Proactive management of service contract risks ahead of contract signing is becoming increasingly important for IT service providers due to the cost pressure associated with IT outsourcing. Within an end-to-end risk management process, various risk assessments are performed at multiple stages before a service contract is signed. Based on the risk assessment data, service providers seek to have predictive models that indicate risks of future service contracts. Considering the wide range of risk assessments, the variable frequency in which they are conducted, their sequential nature and the prevalent data scale, naïve statistical modeling approaches, such as linear regression, are not readily applicable to such data sets. It is, therefore, necessary to identify a new methodology for predicting service contract risks based on ordinal risk assessment data. In this paper, we describe an analytical methodology that enables optimal risk prediction for service contracts, along with the lessons learned from implementation within an enterprise-level risk management ecosystem. Such real-world insights can provide guidance to data scientists and researchers both in the service delivery domain as well as other domains with similar data characteristics.