A Recommendation System for Personal Financial Management via Deep Learning-based Categorization

Nowadays, Personal Financial Management (PFM) is a fundamental part of banking services. It allows clients to get a holistic view of their transactions and overall financial situation, and banks to dynamically collect data about their clients’ movements and mine these data for useful information that would help to build financial profiles, suggest the clients personalized services and/or predict their future needs. The work of this thesis consists of two parts. At first, it is proposed a Deep Learning solution to the Transaction Categorization problem, leveraging a Convolutional Neural Network (CNN). Each transaction contains enough information to allow for an efficient categorization. State-of-the-art technologies and methods of Natural Language Processing (NLP) were used in order to classify clients’ transactions in 7 different spending areas. The CNN model proposed is able to classify with very high accuracy the transactions in the pre-defined areas. Transaction Categorization provides a useful insight to understand where clients spend their money, what are their needs and habits, and what possible banking services/products could be worth to them, thus offering the possibility to create a personalized experience to each customer. In the second part, techniques of clustering and similarity in vector space were used in order to associate items to the users based on their interests, which were analysed by getting statistics from the history of their past categorized transactions. Similar users were grouped in clusters using the K-means algorithm in order to set the path for a collaborative-filtering RS, where the behaviour of a user within the cluster would help to recommend similar users the same services