In real life, the phenomenon of delayed payment of invoices often occurs. Malicious delayed payment may even lead to the break of capital flow of companies. Debtor payment prediction according to the historical payment behaviour of debtors can help give companies more insight into the risk of late payment. The debtor payment prediction is important for financial risk analysis because companies can better manage their cash flow to avoid the risk of capital chain breakage if they know when the invoice they present will be paid in advance. However, real-world data is far from perfect. With the inflow of various free-form data streams, it is challenging to integrate invoice payment records regarding debtors' historical payment behavior. As a result, the debtor payment prediction model sometimes performs sub-optimal because sufficient historical payment data cannot be retrieved for debtors. To improve debtor payment predictions, the data sparseness problem is studied to be addressed through entity resolution technology. Instead of fine-tuning a more complex model or adding new financial features, the prediction performance is studied to be improved by mining the information hidden in the data. More specifically, an end-to-end entity resolution workflow is proposed for invoice payment data to match debtors representing the same entity so that more historical payment data is available for the debtor that is known to be matched with other debtors. With the incorporation of entity resolution, the debtor payment prediction is verified to be improved in this project.