Debt portfolios are packed with data at various levels: industry data and economic indicators at macro level; typology, changes in management, position in the supply chain, diversity of activities at creditor level (the company itself); current contracts, revenue data, degree of concentration, credit limits and credit insurance, consolidation issues and creditor positions at debtor level; credit amounts, inconsistent invoice numbers, number sequences, descriptions, codings at invoice level.
Databases, models and historical data make it possible to efficiently and effectively arrange and interpret data in a meaningful way. Questions may arise in specific sub-areas which can be resolved through algorithmic investigation. Mirus is continuously perfecting these tools to meet the market’s changing needs in order to generate relevant insights, insofar as technically possible. Hands-on debt collection generates data that is used as input for standard setting and this practice ensures that standard setting is constantly adjusted.