Mapping AI and Data-Driven Research in Financial and Risk Analytics Using LDA and HJ-Biplot: A Bibliometric-Computational Framework

Lois Soto, Sofia Romero, Rick Acosta-Vega

Abstract

The rapid convergence of artificial intelligence, machine learning, and financial and risk analytics has produced a large and fragmented body of scientific literature, making it difficult to identify its thematic structure and evolutionary trends. This study addresses that gap by proposing a bibliometric-computational framework that integrates Latent Dirichlet Allocation (LDA) and the HJ-Biplot to map research at the intersection of artificial intelligence and data-driven methods with financial and risk analytics. Bibliographic records were retrieved from Scopus and Web of Science, yielding a corpus of 20,213 documents published between 1999 and 2025 from 4640 sources. After preprocessing, LDA was applied to extract latent topics, and the HJ-Biplot was used to represent the relationships among topics, years, countries, and scientific sources within a multivariate framework. The number of topics was selected through a grid search over every integer value of K between 5 and 40, using probabilistic topic coherence together with manual inspection of topic separability. The analysis identified 36 latent topics (K = 36) spanning portfolio optimization, risk assessment, fintech, and deep learning-based forecasting, and shows a rapid post-2010 expansion in the topics concerned with artificial intelligence and predictive analytics, with output concentrated in China, the United States, and India. Increasing, declining, and fluctuating topics were distinguished by the sign and statistical significance of a linear trend in annual topic prevalence. By combining probabilistic topic modeling with multivariate biplot representation, this study offers a more granular and data-driven alternative to conventional bibliometric counting methods, providing a replicable framework for tracking the evolution of interdisciplinary research fields. The design is descriptive: it characterizes the structure of the indexed literature and does not measure financial practice or establish the causes of the patterns reported.

Source: semanticscholar · PDF

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