at increasing risk of loss in production. This project examines trends in changes in ocean fisheries along with correlations to expected changes in ocean temperatures, and circulation patterns. We make recommendations to the Florida state government about new policies that may help protect the state’s fisheries against these potential risks.
Part 2
Data Sources
We will use information from the USDA NASS database about how much the fisheries produce in Florida’s coastal waters. We will combine this with information gathered from other sources about how fisheries have been migrating to other waters.
Part 3
Mathematical Modeling
We will analyze the potential loss of fishing revenue from Florida coastal fisheries over the past years and look for annual trends. We will project these trends into the future and make some assumptions about how fishing will be at risk due to additional losses from climate change migrations.
Part 4
Risk Mitigation Concepts
We evaluate policies from the Florida State government to help protect its commercial fisheries from extreme losses using insurance products from the government. We will also explore incentive programs from the government to help transition fisheries into other industries.
Sample 3
Part 1
Problem Statement
As the climate warms over the coming decades, the Sierra Nevada snow pack is expected to shrink. This could result in losses to many industries. One particular industry that relies on this snow pack is the skiing and winter sports industry around Lake Tahoe, California. We examine how the potential loss in snow pack from climate change could impact the ski resorts around lake Tahoe. We then provide recommendations about how these companies could best manage the risks of future losses.
Part 2
Data Sources
We will use information about snow pack gathered from California’s state government websites. We will also use information about changes in precipitation and temperatures from the US Global Change Program. We will explore additional data on projected snowpacks in the future and will combine this with historic snowfall data from the NOAA website.
Part 3
Mathematical Modeling
We will conduct analyses on several scenarios of climate change identified in the Global Change website. We will compare a high and low scenario to evaluate the potential losses to the resorts. In each scenario we will conduct computer simulations to evaluate potential open and close dates of the resorts using historic data from past years of high and low snow pack in the Sierras. Finally, we will compare loss in revenue from the resorts in years with significantly shortened seasons.
Part 4
Risk Mitigation Concepts
We will evaluate options for the ski resorts to pay for the development of additional summer recreation activities to supplement lost revenue from shortened winter skiing seasons. We will examine the potential costs of expanding summer revenue options and compare that to the loss of revenue from not opening for skiing.
Drought Project Proposal
Sample 1
Part 1
Problem Statement
On July of 2014, 58% of California was noted as in d4 level of drought (the most severe level). Northern California has experienced increasingly severe fire danger due to high prevalence of droughts. Entire cities, such as Paradise, CA have been virtual leveled in severe summer fires. This project analyzes historic fire dangers and models future risks to Northern California towns based on the expected increase in fire danger due to climate change. We make specific recommendations to the city of Butte Meadows, CA on how to help mitigate potential risks of future fire damage to the city.
Part 2
Data Sources
We will use data from the National Integrated Drought Information System, information on the increasing risk of fires due to climate change from the US Global Change program, and additional local information sources from Butte Meadows to analyze and model future risks. We will also evaluate from FEMA disaster sources when disasters have been declared for fire situations.
Part 3
Mathematical Modeling
We will analyze historic drought and fire disasters from FEMA and NIDIS systems. We will identify relationships and correlations between the size of fire damage or loss in FEMA data and the environmental conditions identifying in NIDIS when fires are present. We will also then examine trends in the data and connect with climate change information from US Global Change to explore how much of an increase in losses might be expected as the climate dries and causes more likelihood of fires.
Part 4
Risk Mitigation Concepts
We explore the local costs to Butte Meadows city government and compare the costs of implementing a “fire ring” around the city center to increase the speed and availability of fire response in the event of an extreme fire danger like happened to Paradise, CA in the last major fire in the region. Our goal is to mitigate the potential extreme loss outcomes by increasing the fire safety of the city without going over the expected loss costs of an extreme fire event happening in Butte Meadows.
