Recommender Systems and Banks: Precious Recommendation

Recommender Systems and Banks: Precious Recommendation

The client’s path, from conceiving an idea to making it a project in the bank, used to be clear, but unpredictable. It involved a potentially noticed ad or the client’s own idea, a visit to the bank counter in person and the more or less successful deal with the bank. It was very time consuming, with very little control from the bank, and even less efficiency. It was very hard to accept this method in an industry that is proud of the motto “time is money”.

Online communication of a bank and a client seems even more chaotic at first sight. Random clicks on the websites in the search for the information, answers to the key questions, wandering around, looking for the needed service.
However, things do not have to look that way. Human behaviour in attempts to communicate with banks is usually all but chaotic.

First Step – Creating a System

The best cure for chaos is – implementing the order. In the online communication between clients and banks, that means identifying the options to get the best possible outputs from the system, relying on the inputs.
“Recommender Systems are used to provide the best recommendation of our product that would interest the client most (system output), based on the user data (system input)”, Things Solver expert for development and implementation of Recommender Systems Strahinja Demic explains, using the company’s definition.

These systems can be classified according to the system input, system output or according to the algorithms that operate in the background and create the recommendation. First two classifications are created in Things Solver and are based on the practical experience.
“In the classification based on system input, we can identify inputs from online (user visits to the website, for example), inputs from offline (user data kept in the bank’s database), or the mixture of both kinds of data. In the classification based on the system output, the recommender can propose products the user already experienced or those that he might be interested at, but still does not have them; or the mixture of both kinds”, Demic describes the systems.

The New Approach for the Banks

Recommender Systems made by Things Solver make their way into the banking systems through the online sessions data.
“An online session is defined as a client’s visit to the website and its entire activity on the website – for example, the path through the website, time spent at certain pages, choice of links. Based on that data, we try to describe what client actually wants – the client’s visit to the webpage for loans or his attempt to make a calculation can send us a signal that there is some interest for taking a loan”, Demic explains.

The outcome of the process is a package of five products the client analysed and five products he might be interested at, but had no direct experience with it so far – only the algorithm noticed the potential interest for the product.
“We can notice the interest even before they show it at one of the banks for at least 10 percent of clients, and that is not a small number. The percentage would grow if those clients would be contacted in order to maintain their interest”, and this is the moment when, as Demic explains, the managerial structures of banks became more interested for the Recommender Systems.

But this results do not make only the management happy. The other employees are satisfied as well – without many changes in the procedures, since people rarely welcome big changes, the results and efficiency are improved. This recommendation then definitely deserves the adjective “precious” in its description.