In the world of artificial intelligence, many shifts have taken place to allow for a more human centric approach to business. One such shift is financial wellness. This article introduces the concept of financial wellness and the practical implementation of using machine learning and AI to give customers a financial wellness score. There are further considerations regarding the concept, including why, despite it being an extremely relevant topic, it has not yet been implemented globally. 

Financial wellness is defined in a number of ways; the most familiar of which refers to an individual’s overall financial health. It is often described as a position of  ‘being prepared for an emergency’, ‘meeting financial goals’ and ‘budgeting spends’, amongst others.

The case for financial wellness in business is a problem that many big companies are currently attempting to solve. Some remedies include taking well-known concepts and altering elements to change focus, while others involve the capabilities afforded by machine learning. Because wellness is not a static construct, it requires a more nuanced approach to measuring. There is a need for frequent experimentation, model redeployment and constantly updated scoring. Alongside this, there needs to be a space in which customer interactions can be considered, to reveal whether contexts are changing. This dynamism is imperative to stretching analysis beyond the traditional statistical approaches to client behaviour. History is quantitative and fixed, the future is qualitative and limitless.

Financial Wellness poses a multidimensional problem, for which there are a number of proposed approaches to solving.  Factors must be in place that allows for the altering states of the human condition to be identified. This makes for a very complicated machine learning and AI landscape. While typically approached in a quantitative manner, there are many qualitative properties to consider when using an entirely automated solution. Principles of changing behaviours can be derived from historical data. It is possible to ascertain these patterns based on constructs already in existence within the field of Computational Social Science. The technological understanding of shifting behaviours becomes vital to establishing trends of financial wellness, or lack thereof.

The ecosystem.Ai approach to this process involves a continuous cycle of learning and updating customer profiles at the exact moment changes happen. A cold start intervention begins the process of testing, and as more information is gathered, its fed back into the system in real-time, and used to provide useful motivations for the customer to continue to improve. One approach to encouraging a drive towards wellness includes offering a view of a customer’s ideal self, and further offering solutions and steps that can be taken towards becoming that self. 

One of the best financial wellness breakdowns involves creating four pillars upon which to balance a customer’s financial health. The areas of focus are Spend, Save, Borrow and Plan. The ecosystem.Ai approach is to bypass traditional survey questions, which provide a view of customer stability from their perspective, and attempt to avoid the human bias using transactional data. This can be achieved a number of ways depending on the institution, and then combined with common pillar measures, which provides an overall financial wellness measure. By not relying on the customer’s unintentionally biased feedback, and automating the process, a truer representation of that customer’s financial situation becomes clear.

Financial wellness is a topic of our time. With the capabilities afforded by machine learning, the resolution of which can lead to solutions for other business and customer problems. The current trends in favour of customer-centric approaches to business, mean that artificial intelligence has taken precedence. Using ecosystem.Ai products allow for the utilisation of machine learning functionalities without the complicated use of multiple disparate products. A single interface from which to build out the features needed to create feature stores, even if there are many of them in constant use. As well as constructing models to productionize, deploy and test using various feature store and algorithm combinations. The hypothesis relies on companies having the ability to continuously experiment and utilize models that fit their wellness constructs. Customer financial wellness is achievable, using machine learning and behavioural predictions. It’s now up to companies to make the investment in their customers’ financial wellness.

  • Jessica Nicole