A Graph Oriented Based Recommender System for Financial Products and Services

Recommender Systems are intelligent applications designed to assist the user in a decision-making process whereby user wants to choose one item amongst the potentially overwhelming set of alternative products or services. This research is aimed at developing an intelligent recommender system that provides high quality recommendations in the financial domain. Hashed and anonymized datasets (which are account statements) were acquired from online sources and bank customers. The acquired data was pre-processed using the Microsoft Excel 2016 and WEKA 3.8.3 data mining software. The K-nearest neighbor (KNN) algorithm was used to classify the dataset and train the model. The trained model was used to develop a recommender system using the Java 2 platform Enterprise Edition (J2EE). For effective management of the data and consideration of rapid increase in data growth, a graph-oriented database approach was proposed and utilized. The database management system used was the Neo4j. From the evaluation of the algorithms implemented in the recommender system taxonomy, the KNN algorithm recorded the best performance building the model in 0.3seconds with an accuracy of 89.8%. The fuzzy decision tree algorithm performed second best building the model within 0.48 seconds with an accuracy of 62.8%. The decision table algorithm performed poorly building the model in 3.9 seconds with an accuracy of 53%. However, the baseline accuracy of the dataset used was evaluated to be 62.75% of accuracy in 0.4 seconds. It is therefore recommended, as proposed in this study that the graph technology be used in developing recommender systems especially for institutions with massively growing data like the financial institutions. In addition, bank products should be classified and targeted towards customers in order to bolster their level of involvements and improve financial inclusion. With a targeted product, customers will be more willing to opt-in if products are suitable and within financial reach. This will help financial institutions earn more and the customer’s financial power will also be strengthened.