Sample 2
Part 1
Problem Statement
Climate change is increasing drought conditions in Colorado’s Rocky Mountains, and lowering seasonal snow pack. Many reservoirs rely on seasonal snow pack to replenish their waters. Cities such as Grand Junction, CO rely heavily on these reservoirs for drinking water and all other types of fresh water use. Residents of Grand Junction may be at risk of future water outages imposed by the city if the snow pack continues to shrink. We explore how residents of Grand Junction could protect against the risks of having an imposed water outage through behavior change, insurance, and modifying outcomes of potential losses.
Part 2
Data Sources
We will use the NIDIS snow pack monitor information as well as additional NASA Data we have found measuring the seasonal extend of Colorado snow pack. We will also use information on future climate change from the US Global Change program. Additionally, we will use information on the amount of water used per resident per year and local Grand Junction ordinances and information on the history of water shortages throughout the rocky mountains.
Part 3
Mathematical Modeling
We will analyze the historic likelihood of Colorado reservoirs running dry and forcing Grand Junction to impose water limitations on its residents. We will also include information from other Rocky Mountain regions to make the model more robust. We will include historic trends and trends in climate change to evaluate how the likelihood of a water shortage could increase in the coming decades through 2050. We will then analyze the severity of potential losses to each home owner based on how much water is used for various activities.
Part 4
Risk Mitigation Concepts
We will explore the risk mitigation costs of rainwater catchment systems and explore how insurance companies might be able to offer products that could help residents eliminate the highest risks of a severe water shortage through support of these systems.
Flood Project Proposal
Sample 1
Part 1
Problem Statement
Homes built in the Mississippi River flood plain (particularly in the lower Mississippi) are having a harder time securing affordable flood insurance due to the increasing likelihood of floods and extreme precipitation from climate change. In this project we examine new options for how local governments and non-profit organizations could help home owners with innovative insurance options.
Part 2
Data Sources
FEMA National Flood Insurance Program data will be our primary source to identify the severity of losses from historic flood events in the lower Mississippi River area. We will also use data from US global change program to evaluate how the likelihood or frequency of flooding might change in the coming years.
Part 3
Mathematical Modeling
We will conduct regression analyses on historic flooding data and the severity of losses for Louisiana counties bordering the Mississippi River. We will extrapolate the likelihood of floods and identify how climate change may be increasing these likelihoods.
Part 4
Risk Mitigation Concepts
Because changing the climate is so difficult, and not much can be done at just a county by county level for this, we focus risk mitigation on increases to insurance premiums that may be needed in the future as well as possible risk mitigation through flood control and barriers.
Sample 2
Part 1
Problem Statement
n the National Climate Assessment from the US Global Change Program, it is expected that sea levels will rise over the coming decades. This poses a significant risk to our city of Charleston, SC. Much of Charleston is already underwater for portions of the year and even with small changes in sea level, this is expected to get much worse. We explore how the city government of Charleston can best respond to these future risks being brought about by climate change.
Part 2
Data Sources
We expect to use data gathered from the city of Charleston itself about flooding events the city has seen over the past 2 decades. We will also use information from the FEMA NFIP to gather more detail on losses from these flooding events. We will then combine this data with information from the US Global Change program about how climate change is expected to increase sea levels in the coming decades.
Part 3
Mathematical Modeling
We will us computer programming and random walk analyses to evaluate possible outcomes of loss for future sea level scenarios in Charleston. We will evaluate loss to private property as well as loss of tourism expected from the increase in flooded streets and closed businesses.
Part 4
Risk Mitigation Concepts
We evaluate the costs of having new sea walls, dams, and levees built in strategic locations around Charleston to protect its historic downtown businesses from future losses.
Other Natural Disasters
Sample 1
Part 1
Problem Statement
Tornadoes cause hundreds of people to lose their homes each year in the united states. FEMA spends millions of dollars on Housing Assistance Programs to support these households, and this is expected to increase as climate change increases the likelihood of powerful tornadoes and storms. This project evaluates a possible risk mitigation strategy of using community housing with neighbors and other willing participants (such as AirBnBs) to provide affordable and valuable housing needs during and after these disasters.
Part 2
Data Sources
We will use FEMA’s individual assistance program data to identify how often and how severe losses occur from tornadoes. We also will use information about the total housing stock and the availability and costs of renting AirBnBs.
