The stochastic programming scenario optimization using multi-period stochastic linear programming is a very good approach to asset and asset-liability management. This talk discusses the authors experiences in building and implementing large scale ALM models. A comparison of the difficulties and advantages is made and shown to be more useful than other approaches. The basic idea is to generate future scenarios for the uncertain asset returns and liability commitments. Then one optimizes the final wealth risk adjusted given the various constraints on the activities. Shortfalls of deterministic and stochastic targets convex weighted over time where the weights involve type of shortfall and when it might occur over time create the models risk. So the objective is the maximization of a concave function that’s piecewise linear approximated. Successful implemented applications are discussed as the approach progressed over time. These include the Russell Yasuda Kasai and InnoALM models in insurance, pension fund and bank portfolio management, futures trading and other areas. Models for institutions are simpler than models for individuals. Computing is now not a key issue. Most difficult is scenario generation and selling the models.