In this article, we discuss the issues associated with acquiring behavioral and financial data and transforming it into a business decision. We also present a unified modeling approach for combining the information into a credit risk assessment for both small firms and medium-sized enterprises.
This article explores innovative strategies that traditional banks can use in small business lending to remain competitive with alternative lenders.
In this article, we show the mechanisms through which data quality and productivity interact, and how investments in data quality can offer productivity gains.
In this article, we combine financial information with behavioral factors to more accurately assess credit risk for small firms and medium-sized enterprises.
The main barrier to the adoption of business intelligence (BI for short) is making sense of the overwhelming number of options available for boosting performance. Let us get started on the journey to become a data-driven organization and turn data analysis into bottom-line results.
With ever-increasing requirements for a higher quantity and quality of analytical output, the need to boost productivity in risk management has become more acute. In pursuing these productivity gains, we have observed that investments in data quality can offer dramatic improvements and typically pay for themselves.