Part 3
Mathematical Modeling
We will examine the current costs of FEMA provided Housing Assistance and the potential costs of supporting community housing services such as AirBnB rentals for those who have lost housing in a tornado disaster. We will compare these costs and availability of housing, then we will extrapolate future potential costs through trend regressions on the FEMA data and climate change information that may be used to project future increases in tornado occurrence.
Part 4
Risk Mitigation Concepts
Our risk mitigation concept is to provide housing in extreme loss circumstances to those who no longer have housing through AirBnB or other community housing services. Particularly in the case where rental properties may be vacant. We examine insurance options to help pay for these possible housing situations instead of having FEMA trailers brought to the site of the disaster.
Sample 2
Part 1
Problem Statement
As climate change increases the likelihood of severe storms in Texas over the coming decades, many Texas communities will not be able to support the costs of cleanup and repair to critical infrastructure after these disasters. Traditionally, FEMA has supported some of these costs through their public assistance and Hazard Mitigation programs. We examine risk mitigation strategies for three Texas communities to help minimize the costs of post-disaster cleanup by limiting downed powerlines and other loses to critical infrastructure.
Part 2
Data Sources
We primarily rely upon the FEMA Public Assistance and Hazard Mitigation databases to determine the severity and frequency of historic losses. We then also use the US Global Change program data to evaluate potential trends in increasing losses in the future.
Part 3
Mathematical Modeling
We will model the severity and frequency of historic losses in our three Texas communities over the past 30 years. We will include inflation adjustments to the data to gather a more accurate trend line. We will then evaluate how climate change might be expected to change these trends over the coming 30 years to 2050. We will determine a 95% confidence interval on how large future damages could be to severe storms in our three communities based on this data.
Part 4
Risk Mitigation Concepts
We will look at possible insurance adjustments that could help these cities pay for the increase in loss from these storms. We also will explore possibilities to pay for extended power line protection programs of cutting back trees and otherwise protecting from power outages since we hypothesizes that the majority of infrastructure costs come from repairing or replacing downed power lines.
Labor and Employment
Sample 1
Part 1
Problem Statement
Many workers in southern New Mexico rely on seasonal farming jobs. Climate change is making it more difficult to maintain these jobs as much arable land in this region, increasing the risk of lost wages for this already impoverished community. This project evaluates wage insurance products that the state of New Mexico could provide to its rural farming community to help protect against the potential of lost farming wages due to severe climate events.
Part 2
Data Sources
We use the BLS data on employment to identify specific jobs in the agricultural sector for southern new mexico. We want to see how agricultural jobs are changing over the years. We also use information from the US Global Change program and the USDA’s NASS program to identify trends in how arable land use in New Mexico is changing.
Part 3
Mathematical Modeling
We will model how agricultural jobs are changing (shrinking) over the years. We are assuming that many of these job losses are due to loss of arable land. We evaluate the size of these lost wages for southern new mexico communities of Dona Ana, Sierra, and Luna Counties. We then extrapolate to see how unemployment payments could be at risk of extreme payouts due to future losses of jobs in this sector.
Part 4
Risk Mitigation Concepts
We explore how job programs could be implemented in southern New Mexico to prevent the unemployment of these communities going up. As Agricultural jobs wane, we imagine that new job sectors could be supported to replace and even increase on those lost wages.
Sample 2
Part 1
Problem Statement
In Arizona, mining has become one of the top industries, employing 6% of the total workforce. Unfortunately, mining is also a dangerous profession. There is significant risk to Arizona’s mining companies in paying for injuries and fatalities to their workers. This project examines possible behavior modification strategies and corporate insurance strategies to help minimize these risks for Arizona’s top mining company.
Part 2
Data Sources
We use the Bureau of Labor Statistics’s information on workplace injuries and fatalities. We specifically identify injuries and fatalities in the mining sector in Arizona over the past 30 years.
Part 3
Mathematical Modeling
We will extrapolate from historic data on mining industry fatalities and injuries to explore upper and lower bounds on expected casualties in the future. We will create a confidence interval for these high and low bounds. We will then model a possible insurance plan for mining workers to protect their families against the possible loss from a mine accident or casualty.
