This monograph, as entitled, defines and describes the research field at the interface of Finance, Operations, and Risk Management (iFORM), provides examples where operations and finance overlap in meaningful ways, outlines promising research directions, and reduces the entry cost for anyone who would like to explore this new and exciting research field. The intended audience for this article includes both PhD students in operations management (OM), finance, and economics, who are looking for dissertation topics, and experienced researchers looking for novel applications of their expertise. The following outlines the rest of this article. Chapter 2 compares perspectives of finance and operations on the same topic: the firm. This motivates the key questions in finance, which is presented in the finance primer in chapter 3 and key questions in OM, which is presented in the OM primer in chapter 4. Having discussed key ideas from these disciplines separately, chapter 5 examines how OM and finance intersect in meaningful ways and suggest several promising research directions. Chapter 6 presents a "dos and don'ts list for publishing and reviewing iFORM papers.

This book examines the challenges and opportunities arising from an assortment of technologies as they relate to Operations Management and Finance. The book contains primers on operations, finance, and their interface. After that, each section contains chapters in the categories of theory, applications, case studies, and teaching resources. These technologies and business models include Big Data and Analytics, Artificial Intelligence, Machine Learning, Blockchain, IoT, 3D printing, sharing platforms, crowdfunding, and crowdsourcing. The balance between theory, applications, and teaching materials make this book an interesting read for academics and practitioners in operations and finance who are curious about the role of new technologies. The book is an attractive choice for PhD-level courses and for self-study.

Competitive advantage is not achieved by a single company, but rather by the entire supply chain, with a focus on improving the flow of information, finance and goods. Improving these flows requires strategic investments relevant to the supply chain that are often long-term, capital-intensive, relationship-specific, and therefore risky. The past financial or COVID-19 crisis has made banks more risk averse, making it more expensive for supply chain members to finance risky investments or even more difficult to raise funds from external financial institutions. Supply chain finance is a young discipline that focuses on improving financial flows through a collaborative approach. Since most strategic investments are made by suppliers facing financial constraints, there is a high likelihood that supply chain disruptions will occur because the financing party becomes insolvent, or the investments do not materialize. To encourage strategic investments and avoid supply chain disruptions, a model is developed in which supply chain members leverage their financial and operational capabilities to realize these investments and share costs, risks, and benefits according to their contribution. The proposed model is based on a structured coalition game with transferable utility that determines the supply chain members best able to finance the investments without compromising supply chain stability.

This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

This book focuses on identifying and explaining the key determinants of scenario analysis in the context of operational risk, stress testing and systemic risk, as well as management and planning. Each chapter presents alternative solutions to perform reliable scenario analysis. The author also provides technical notes and describes applications and key characteristics for each of the solutions. In addition, the book includes a section to help practitioners interpret the results and adjust them to real-life management activities. Methodologies, including those derived from consensus strategies, extreme value theory, Bayesian networks, Neural networks, Fault Trees, frequentist statistics and data mining are introduced in such a way as to make them understandable to readers without a quantitative background. Particular emphasis is given to the added value of the implementation of these methodologies.