Innovation risk in banks, a formalized instrument that is part of banks’ financial and innovative strategies, influences the assessment of innovative activity, demonstrating the importance of forecasting and assessment models of potential innovation risks. Our research into general scientific and specific methods allowed us to: (1) distinguish hierarchical concepts and their order—namely, “banking innovation”, “economic effects of innovational activities”, “financial and innovative strategy”, and “innovation risk”; (2) identify links between innovative and strategic bank management, since bank innovations are carried out in conjunction with strategies and imply positive strategic economic effects, making the assessment of potential innovation risk necessary for the current moment and the future; (3) note that the launching and use of new technologies on economic cycles and phases involving a necessary correlation between innovative profit and these phases; (4) provide preferable measurements of banks’ innovative activity and financial performance against commission income; (5) assess the potential financial performance of banks’ financial and innovative strategies within economic cycles and phases and in accordance with the nature of income; (6) present general areas for the practical application of an adapted data mining–Monte Carlo method, based on a proprietary software product. The model’s application in the “open innovation” system exhibits its multipurpose nature and allows for the selection of alternative strategic innovative solutions within economic cycle phases. It also serves in the promotion of Big Data technology in relation to finance and innovation, which is a promising area, and determines the values of the desired indicators for the “bank of the future” concept.

中文翻译：

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“开放式创新”系统中基于数据挖掘-蒙特卡罗的银行潜在战略创新风险评估模型的开发与验证

银行创新风险是银行金融和创新战略一部分的正式工具，它影响创新活动的评估，证明了潜在创新风险预测和评估模型的重要性。我们对一般科学方法和具体方法的研究使我们能够：（1）区分层次概念及其顺序——即“银行创新”、“创新活动的经济效应”、“金融和创新战略”和“创新风险”；(2) 确定创新和战略银行管理之间的联系，因为银行创新是与战略相结合的，意味着积极的战略经济效应，因此对当前和未来的潜在创新风险进行评估是必要的；(3) 注意到新技术的推出和使用在经济周期和阶段涉及创新利润与这些阶段之间的必要关联；(4) 根据佣金收入对银行的创新活动和财务业绩提供更好的衡量标准；（五）根据收入的性质，在经济周期和阶段，评估银行的财务和创新战略的潜在财务绩效；(6) 介绍基于专有软件产品的自适应数据挖掘 - 蒙特卡罗方法的实际应用的一般领域。该模型在“开放式创新”系统中的应用展示了其多用途性质，并允许在经济周期阶段选择替代性战略创新解决方案。