There is a submovement within the world of artificial intelligence that holds a human-centric approach at its centre. Given that AI has an increasing ability to guide customers down a specific journey, it has become particularly relevant in enhancing customer financial wellness.
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’.
Immunising financial wellness against volatility
The case for financial wellness has customer lifetime value at its core, with the primary goal of long term, profitable gains. However, wellness is not a static construct, and is inherently relative to current context.
This requires a more nuanced approach to measurement, facilitated by mechanisms such as frequent experimentation, model redeployment and continuous scoring. Alongside this, mechanisms are required for analysing customer interactions for signs of changing behavior and context. This dynamism is imperative to stretching analysis beyond the traditional statistical approaches to client behaviour, viewing history as quantitative and fixed, and the future as qualitative and limitless.
Financial Wellness strategies must accommodate the volatile nature of the human condition. This poses a challenge for businesses with inflexible technology, but creates immense opportunity and competitive advantage for those with the right machine learning and AI capabilities.
Bypassing bias with automation
An understanding of shifting behaviours becomes vital for establishing trends of financial wellness, or lack thereof, but measuring this requires systems that can approach quantitative and qualitative problems with equal fervour. 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 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, it is 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 avoid potential for human bias in surveys, which provide a view of customer stability from their perspective, and use transactional data instead. 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, you can acquire a truer representation of that customer’s financial situation.
Many birds, one platform
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.
