Over the past decade, the recognition of what AI can bring to a business has risen dramatically across all industries, and banking is no exception. Consequently, we have seen a greater degree of awareness interest in this field, from banks as they work proactively to get an AI advantage from our solutions, in addition to the ideas and services we provide.
Typically, in a banks’ loan process a large percentage of resources and expenditure towards acquiring customers and defining suitable loan conditions for each potential client to step in (take a loan) and deliver sufficient profit. (You can see our other posts to get an idea about such products). Ideally, however, to optimize the customer experience and profitability, this process should continue throughout the whole customer loan lifecycle. Analyzing data on customers’ payment habits and how they evolve over time, serves to indicate certain changes in a customer’s needs. With a Top-up Recommender in place, we predict a customer’s need to apply for a larger loan and thereby proactively make a suitable offer.
A top-up loan is implemented into the current loan, so that the loan amount is raised and maturity time extended. This approach is usually preferable over taking a separate loan, due to lower maintenance costs for one loan as opposed to multiple loans. Despite its name, our Top-up Recommender works in both cases, i.e., for offering additional loans to suitable customers, either as a top-up or a second loan.
In addition to being easily implementable, the Top-up Recommender brings the following quantitative benefits:
1. When a customer decides they need another loan, they may make simultaneous applications at various banks to find the most competitive offer. There is a risk they may even decide to transfer their current loan to another bank. switching to competitors.
2. In addition to avoiding unnecessary costs and losses, a favorable top-up loan will enhance the revenues from the customer, increasing their individual ROI.
As soon as a new customer is acquired, their payment behavior and other available information on the customer is regularly processed (e. g. monthly), to forecast their need for a top-up loan (also referred to as “credit hungriness”). Once a customer is recognized as someone who has a high likelihood of applying for a top-up loan, a multidimensional optimization problem is run with the aim of maximizing the potential extra profits available. The outcomes are:
Based on our experience of deploying AI powered services, we’ve put emphasis on scalability. This eases the onboarding process, so that no matter which internal systems and technologies you utilize, Top-up Recommender can be easily implemented without costly investments.It’s time to start using data to its full potential and thereby save costs, improve the customer experience and achieve your targets. We would be very happy to assist you in this journey.
Learn more about Tietoevry’s data-driven solutions for banking and let’s have a data transformation journey together. Contact us today!
Anton is a senior data scientist currently focusing on financial services at Tietoevry. Having a mathematically-oriented mindset combined with expertise in banking industry, he predominantly aims at creating an effective balance between the complexity and stability of the Tietoevry financial products.