RISK ASSESSMENT WITH PRESCRIPTIVE RECOMMENDATIONS

Transaction, customer, employee, security, and sales forecast data are obtained for a store. Known actions to prevent shrink events are maintained. Transactions associated with shrink events are identified and features are derived. The data is labeled and used to train a machinelearning model to produce, as output, scores for the features, combinations of the features, and prescriptive action identifiers. Each score represents a likelihood of shrink for a given feature or a given combination of features. The output scores and prescriptive action identifiers are predicted at intervals over a period of future time. At each interval, the output for remaining intervals is updated based on real-time store data generated for transactions at the store in a previous interval. An action identifier can be provided to a security application causing the application to increase sensitivity of security detection on a terminal based on the predicted likelihood of shrink.