Part 4
Risk Mitigation Concepts
We examine insurance policies to adequately protect mining families from losses. Because pay for these jobs is already very low, we explore alternative options where the mining company or mining communities may pay for these insurance plans to support their communities.
Poverty Project Proposal
Sample 1
Part 1
Problem Statement
The poverty rate of Clay County, WV has remained between 30% and 40% for decades. This is significantly higher than the US national average, and the West Virginia state average. Poverty is linked to significantly higher crime rates which cost the county millions of dollars each year to manage. This project evaluates a basic minimum wage poverty reduction strategies for Clay County, WV to help reduce the financial burden of increased crime in the county.
Part 2
Data Sources
We use Clay county crime reports for the past 10 years as well as data from the Census Bureau’s SAIPE data tool to identify poverty rates and crime correlations.
Part 3
Mathematical Modeling
We conduct a correlation analysis to determine an r-squared value between crime and poverty. We also use background information ton validate this. We project future costs over the next 10 years due to criminal activity and compare this with another West Virginia county housing Morgantown, WV which has a significantly lower poverty rate, and crime rate. We then evaluate the costs of providing a basic minimum wage in Clay County, and compare this to the potential savings from an expected drop in criminal activity.
Part 4
Risk Mitigation Concepts
Our main risk mitigation concept is that of a basic minimum wage for all Clay County residents. We compare the costs of paying a basic minimum wage with the potential losses and expenses of crime in the county.
Sample 2
Part 1
Problem Statement
In Los Angeles, an innovative poverty reduction program implemented in 2014 has helped to reduce the poverty rate from 26.4% to 19.5%. This project evaluates the potential for replication of this program in other metropolitan areas such as Miami Florida, with similar poverty rates, and metropolitan characteristics. However, this program comes at a cost. We evaluate the potential poverty risk reduction benefits of implementing such a program in Miami. We explore if property insurance companies could benefit from such a program and potentially help pay for it.
Part 2
Data Sources
We use information on the historic poverty rates from the US Census SAIPE program, and evaluate relationships and correlations with data on property damage from the Miami-Dade county information. We make some assumptions about property damage costs because we only have the number of incidents and not information on total insurance losses.
Part 3
Mathematical Modeling
We model the relationships and correlations between property damage and poverty in Miami and compare it with similar information from Los Angeles County in 2014-2015.
Part 4
Risk Mitigation Concepts
Our risk mitigation concept is to implement a similar program in Miami as what was implemented in Los Angeles to help lower the risks and costs of property damage by lowering the overall poverty rate. Our innovative idea is to connect with property insurance companies to help pay for this intervention.
Health Project Proposal
Sample 1
Part 1
Problem Statement
In 2014, the Affordable Care Act (ACA) was implemented, reducing the National uninsured rate from 31.7% to 17.8%. However, uninsured populations in some states continued at high rates, and the trend in the percentage of uninsured is now increasing in states such as Texas and Iowa. Uninsured populations pose a significant risk to the healthcare industries in these states and nationally. This project defines these risks and analyzes behavior modification risk reduction mechanisms that the Texas state government could enact to help increase the percentage of insured.
Part 2
Data Sources
This project relies on the Census Bureau’s SAHIE data to determine the number of people insured. We then also combine this with information from the CMS and CDC and other research online to gather background information on the likely costs to the healthcare system that each uninsured citizen poses on the health care system in any state.
Part 3
Mathematical Modeling
We examine the trends in health insurance in Texas and we identify overall costs of uninsured residents in Texas. We evaluate the costs of uninsured residents based on age and the expected costs of care depending on their age. We then compare this to potential costs of behavior modification programs to help more Texas citizens buy in to health care.
Part 4
Risk Mitigation Concepts
We examine two potential mitigation concepts: (1) a Texas-wide marketing plan to encourage citizens to purchase insurance, and (2) a Texas-wide payment plan where the government will pick up a percentage of health insurance costs for the first years of a plan given the resident then continues the plan for the next 5 years at least.
