A system and process for forecasting third party disruption that uses a complex computerized network map as part of a risk model may be used to analyze risk for third parties. A number of risk factors or nodes of the map may include a wide range of risks (e.g., corruption) that have an impact on third parties in a geographic region. A baseline risk level may be established by scoring underlying risk measures for respective geographic regions. By executing the risk model using dynamic signal processing in a near real-time manner and considering impact, velocity, likelihood, and interconnectedness of risk factors and the diffusion of risk across a network, potential third party disruption and/or vulnerability can be forecasted.