Atrial fibrillation (AF) is a common cardiac arrhythmia associated with an increased risk of stroke and other complications. Identifying individuals at higher risk of developing AF in the community is now possible using validated predictive models that take into account clinical variables and circulating biomarkers. These models have shown adequate performance in racially and ethnically diverse populations. Similarly, risk stratification schemes predict incidence of ischemic stroke in persons with AF, assisting clinicians and patients in decisions regarding oral anticoagulation use. Complementary schemes have been developed to predict the risk of bleeding in AF patients taking vitamin K antagonists. However, major gaps exist in our ability to predict AF and its complications. Additional research should refine models for AF prediction and determine their value to improve population health and clinical outcomes, advance our ability to predict stroke and other complications in AF patients, and develop predictive models for bleeding events and other adverse effects in patients using non-vitamin K oral anticoagulants. (Circ J 2016; 80: 1061-1066).