Sample 2
Part 1
Problem Statement
Air pollution is linked to a number of physical and respiratory diseases. While fine particulate matter has been on a decline overall in the United States, during extreme heat events in the desert southwest spikes in fine particulate matter have been seen causing health risks, particularly to the elderly in cities such as Phoenix, AZ. In this project we define the severity of these health risks associated with increased heat waves and the related spikes in fine particulate matter in the air. We then make recommendations for additional insurance products that specifically address these risks.
Part 2
Data Sources
We use the CDC Wonder Database to identify environmental factors like air particulates and also the health risks and costs associated with this. We also include information from the US Global Change Program to model the potential increase in 100 degree days in Arizona.
Part 3
Mathematical Modeling
We will model the potential increase in fine particulate days and 100 degree days based on information from the climate change site. We will compare trends in this information and connect it to the health care expenses identified from the CDC WONDER database.
Part 4
Risk Mitigation Concepts
We explore insurance products to specifically help residents of Phoenix, AZ cover risks of hospitalization due to increasing high heat and high particulate matter days.
Sample 3
Part 1
Problem Statement
The Navajo nation, spread across primarily Arizona and New Mexico states has an elevated prevalence of alcoholism and alcohol related diseases which has become one of the leading risk factors for death in these populations. The risks of premature deaths is especially notable in the 18 to 25 age range for this population. In this project we evaluate two potential risk mitigation strategies to help the Navajo Nation address these continuing risks to their young population.
Part 2
Data Sources
We use the CDC WONDER and WISQARS databases to gather information about alcoholism rates and fatalities due to alcoholism.
Part 3
Mathematical Modeling
We explore the risks of death due to alcoholism and identify correlations between the percentage of the populace that is identified as an alcoholic and deaths due to alcoholism.
Part 4
Risk Mitigation Concepts
We evaluate the potential economic cost of a ban on alcohol sales in the Navajo nation and explore opportunities for the Navajo nation, or the US Federal government to help Navajo youth stay away from alcoholism through payment to the nation for ban of alcohol sales.
Sample 4
Part 1
Problem Statement
Pittsburgh, PA has seen an incredible increase in the number of Opioid overdoses within the last decade. This trend is expected to continue without additional intervention. The city’s hospitals and Medicare and Medicaid services are spending significant dollars on treating these patients who are not able to pay their bills. We examine methods of reducing the loss expected to Pittsburgh’s hospitals and Medicaid from increasing opioid overdoses.
Part 2
Data Sources
We use the CMS data on Opioid treatment to quantify the expenses on these issues. We also look at other CMS data to quantify these numbers.
Part 3
Mathematical Modeling
We model the likely continued Opioid overdoses in Pittsburgh in the future 10 years based on historic trends. We then identify the potential healthcare expenses caused by these overdoses and model how many likely overdose cases are not insured.
Part 4
Risk Mitigation Concepts
We examine possible health insurance options that the City of Pittsburgh could implement through a tax increase to help cover the cost of care and rehabilitation for opioid patients so that the burden doesn’t fall on hospitals themselves.
Sample 5
Part 1
Problem Statement
Diabetes cost the US billions of dollars in health care expenses. There is an increasing trend in the number of people with diabetes in the US. We will analyze how the federal government could support the development and implementation of future stem cell therapies that are expected to come to the market for diabetes treatment.
Part 2
Data Sources
We use information from the CMS Chronic Conditions database to identify trends in diabetes over the years and identify expenses related to diabetic care. We also have identified possible stem-cell treatments that are being tested now.
Part 3
Mathematical Modeling
We then combine this with information we have found about potential stem cell treatments for Diabetes. We then evaluate the potential savings from reducing the number of diabetic patients needing long-term hospital care.
Part 4
Risk Mitigation Concepts
We explore government insurance support that could help encourage the use of these new Stem Cell treatments to lower the costs of diabetic care.
Sample 6
Part 1
Problem Statement
EMS response to drug overdoses from uninsured patients costs millions of dollars each year. In Ohio, Opioid overdoses have been on an increasing trend the past 10 years; however, many of these cases requiring emergency response are not insured. We evaluate the continuing risk to the Ohio EMS services and analyze risk mitigation possibilities for the state of Ohio government.
