8MAY 2025By Ioannis Michopoulos, Director - Complex Securities & Financial Instruments, StoutTHE REVOLUTIONARY IMPACT OF AI IN THE VALUATION ADVISORY SPACEThe role of Artificial Intelligence (AI) in the modern valuation advisory space has been pivotal and instrumental in reshaping the operational excellence, strategic thinking, and applied thought leadership focus of market participants as well as the nature of market dynamics. AI has shed light on unsolved business problems, incomplete computing capabilities, resource inefficiencies, scalability issues, and organic inter-company integration challenges. AI has also provided valuation practitioners, appraisers, consultants, and financial executives with access to powerful and dynamic models and real-time information that have transformed the chessboard of business intelligence and valuation accuracy. While there is still a long road ahead in terms of the development of AI and its integration into everyday business life, the evolution of machine learning (ML), natural language processing (NLP), and robotics have revolutionized the valuation advisory space in many ways, the most critical of which are described below.Data Structure and Advanced Data Analytics: Valuation appraisers now have access to a "treasury" of data through advanced machine learning, supervised and unsupervised deep learning, and advanced data analytics techniques. Data that has been traditionally extracted through manual intervention (i.e., information from financial statements, market intelligence on customer products, online business due-diligence, historical pricing indications, etc.) can now be extracted with advanced ML and NLP techniques and summarized into powerful business reports. Additionally, data analysis techniques like clustering, principal component analysis, artificial neural networks, decision trees, and random forests boost the quality of data and the statistical significance of the data available for valuation and financial analysis purposes. · Advanced Predictive Analytics, Computation Techniques, and Cloud ScalabilityThe development of supervised and unsupervised deep learning techniques has exponentially increased the predictive accuracy of forecasting models and the robustness of computationally intensive techniques (i.e., improved credit rating models, advanced sentimental analysis, dynamic pricing techniques, dynamic market segmentation analysis, more sophisticated valuation models related to FDA drug applications, etc.). Additionally, the development of data visualization techniques (i.e., parallel coordinates, scatter plot matrix, kernel density estimation, network diagrams and Box & Whisker plots, etc.) has played a fundamental role in better understanding of the nature of data, the processing of inputs in generating more customized and advanced outputs, and the minimization of the standard error in valuation models. Furthermore, the focus on the development of scalable cloud-based technology infrastructure had yielded fruitful results since there are now many products available that provide a variety of data analytics that cover a broad set of variables and business needs and are aligned with the digital strategy of the underlying companies. · Optimization and Automation One of the fundamental principles of the AI revolution wave was the automation and optimization of large, repetitive procedures that involve a considerable amount of manual human intervention. During the last decade, there has been significant growth in this area, and valuation advisory firms have been consistently focused on the competitive advantages obtained by the automation of processes/procedures in order to develop more sustainable and profitable operational frameworks, advanced scale economies as well as boost their market position OPINIONIN MYIoannis Michopoulos
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