The use of Large Language Models (LLMs) such as OpenAI ChatGPT to enhance teachers' and learners' experience has become established. The impressive capabilities of ChatGPT in solving Text2SQL problems prompts their use in database courses to solve SQL exercises. In this paper, we dig deep into ChatGPT abilities applied to SQL exercises. We quantitatively and qualitatively evaluate the performance of a ChatGPT-as-a-SQL-assistant on benchmark data, with particular attention paid to its ability to correctly detect syntactic and semantic errors, provide insightful judgment explanations, and assign grades comparable to those of human teachers. Furthermore, we also analyze the benefits of leveraging few-shot learning to adapt LLM responses to the expectation.

Abstract Driving the decisions of stock market investors is among the most challenging financial research problems. Markowitz’s approach to portfolio selection models stock profitability and risk level through a mean–variance model, which involves estimating a very large number of parameters. In addition to requiring considerable computational effort, this raises serious concerns about the reliability of the model in real-world scenarios. This paper presents a hybrid approach that combines itemset extraction with portfolio selection. We propose to adapt Markowitz’s model logic to deal with sets of candidate portfolios rather than with single stocks. We overcome some of the known issues of the Markovitz model as follows: (i) Complexity: we reduce the model complexity, in terms of parameter estimation, by studying the interactions among stocks within a shortlist of candidate stock portfolios previously selected by an itemset mining algorithm. (ii) Portfolio-level constraints: we not only perform stock-level selection, but also support the enforcement of arbitrary constraints at the portfolio level, including the properties of diversification and the fundamental indicators. (iii) Usability: we simplify the decision-maker’s work by proposing a decision support system that enables flexible use of domain knowledge and human-in-the-loop feedback. The experimental results, achieved on the US stock market, confirm the proposed approach’s flexibility, effectiveness, and scalability.

Transaction Monitoring is one of the main labor-intensive tasks of anti-financial crime and it requires to scrutinise billions of transactions per month against possible crimes. The first step in the process is the correct identification of the involved parties. This foundational step defines the focal entities on which transaction monitoring algorithms rely to spot suspicious events. Unfortunately, the loose syntax of protocols and the free text fields of inter-banking communications make party disambiguation particularly challenging. The first step of a fully automated data-driven strategy is thus the detection of the actual entity owning or using a given account.In this paper, we leverage data-driven techniques to identify and disambiguate the owners of accounts involved in cross-border international transactions when a Financial Institution only knows a minority fraction of such parties as its own customers. For this, we propose a data science pipeline relying on hierarchical clustering to capture similarities among names of parties involved in actual transactions. We test and tune the proposed approach using a large, real-world, multi-language, proprietary dataset of actual international transactions. Our highly parallel implementation completes the identification of parties that share an account and identifies all accounts owned by a party with f-score higher than 0.8.

Quantifying the effects of adverse weather conditions on road traffic is crucial for several reasons, among which monitoring traffic safety, managing vehicle fleets, and ensuring autonomous vehicle security and performance. This paper studies the incidence of adverse weather effects on road traffic safety. The aim is to assign a unified risk level to all spatial regions sharing the same context (e.g., the highly populated areas), which reflects the impact of adverse weather events on road accident occurrences. The proposed approach relies on an incomplete set of historical accident data, which report weather-related accident occurrences in specific risky areas. To estimate the percontext risk level we propose to analyze the weather element measurements acquired by meteorological stations spread over the analyzed area. The series of adverse weather events are embedded into a high-dimensional embedding space to enable the identification of temporal event patterns similar to those observed in risky areas. The experimental results show the effectiveness of the proposed approach in a real case study.