A Statistical Approach to Inferring Business Locations Based on Purchase Behavior

07/16/2018
by   Yehezkel S. Resheff, et al.
0

Transaction data obtained by Personal Financial Management (PFM) services from financial institutes such as banks and credit card companies contain a description string from which the merchant, and an encoded store identifier may be parsed. However, the physical location of the purchase is absent from this description. In this paper we present a method designed to recover this valuable spatial information and map merchant and identifier tuples to physical map locations. We begin by constructing a graph of customer sharing between businesses, and based on a small set of known "seed" locations we formulate this task as a maximum likelihood problem based on a model of customer sharing between nearby businesses. We test our method extensively on real world data and provide statistics on the displacement error in many cities.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/23/2018

Identifying Financial Institutions by Transaction Signatures

Financial data aggregators and Personal Financial Management (PFM) servi...
research
09/09/2016

An Integrated Classification Model for Financial Data Mining

Nowadays, financial data analysis is becoming increasingly important in ...
research
05/23/2019

CUDA-Self-Organizing feature map based visual sentiment analysis of bank customer complaints for Analytical CRM

With the widespread use of social media, companies now have access to a ...
research
11/19/2017

Building an Entrepreneurship Data Warehouse

The main principle of the Lean Startup movement is that static business ...
research
02/05/2023

ODEWS: The Overdraft Early Warning System

When a customer overdraws their account and their balance is negative th...
research
12/04/2019

Exploring Multi-Banking Customer-to-Customer Relations in AML Context with Poincaré Embeddings

In the recent years money laundering schemes have grown in complexity an...
research
09/27/2016

Analysis of Massive Heterogeneous Temporal-Spatial Data with 3D Self-Organizing Map and Time Vector

Self-organizing map(SOM) have been widely applied in clustering, this pa...

Please sign up or login with your details

Forgot password? Click here to reset