Wearable Tech — Expense Tracker
In the endless expanse of new technological products, innovative developments come from looking at the way technology is moving towards in our society. Under the umbrella of AI machinery, machine learning has become a major league player to make it possible for our technology to learn and adapt to our changing and diverse behaviours without the limitations that explicitly programmed code characterizes our technology today. Incorporating machine learning attributes into the users experience is where I started structuring the development of my product.
For the concept of my product, I decided on creating a bracelet that would work in turn as an expense tracker. A person’s finances encompass a large array of different factors including rental costs, a person’s salary, food preferences, established spending habits, emergency occurrences and other situational differences that vary person to person. I started with incorporating an app that would be a way for users to input their payment methods which can include Apple Pay options or similar systems like it, or to simply manually input their credit card information. This feature would be the main recording system for a person’s purchases. This would also be a starting way for machine learning algorithms in the product to learn characteristics of the user and start comparing the individual’s spending habits with global data it has collected.
The app would be the main functionality for users to interact with the bracelet systems. Coherently, the bracelet would work with the app to be a way for the individual to use as a form of payment similar to Apple Pay in Apple Watch products in order to automatically dispense the payment information into the technology’s database. An issue that would arise would be the lack of usability for this technology if people already had other payment methods they preferred. To counter this, the bracelet would be able to sync up with other payment methods to automatically record all your purchases without having to use the bracelet’s payment function directly. Cash options can also be implemented manually as this system works based on the integrity of the user.
To correspond with the app’s software, the bracelet would showcase the user’s spending habits through a visual response on the interface of the bracelet. Unlike the act of ‘closing circles’ on an Apple Watch, the feedback would need to have a negative reinforcement to discourage users from spending money. In studies however, negative reinforcement has been found to not be as effective as positive reinforcement. To counter that issue, money leftover at the end of each day would be allocated to long-term or short-term goals that are established in the app, encouraging users to continue their financial progress.
The bracelet itself would start with vibrant and appealing visuals to and desaturate as money is spent. This would be demonstrated in two signifiers, one that represents the daily results and the other that shows monthly expenses. Having both represented would let users be able to consider costs from other factors that are not dealt with on a daily basis such as rent or grocery costs.
Machine learning algorithms would also be useful in studying the behavioural factors of where the user would spend their money. Within the app, the amount spent at each location would be documented into specific categories such as food, entertainment, groceries, etc. This is important in understanding a user’s habits and to pinpoint the problem areas where a user might not realize is a hindrance to their finances. An example of the benefits of this feature would be if the user were to make a habit of getting a coffee at Starbucks every morning and at the end of each month, accumulating a decent amount towards their food expenses. In the monthly financial summary, the user would realize that the cost of investing in a coffee machine would decrease their overall spending costs, using the additionally saved money towards their savings plan or leave room in their food budget.
In relation to the previous feature, the device could also deter people away from spending at locations that are out of their financial budget by displaying the price range of the establishment based on the user’s location. This is under the assumption that users would be searching for cheaper options and with machine learning algorithms, the device could suggest options better fit for the individual based on their financial standings and their personal preferences.
As an expense tracker, the technology also does not only have to discourage spending. Incorporating positive reinforcement improves the user’s likelihood of continuing with their budgeting plan. While thinking of ways of providing positive reinforcement, I came across the realization that users do not always have to be encouraged to spend less. To improve overall wellbeing and promote altruistic behaviour, users could be encouraged when they spend money on actions that benefit them such as money spent on a gym membership, groceries instead of take-out, and donating to charities. These features might not be welcomed by everyone but by providing people with the option, it would encourage a better lifestyle and provide benefits that go further than just a personal expense tracker.
User-based thinking include aspects outside of the interactive design of the device as it also needs to provide services that would make processes in life easier for the user. In that sense of thinking, I thought of incorporating a function that would easily organize and maintain receipts into categories with each payment. In addition to improving the experience of filing their taxes, this system would reduce the amount of paper waste that machine receipts accumulate. Business expenses have never been easier to track.
From the development of this digital product, what were my findings? A financial tracking bracelet with machine learning technology could be the way to make tracking your expenses easier. The ability to sync personal payment systems directly inputs spending habits that individuals could improve upon for short-term or long-term goals simply, by the integration of an app. Machine learning algorithms are also important aspects to learn spending behaviours and can be used to suggest options that would suit the individual, providing both positive and negative reinforcement depending on the person’s financial choices. In addition to expense tracking, receipt tracking would improve usability of the overall product, opening up a whole new area for the development of this device. The simple but detailed features of this product provide a look into how we are integrating user-oriented design into our digital devices today to better our way of life and showing a peek into a brighter future for our society.