Part 2
Data Sources
We use data from the EMS website to identify the number of deployments due to drug overdoses. We also use information from the CDC WONDER database about Opioid care.
Part 3
Mathematical Modeling
We evaluate the long-term trends in EMS response to Opioid overdoses in Ohio. We explore the potential increasing costs on the EMS system for un-insured overdose victims, and use this to examine possible mitigation strategies that would be worth it to the state and EMS systems.
Part 4
Risk Mitigation Concepts
We explore government insurance plans and Opioid behavior change campaigns that could be cost effective for the state of Ohio and EMS providers.
Veterans Project Proposal
Sample 1
Part 1
Problem Statement
Post-Traumatic Stress Disorder (PTSD) is a significant risk to veterans returning from war or conflict zones. PTSD treatments currently cost the Department of Veterans Affairs millions of dollars each year, and many cases of PTSD continue to go untreated causing risk of additional healthcare needs. In this project we evaluate alternative care services that may help reduce the overall risk of losses in treating PTSD for our veterans.
Part 2
Data Sources
We use the Veterans Affairs Utilization tables to determine how much the Bureau is spending on PTSD treatments.
Part 3
Mathematical Modeling
We model the costs of PTSD treatments year over year through the VA. We extrapolate trends and also examine ancillary costs that may be associated with PTSD treatments not reported.
Part 4
Risk Mitigation Concepts
We evaluate potential low cost treatments that have shown promise in local area deployments – such as community nature hikes – to see the potential for state or VA supported programs such as these at a national scale.
Sample 2
Part 1
Problem Statement
Future escalation of conflicts throughout the world could potentially involve hundreds of thousands of American Troops according to the DoD. If this escalation were to happen, even though the likelihood of such an escalation is small, the costs of caring for the additional veterans support services would be astronomical. In this MTFC project, we evaluate the potential risks to the Department of Veterans Affairs if such an escalation occurs, and analyze potential insurance strategies to help the government pay for the increased costs.
Part 2
Data Sources
We use the VA utilization tables and expenditure tables as well as a report from the DoD evaluating the potential of a China-US war or other future large scale conflict.
Part 3
Mathematical Modeling
We use the number of potential soldiers involved in future conflicts and model the long-term VA benefits provided to soldiers. We model costs based on previous conflicts and VA data.
Part 4
Risk Mitigation Concepts
We examine risk mitigation of a large-scale federal insurance policy protecting against Veterans Affairs losses due to the possibility of future conflicts.
Retirement and Social Security
Sample 1
Part 1
Problem Statement
The total cost for Social Security Benefits has now become the number one government expense at the national level. Disability insurance benefits are a large portion of this cost. As the nation’s populace ages over the next decade these expenses are expected to continue to rise. We evaluate a risk mitigation strategy to help lower the cost of disabled worker benefits by recommending a national worker re-training plan to help disabled workers gain training in other jobs.
Part 2
Data Sources
We use the Social Security Administration’s reports on SSA Social Security Disability Insurance. We also use information we have found on a local job-training program that we model for risk mitigation.
Part 3
Mathematical Modeling
We compare the potential future costs of the SSA Disability Insurance program – modeled through trend analysis – and evaluate the potential of the costs of a re-training program.
Part 4
Risk Mitigation Concepts
Our main concept is in the potential to replace all or some part of the long-term Disability Insurance program with a short-term job re-training program to learn new skills that they can still do. This program could support continued wages for previously disabled workers and eliminate the need for long-tern social security disability payments.
Sample 2
Part 1
Problem Statement
Extreme financial crises like the 2007-08 Financial Crisis, and the global COVID-19 pandemic can cause significant risks to financial holdings of all types. In this project we evaluate the risks of future pandemics on the Pension Benefit Guarantee Corporation and explore how novel pandemic insurance may be helpful.
Part 2
Data Sources
We use data from the Pension Benefit Guarantee Corporation to identify the total number of claims on pension insurance during large scale financial crises like the 2007-08 financial crisis and the current COVID-19 pandemic.
Part 3
Mathematical Modeling
We will model the potential losses to the PBGC through these crises to project potential future losses, and examine the likelihood of a new crisis happening.
