Geospatial-based location-aware recommendation of banking services in the world of big data

Recommender system applications in the financial sector, specifically banking, in fast growing cities like Accra are characterized by serious challenges such as the use of stagnant distance-based search, unreliable GPS positioning, unreliable geocoding accuracy, routes oblivious to congestion, and lack of branch-scaled level of service availability verification. For instance, customers can be referred to the closest bank brach that is located geographically, but is not offering the service that is being requested or even not accessible practically based on the traffic. Such restrictions augment the risk of misguiding and inconveniencing customers. This paper proposes a framework of Geo-Banking, which is a geospatial-based, location-aware recommender system that encompasses spatial indexing, route optimization through networks, service validation and location-awareness systems under a single architecture. Geo-Banking utilizes R-tree indexing to provide efficient retrieval of candidates and congestion-aware routing with the shortest paths to optimize travelling time instead of Euclidean distance. The extra validation layers eliminate false positive geographic identifications and enhance reliability of recommendations. Empirical performance in the Accra Metropolitan Area also indicates better accuracy and recall of service similarity, less geographic mislocation and better computation efficiency than the baseline models. Ablation analysis also supports the fact that GIS-based spatial indexing, as well as routing, adds to the performance improvements over the traditional recommender logic. The Geo-Banking framework is therefore a real-time and scalable urban financial service recommendation model that can be used in smart-city implementation.