Challenge 3: Spensor

A study by the Federal Reserve Board found that 31% of Americans have no retirement savings. Though this problem appears universal, a potentially more alarming statistic from the same report indicates that among adults aged 55-64, who have just a few years left to accumulate an adequate retirement savings fund, still 19% have no savings whatsoever.Our app attacks this financial epidemic, and though it has some all-encompassing and widely applicable features, we have chosen to specifically target the Baby Boomer generation – those adults, aged about 45 and up, that are nearing their retirement in 10-20 years, and yet, a significant portion of this population has saved little to nothing towards their retirement.

This phenomenon can be attributed to several factors, the biggest of which are the lack of financial role models (people that these adults would have been able to model their savings habits after) and a fundamental lack of understanding of the quantity of money required for the lifestyle they wish to lead after retirement. This lifestyle, 88% of the time, is one simply defined by “financial peace of mind,” as opposed to the 12% that say they are saving in the interest of “accumulating as much wealth as possible” (Yahoo). But still, currently, the average comfortable retirement costs around $700,000, a statistic that younger generations are increasingly aware of, but that older generations have little time to turn into a reality.

Our app seeks to motivate this crowd of near-retirees by addressing their most detrimental attribute: their continued misunderstanding about the scale to which they should be saving. On one level, the size of a nest egg gives little comprehensible information as to how much money a person will have to spend in a given week or given month. What may seem like a great deal of money will in fact, pay out only meager dividends on a week-to-week or month-to-month basis. Making this disparity more evident would serve as an excellent motivator for continued patterns of saving. On another level, once they hit 50, Baby Boomers also have access to a multitude of programs that allow them to save more money in a tax-free or tax-deductible way. Both 401(k) and health savings account programs allow for greater investment opportunities for participants over 50. By taking these disparities and opportunities, and making them either easier to address or take advantage of, our app seeks to tighten the strings on any Baby Boomer coming to saving late in the game.

The app thus seeks to provide two main functions: increase the user’s awareness of how much money they need to save in a contextual, relevant way, and act as a saving tool to enable incremental, automatic transactions to saving programs. The main inputs include the user’s primary bank account information (to understand spending habits and enable transactions from checking accounts to savings accounts) and the user’s savings account information (i.e. IRA, HSA, 401(k)) to accept these transactions.

The app’s main logo.

The one-time setup of the app requires users to input their savings information: how many years they anticipate saving for, how many expected years of retirement, current income, current savings, etc. Through this information, the app will calculate how much money the user is expected to have to spend for a given year, month, and week of retirement. This calculation is based on fairly standard estimations on anticipated required retirements savings, but the user can override the defaults of the calculation if they wish to personalize it more. For example, the calculation will assume that the user requires 70-80% of their income pre-retirement to maintain that same lifestyle during retirement. This approximation has been tested by Retirement Research Groups in major Investment Management companies, and has proven to be a good starting point for estimating the amount that will need to be saved within 10-15 years of retirement. However, if the user recognizes that they can live off quite a bit less, they can manually adjust this percentage to reflect a lower value, which will feed into the next functionalities.

Once the savings prediction and calculation is completed, the app will use information from the user’s current spending habits from their bank account history to identify different categories of spending. This is similar to how many banks segregate spending according to different retailers, such as entertainment, shopping, food, rent, etc.. Considering these groupings, the app will determine what portion of overall spending is designated to these different areas, and can estimate, based on existing savings, what amount the user will have to spend on these different areas during retirement. For example, if the user currently spends $200 on groceries each week, which is 20% of their weekly salary, the algorithm will expect a similar breakdown of spending for the future, and will predict the amount of money the user will have for weekly groceries during retirement. Perhaps the user is only expected to have 50% of their current income, so they will only have $100 to spend each week.

Using this anticipated future spending prediction, the app will alert users of the spending limitations of their future selves, at strategic times, and provide suggestions prior to spending money in each specified area. For transactions that can be repetitive with predicted timing, like weekly grocery shopping, the app will recognize when the user goes on their weekly grocery trip, will notify the user of their future budget, and suggest an incremental change to their spending prior to making the purchase. For spending that is not as repetitive, such as different entertainment activities, the app will alert the user on days where spending on these activities is common. For example, a notification on Friday afternoons for their future entertainment spending breakdown and recommended spending budgets could provide timely feedback before potentially expensive weekend activities.

Some sample screenshots from the app in action.

The above functionality seeks to provide users with a better understanding of how their retirement savings breaks-down in terms of relatable, current events. As noted in the demographic research, many people do not recognize how a large retirement nest translates into daily spending decisions and limitations. By providing a sort of ‘trigger’ relating to the present activity, users will be more aware of the implications of their spending.

The second f
unctionality of the app is to actually promote saving, as information on how much money the user will have during retirement does little if it is just left as information. To do this, the app will allow the user to automatically save the difference between what they could have spent, and what they did spend (i.e. their incremental savings). This will appear as a sort of notification around the time of purchase, having the user agree to this automatic savings just before or after the transaction in question. If saved, that incremental amount will automatically get moved from the user’s checking account and into their savings account. The algorithm can also learn which types of transactions the user tends to save on, and can predict where more saving could be done, or where the user will not budge on spending habits.

The functionality of this app is centered around Fogg’s three factors for human behavior. In general, people want to save, and in particular, the older generation feels a greater urgency to do so. The motivation is there, but it is often forgotten or misunderstood. By providing relevant breakdowns of a user’s future spending habits with respect to their current decisions, the motivation to save is made much more clear and relevant. The ability of a user to save is also improved, as the incremental, automatic saving contributions are easy and require little-to-no user input. This is in stark contrast to the arduous task of meeting with a banker and moving large sums of money at one time. And although moving small, incremental amounts may not be as notable as larger sums, it can make financial freedom significantly easier later on. Finally, the pre-purchase alerts and post-purchase savings option acts as triggers for the user, so savings become relevant to everyday decisions and help maintain that consistent motivation.

For these reasons, we have named the app Spensor – a portmanteau of “spend” and “censor,” that can also be heard and understood as a friend’s name (“Spencer”). Spensor works to curtail spending in an incremental and not overly invasive fashion, while communicating with you as a friend might. Spensor is the financial role model that many bemoan not having had earlier in life. Spensor’s approach is colloquial and familiar so that your habits of saving are developed simply and in a friendly environment. You stick with Spensor because he helps you get the savings you need, and because you probably couldn’t do it without him.

A breakdown of the information taken in by the app and how it affects the flow of money within purchases and savings.

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