Keeping a steady cash flow is one of the biggest if not the biggest problem that Small to Medium Enterprises (SMEs) deal with daily. Within the different types of cash flow, Accounts Receivable (AR) classifies the balance of money that needs to be paid by the company's customers. In the most typical case, after receiving goods or services, the customer receives an invoice with the amount that is owed to the supplier. However, this often does not happen before the aforementioned date, meaning that the invoice is often paid late. Intervention requires resources and over-intervention could cause unwanted customer dissatisfaction. Knowing whether an invoice is going to be paid late can be vital information. Current methods of late payment prediction focus only on the history between the seller and the buyer and are unusable when this history is not present. Intuitively, one's business depends on the relationships and transactions that it has with its neighbors. Suggesting that neighbor behavior could be useful when predicting the cash flow of a company. Unfortunately, this type of information is not always given and needs to be data mining from non-relational data. This work presents a method for building a relational network of SMEs using entity resolution and improving the current state of the art of late payment prediction using features extracted from the graph.