Kiarash Shamsi

Kiarash Shamsi, Danijel Novokmet, Joshua Peters et al.

Credit risk assessment is essential in the financial sector, but has traditionally depended on costly feature-based models that often fail to utilize all available information in raw credit records. This paper introduces LendNova, the first practical automated end-to-end pipeline for credit risk assessment, designed to utilize all available information in raw credit records by leveraging advanced NLP techniques and language models. LendNova transforms risk modeling by operating directly on raw, jargon-heavy credit bureau text using a language model that learns task-relevant representations without manual feature engineering. By automatically capturing patterns and risk signals embedded in the text, it replaces manual preprocessing steps, reducing costs and improving scalability. Evaluation on real-world data further demonstrates its strong potential in accurate and efficient risk assessment. LendNova establishes a baseline for intelligent credit risk agents, demonstrating the feasibility of language models in this domain. It lays the groundwork for future research toward foundation systems that enable more accurate, adaptable, and automated financial decision-making.

Kiarash Shamsi, Tran Gia Bao Ngo, Razieh Shirzadkhani et al.

Temporal Graph Learning (TGL) has become a robust framework for discovering patterns in dynamic networks and predicting future interactions. While existing research has largely concentrated on learning from individual networks, this study explores the potential of learning from multiple temporal networks and its ability to transfer to unobserved networks. To achieve this, we introduce Temporal Multi-network Training MiNT, a novel pre-training approach that learns from multiple temporal networks. With a novel collection of 84 temporal transaction networks, we pre-train TGL models on up to 64 networks and assess their transferability to 20 unseen networks. Remarkably, MiNT achieves state-of-the-art results in zero-shot inference, surpassing models individually trained on each network. Our findings further demonstrate that increasing the number of pre-training networks significantly improves transfer performance. This work lays the groundwork for developing Temporal Graph Foundation Models, highlighting the significant potential of multi-network pre-training in TGL.

Kiarash Shamsi, Koosha Esmaielzadeh Khorasani, Mohammad Javad Shayegan

Today's world is struggling with the COVID-19 pandemic, as one of the greatest challenges of the 21st century. During the lockdown caused by this disease, many financial losses have been inflicted on people and all industries. One of the fastest ways to save these industries from the COVID-19 is to provide a reliable solution for people's health assessment. In this article, blockchain technology is used to propose a model which provides and validates the health certificates for people who travel or present in society. For this purpose, we take advantage of blockchain features in protecting people's privacy. Since a variety of antibody and human health proving tests against the virus are developing, this study tries simultaneously to design an integrated and secure system to meet the authenticity and accuracy of different people's health certificates for the companies requiring these certifications. In this system, on the one hand, there are qualified laboratories that are responsible for performing standard testing and also providing results to the system controller. Finally, people are considered as the end-user of the system. To provide test information for the entities, the mechanism of KYC tokens will be used based on the Stellar private blockchain network. In this mechanism, the user will buy a certain amount of KYC tokens from the system controller. These tokens are charged in the user's wallet, and the user can send these tokens from his wallet to any destination company, to exchange the encrypted health certificate information. Finally, considering the appropriate platform provided by blockchain technology and the requirement of a reliable and accurate solution for issuing health certificates during the Covid-19 pandemic or any other disease, this article offers a solution to meet the requirements.