Part 4
Risk Mitigation Concepts
We will explore how novel pandemic insurance may be useful if the likelihood of pandemics is projected to increase as many expect.
Housing and Homelessness
Sample 1
Part 1
Problem Statement
A pandemic like COVID-19 can cause significant disruption to family incomes. In 2020, U.S. unemployment rose to over 20%. This could cause a rush of new applicants and families in need of low-income housing subsidies. The risks are at programmatic, local government, and individual family levels. In this project we evaluate the risks to the local government of an already low-income region, South Chicago, that the loss of income due to future pandemics such as COVID-19 could have on family abilities to pay for housing. We evaluate what the local government could do to help avoid a flood of evictions or new homeless families in their region.
Part 2
Data Sources
We use the HUD pictures of low-income households to gather information on how many families in Chicago are receiving low-income housing support. We also use information from COVID-19 data sources, and unemployment data sources to model the potential increase in applicants for low-income housing in the event of a future pandemic.
Part 3
Mathematical Modeling
We model the likelihood of a potential increase in low income housing applications in the event of a future pandemic similar to that of the COVID-19 crisis. We use unemployment figures from Chicago and low-income housing figures from HUD, and historic unemployment cases to generate a model of how future pandemics could affect low-income housing needs.
Part 4
Risk Mitigation Concepts
We evaluate how the local city government of Chicago could implement insurance or modified outcome mitigation strategies to help limit the number of evictions caused by a future pandemic.
Sample 2
Part 1
Problem Statement
Many small cities in the United States are reliant upon 3 to 5 major employers supporting large portions of their populace (such as large manufacturing plants). If one or more of these employers shuts down, a significant portion of the city’s population could be at risk of losing their homes due to defaulting on a mortgage. This project explores how Mortgage banks could support an innovative insurance option to help support families in these extreme circumstances and reduce the risk of foreclosures.
Part 2
Data Sources
We use computer modeling for the majority of this work and only pull some statistical information on foreclosure and eviction rates from HUD and other online informational sources.
Part 3
Mathematical Modeling
We use our own computer modeling to evaluate various situations in smaller cities based on the number of large employers, the median costs of homes and several other variables to determine how large of an effect losing one or more major employer would have on these cities.
Part 4
Risk Mitigation Concepts
We evaluate a home insurance product that could be provided to smaller towns with limited access to new jobs and a potential high-risk of foreclosure. We examine how the mortgage banks might find it valuable to include such insurance in their mortgages.
Transportation and Automobiles
Sample 1
Part 1
Problem Statement
San Francisco, CA is known as “Fog City” for its dense covering of fog that typically occurs during the summer months. This natural hazard has been linked to significant automobile accidents throughout San Francisco county. We evaluate the costs of these accidents to the city and recommend risk mitigation strategies to modify driver behavior in these foggy months.
Part 2
Data Sources
We use the NHTSA, FIRST reporting system to gather information on the number of fatalities and injuries in San Francisco that are due to visual impairment.
Part 3
Mathematical Modeling
We model the costs of these accidents due to foggy conditions including the number of injuries and fatalities. We also attempt to gather information on the overall cost of auto damage, but make some assumptions on these numbers.
Part 4
Risk Mitigation Concepts
We evaluate the possibility of lowering the speed-limit during foggy conditions on highways and placing additional high-visibility signage on the roads to help reduce high-speed accidents in foggy conditions.
Sample 2
Part 1
Problem Statement
Distracted driving has become a serious transportation safety risk across the country. It is even more pronounced in young drivers aged 16 to 25. We evaluate the overall risks that distracted driving is causing the country in terms of accidents and costs of care for those involved. We then recommend an alternative insurance product involving a hands-free guarantee phone system in cars.
Part 2
Data Sources
We use information from the NHTSA about the number of accidents and fatalities caused by distracted driving over the years. We also include information about EMS responses to these accidents to include additional cost information.
Part 3
Mathematical Modeling
We look at annual trends in distracted driving over the past two decades. We correlate this with the rise in cell phone usage, and with some assumptions identify overall costs (in a range) of distracted driving accidents.
