Why? If you consider the whole lending process – from application to full loan repayment – the entire lending lifecycle can be optimized by utilizing data science and AI. Win rates, approval rates, credit losses, customer retention, and churn – all these can be modeled to deliver ROI.
Impacting the lending process
A bank has three main ways to affect the lending process: sales and marketing strategy, credit risk appetite, and pricing. AI enables you to find combinations of and trade-offs between them to maximize the profit and return on allocated capital. Business-wise, that means that instead of simply minimizing credit losses or maximizing sales, a bank should concentrate on profit maximization.
It’s simple economics, the higher the interest rate, the higher the potential revenue. However, with that comes a lower win rate and higher churn, which leads to obvious repercussions. The same applies to credit risk appetite. The tighter the credit policy, the smaller the credit losses, but at the same time sales suffer due, in part, to low credit approval rates. In the language of mathematics, the bank faces a multidimensional optimization problem with several variables affecting each other. This poses an enormous challenge for human beings but presents a vast opportunity for AI.
Automating the credit lifecycle
Here at Credit Solution and Services, we develop software platforms that automate credit processes throughout the entire credit lifecycle. But that’s just one side of the coin – the other is supporting our clients in their daily operations, with strategic decision-making and general improvements to their operational efficiency.
That’s why we’re meeting lending-related challenges through the creation of Intelligent products with embedded AI capabilities. This will not only provide companies with all the analytics necessary to optimize their businesses but also make it possible to translate business decisions into immediate actions.
J.P Morgan famously said “lending is not about money and property, it is about character” and this is how we operate – we try to understand the end customers’ character and predict their future behavior. The solution gains an insight overview of the banks’ data assets, and by using machine learning methodology, estimates the likelihood of various events happening during the lending lifecycle before summarizes them, without compromising the data.
In other words, our AI solution goes through the whole loan lifecycle – from loan origination to loan closure in order to estimate what is an expected revenue stream. Based on this, it provides knowledge-based recommendations to enhance your profitability.
Essentially, this means that a bank can utilize AI to guide them when determining: what is the optimal credit risk appetite, what is an adequate pricing policy and how to best control customer acquisition costs. It also broadens a bank’s capabilities to develop personalized offerings and customized service models.
If you want to make your history a competitive advantage, intensify portfolio profitability or just hear more about our AI-based solutions, please do not hesitate to contact us. Let’s have a data transformation journey together!
Aleksandr leads the data science domain development in Financial Services. He has worked in several international banks within risk management and business development, with a strong track record of business growth and intensifying corporate performance in Nordics, Baltics, and Poland.