The provisions of B.d.I. of 5/12/2018 on governance and control mechanisms for retail banking products introduce the protection of consumers of banking services, through a set of rules governing the life cycle of the product.
There are no specific indications or prescriptions at the regulatory level regarding the criteria to be used to define the reference market, positive and negative. During the entire life cycle of the product, internal procedures must ensure that intermediaries take into due consideration the interests, objectives and characteristics of customers, the typical risks of the products that can cause prejudice for customers, and possible conflicts of interest.
Therefore, the identification of the positive and negative target market remains essential, the classes of customers for which a product can be considered suitable or unsuitable.
Our solution allows the preliminary identification of the set of data wich describe the customer and the products, describing on the one hand risk propensity and degree of financial literacy and on the other the degree of risk and complexity.
The obtained datasets are submitted to clustering, an unsupervised method that allows the dataset to be separated into homogeneous subsets.
In this way the initial pairs (Customer, Product) are remapped into families (Customer Cluster, Product Cluster) that define the target markets. Finally, the target markets are submitted to machine learning methods that identify any outliers, discovering the numerical consistency of off-target customers.
It should be noted that this approach can also be used in a proactive key, referring a new customer to a cluster of customers and proposing the products related to the most appropriate cluster of products.