Risk Management

New submissions

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New submissions for Fri, 7 Jun 24

[1]  arXiv:2406.03500 [pdf, ps, other]

Title: Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves

Authors: David Curto, Fernando Acebes, Jose M Gonzalez-Varona, David Poza

Comments: 15 pages

Journal-ref: International Journal of Production Economics 253, 108626 2022

Subjects: Applications (stat.AP); Risk Management (q-fin.RM)

In construction projects, contingency reserves have traditionally been estimated based on a percentage of the total project cost, which is arbitrary and, thus, unreliable in practical cases. Monte Carlo simulation provides a more reliable estimation. However, works on this topic have focused exclusively on the effects of aleatoric uncertainty, but ignored the impacts of other uncertainty types. In this paper, we present a method to quantitatively determine project cost contingency reserves based on Monte Carlo Simulation that considers the impact of not only aleatoric uncertainty, but also of the effects of other uncertainty kinds (stochastic, epistemic) on the total project cost. The proposed method has been validated with a real-case construction project in Spain. The obtained results demonstrate that the approach will be helpful for construction Project Managers because the obtained cost contingency reserves are consistent with the actual uncertainty type that affects the risks identified in their projects.

[2]  arXiv:2406.03614 [pdf, ps, other]

Title: Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs

Authors: Alexander Bakumenko (1), Kateřina Hlaváčková-Schindler (2), Claudia Plant (2), Nina C. Hubig (1) ((1) Clemson University, USA, (2) University of Vienna, Austria)

Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL); Risk Management (q-fin.RM)

Detecting anomalies in general ledger data is of utmost importance to ensure trustworthiness of financial records. Financial audits increasingly rely on machine learning (ML) algorithms to identify irregular or potentially fraudulent journal entries, each characterized by a varying number of transactions. In machine learning, heterogeneity in feature dimensions adds significant complexity to data analysis. In this paper, we introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. To encode non-semantic categorical data from real-world financial records, we tested 3 pre-trained general purpose sentence-transformer models. For the downstream classification task, we implemented and evaluated 5 optimized ML models including Logistic Regression, Random Forest, Gradient Boosting Machines, Support Vector Machines, and Neural Networks. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines, in selected settings even by a large margin. The findings further underscore the effectiveness of LLMs in enhancing anomaly detection in financial journal entries, particularly by tackling feature sparsity. We discuss a promising perspective on using LLM embeddings for non-semantic data in the financial context and beyond.

Replacements for Fri, 7 Jun 24

[3]  arXiv:2209.12222 (replaced) [pdf, other]

Title: Efficient Wrong-Way Risk Modelling for Funding Valuation Adjustments

Authors: T. van der Zwaard, L.A. Grzelak, C.W. Oosterlee

Subjects: Computational Finance (q-fin.CP); Mathematical Finance (q-fin.MF); Risk Management (q-fin.RM)

[4]  arXiv:2310.11293 (replaced) [pdf, ps, other]

Title: On the use of artificial intelligence in financial regulations and the impact on financial stability

Authors: Jon Danielsson, Andreas Uthemann

Comments: 35 pages, 1 table

Subjects: General Economics (econ.GN); Risk Management (q-fin.RM)

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