Part 4
Risk Mitigation Concepts
We envision a potential insurance product paired with a new cell phone holder case for the car that insurance companies could offer to drivers. So long as the driver’s cell phone is in the holder case throughout the duration of the drive, the owners insurance costs would go down. We evaluate various models for such an insurance product.
Epidemics and Pandemics
Sample 1
Part 1
Problem Statement
The global COVID-19 pandemic has upended lives like little before it. Hospitals in Florida have been particularly hard hit, many nearing capacities. The risk of possible infection particularly for Florida healthcare workers is extreme. We evaluate the costs of a supplemental wage requirement for Florida nurses and doctors during periods of high COVID caseloads to help offset this risk.
Part 2
Data Sources
We use the COVID Tracking Project to evaluate the number of hospitalizations due to COVID in Florida. We also gather information from the CMS database about expenses of long-term hospital care.
Part 3
Mathematical Modeling
We make some assumptions on overall expenses of long-term hospital care due to COVID. We use this information and the information about possible infection rates and model the possibility of hospital nurses getting infected due to their on-the-job work. We then equate this to a potential cost of their care (not to mention the potential loss of life).
Part 4
Risk Mitigation Concepts
We explore a supplemental wage for Florida Hospital Nurses to continue to encourage nurse workers and to help nurses cover the potential loss of income and healthcare expenses if they get infected.
Sample 2
Part 1
Problem Statement
Wearing a mask has been shown to lower the infection rate of COVID-19, yet in Miami-Dade county, Florida, mask use remains low. We evaluate the cost to the Miami-Dade healthcare system imposed by this limited mask use and recommend a monetary and other incentives policy the county government and hospital systems could impose to modify the behaviors of county residents favorably in wearing masks.
Part 2
Data Sources
Miami Dade reports on overall mask use from local news sources will be used. We also use information on the number of cases and hospitalizations in Miami-Dade compared to other counties from the JHU Covid Reporting website.
Part 3
Mathematical Modeling
We make some assumptions about mask use and have some limitations due to the limited data on mask use, but we model how make use has affected COVID hospitalizations. We then explore how the lower mask use in Miami-Dade county is costing the county significant money in terms of healthcare and lost wages.
Part 4
Risk Mitigation Concepts
We explore potential government campaigns and policies that could help increase the overall mask usage in Miami-Dade county. We calculate how much could be spent on these policies and campaigns and still come out ahead in terms of overall loss.
Sample 3
Part 1
Problem Statement
Countries across the globe have had very different responses to COVID-19. The risks of cross contamination from countries with large outbreaks to countries with smaller outbreaks is large. We analyze a potential United Nation’s travel policy for all participating nations to help re-open cross border travel and modify behaviors of citizens in high risk countries.
Part 2
Data Sources
We use the JHU COVID Resource center to identify overall case counts.
Part 3
Mathematical Modeling
We use computer programming to model the ability to open cross-border travel based on the trends in case counts.
Part 4
Risk Mitigation Concepts
We explore a global, United-Nations policy that could be implemented for all countries in the UN to create the best risk mitigation on recurrent COVID-19 cases. We explore the base case of zero-cross border travel, all the way up to 100% open cross border travel.
Sample 4
Part 1
Problem Statement
U.S. counties with high percentages of low-income housing populations have been shown to have higher percentages of COVID cases per 100,000 population. The healthcare costs of caring for low-income patients who often have little to no health insurance is large. We evaluate the cost of instituting a behavioral change mitigation strategy for low-income housing units in the greater Chicago metropolitan region.
Part 2
Data Sources
We use the JHU COVID Resource center and HUD low income housing datasets.
Part 3
Mathematical Modeling
We identify correlations between low-income housing and continued, long-term COVID-19 case numbers. We make assumptions on behaviors in these regions being the cause for potential infection rates. We also equate the severity of loss due to COVID to the potential lost wages from not being able to work while sick with the disease.
Part 4
Risk Mitigation Concepts
We explore the cost of implementing behavior modification strategies to help low-income housing areas improve their COVID-19 protections and lower the risk of losing wages and potential housing.
