This paper investigates the application of Machine Learning for credit risk assessment in Multichain Decentralized Finance (DeFi). With DeFi expanding its scope, the need for effective credit risk evaluation becomes paramount. Our study utilizes a diverse dataset gathered from multiple blockchains, including Ethereum, and employs rigorous data preprocessing techniques. DeFi-specific features are extracted, capturing transaction-related statistics. Machine learning models, such as Logistic Regression, Random Forest, XGBoost, CatBoost, LightGBM and a CNN, are deployed to predict wallet liquidations. Evaluation metrics, including accuracy, ROC curve and Area Under the Curve, demonstrate the efficacy of DeFi-related features in credit risk assessment. Furthermore, we analyze feature importance and inter-feature correlations, providing insights into critical risk factors within the DeFi ecosystem. This research contributes valuable insights to the DeFi landscape, offering data-driven approaches to credit risk management and investment strategies. Our findings hold significance for DeFi stakeholders seeking to navigate the evolving financial frontier while mitigating credit risk effectively.

Smart contracts across Blockchains provide an ecosystem of decentralized finance (DeFi), with a total locked value which had exceeded 160B USD. While DeFi comes with high rewards, it also carries plenty of risks. Many financial crimes have occurred over the years making the early detection of malicious activity an issue of high priority. The proposed framework introduces an effective method for extracting a set of features from different chains, and it is evaluated over an extensive dataset with the transactions of the 23 most widely used DeFi protocols based on a novel dataset in collaboration with Covalent. Different Machine Learning methods were employed, such as a Deep Neural Network, XGBoost, and a fine-tuned Large Language Model for identifying fraud accounts interacting with DeFi and we demonstrate that the introduction of novel DeFi-related features, significantly improves the evaluation results.

The paper presents an innovative Internet of Things architecture for building personalized services in the smart city context. The main blocks of the presented implementation comprise data flows implemented through Node-Red, Neo4j data store for handling the smart city big data and a recommendation service which is applied in order to offer personalized recommendations to the users. The current work studies integration of the various components, the modelling approach for user generated data combined with open big data and proceeds with the appropriate reference implementation and experimentation to validate the personalized recommendation services for innovative citizen-centric applications and use cases. Moreover, we study and validate performance issues of this Neo4j based recommendation service and evaluate it as a useful appliance for real-time big data application.