Insights from models based on 30 million data points In the intricate landscape of credit risk assessment, precision holds the [...]
Insights from models based on 30 million data points
In the intricate landscape of credit risk assessment, precision holds the key. When it comes to evaluating Small and Medium-sized Enterprises (SMEs), the challenge becomes even more pronounced. This is why, at Wiserfunding, we’ve adopted a data-centric, proven methodology in crafting our SME risk models.
Key to success: SME-specific data
Constructing a risk model for SMEs hinges on a fundamental principle: the quality of the model is directly tied to the data it relies on. Hence, our focus has been unwaveringly directed towards leveraging data exclusively from SMEs. And not just any data – we’re talking about an extensive dataset, recognising that in the realm of data more is indeed more.
Our journey started in 1998 and spans over two decades. Throughout this time, we’ve diligently amassed financial and alternative data from over 30 million SMEs globally.
This expansive dataset forms the bedrock of our model development, ensuring an unparalleled richness of insights.
Segmented by sector and region for precision
We acknowledge that SMEs are not uniform entities; they exhibit significant variations across sectors and regions. To account for these differences, our models are finely segmented. This segmentation enables us to deliver highly accurate intelligence tailored not only to the general characteristics of an SME but also to its specific contextual nuances.
The culmination is a suite of SME risk models that furnish nuanced, precise, and reliable intelligence. This goes beyond mere data – it’s actionable insight empowering improved decision-making, bespoke strategies, and, ultimately, enhanced success in SME financing.