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Predicting financial trends using text mining and NLP

  • Host institution: The Babes-Bolyai University, Romania (BBU)

  • Starting month: M6

  • Duration: 36 months

  • Pillar 1: Introduction to Blockchain applications in finance (Humboldt University of Berlin, 4 ECTs), Work Package 1

  • Work Packages: WP1, WP6, WP7, WP8


The main goal of this doctoral candidate is to enhance the application of AI-driven natural language processing (NLP) techniques for forecasting credit risk and detecting financial fraud. This involves analyzing speech text from audit reports, social media, and various other sources. The focus is on predicting non-compliance by interpreting the sentiment in free-text answers from survey participants. Additionally, the project aims to develop attitudinal indices derived from free text and integrate them into behavioral models. These models, which also consider other qualitative and quantitative elements, will help in assessing the probability of system fraud and determining the risk level associated with accreditation processes.

Expected Results

Constructing large databases that provide both qualitative and quantitative data for use in the development of AI algorithms for both public and private entities for the prediction of tax fraud in banks, FinTechs offering credit services, or others; Using text mining and NLP to evaluate the viability of various models that could predict the risk of fraudulent behaviour in the financial sector; Utilisation of these models in both the public sector (public policy formulation) and the private sector (help banks and FinTechs in credit scoring);

Planned Secondments

  • Raiffeisen Bank International (RAI), Dr. Stefan Theußl, M12, 18 months, research exposure in a global business environment, trend modelling

  • European Central Bank (ECB), Dr. Lukasz Kubicki, M33, 4 months, exposure to globally leading central bank, research training on EU principles, supervision

Planned Timetable

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