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In response to the Fintech trend, an ongoing debate in the banking industry is how to design the new-generation interbank retail payment and settlement system. We propose a two-stage analytical model that takes into account the value-risk tradeoff in the new payment system design, as well as banks' participation incentives and adoption timing decisions. We find that, as the system base value increases, banks tend to synchronize their investment and adoption decisions. When the system base value is low and banks are heterogeneous, bank association ownership maximizes social welfare. When both the system base value and bank heterogeneity are moderate, government mandate leads to the socially optimal solution. When the system base value is high and banks are relatively homogenous, government ownership is socially optimal. We offer important policy implications regarding the optimal system design and the government regulator's role in shaping the banking industry in future financial innovation.

Innovations involving information technology (IT) provide potentially valuable investment opportunities for industry and government organizations. Significant uncertainties are associated with decision-making for IT investment though, a problem that senior executives have been concerned about for a long time. The uncertainties include consumer, market and regulatory responses, IT-driven changes in operational and transactional performance, technology standards and competition, and future market conditions. All these things have an impact on organizations' willingness to adopt. As a result, traditional capital budgeting, investment experience, and intuition have not been very effective in IT investment decision-making. We propose a new option-based stochastic valuation modeling approach for IT investment under uncertainty that incorporates a mean reversion process to capture cost and benefit flow variations over time. We apply the proposed approach in two industry settings: to a largescale IT investment in the consolidation of data marts at a major airline, and to a mobile payment system infrastructure investment on the part of a start-up. The applications supported the evaluation of the proposed methods, and offered some illustrations about the kinds of managerial insights that can be obtained. We also report on several extensions that demonstrate how the creation of useful management findings from the modeling approach can be supplemented with project value sensitivity analysis and the use of simulation-based least-squares Monte Carlo valuation. The findings are useful to assess the power and value of the approach.