CRAFT Research

Blockchain Interoperability for Business Organizations

Business operations that involve multiple parties and organizations face challenges such as a lack of transparency, inefficiency, and disputes. These challenges can significantly impact the smooth functioning of business operations and increase the risk of financial losses. Blockchain technology has the potential to address these challenges by enabling secure and transparent transactions through smart contracts. However, business organizations have many legacy components and must interface with other organizations that use disparate systems, including various blockchain implementations. However, blockchain interoperability solutions are far from ideal and have recently been subjected to numerous attacks.

Our research will investigate risk-aware blockchain interoperability with a business operations lens. The findings will inform the development of new solutions that are streamlined for secure multi-party transactions within and between organizations. Informed by our previous CRAFT project findings, we will focus on several business-specific use cases that would enable multiple business entities to engage in multi-party transactions, encode their service-level agreements and business logic into smart contracts, and provide a mechanism to resolve any issues if disagreements occur later. We will demonstrate the efficacy of our proposed system using several use cases informed by CRAFT IAB members.

PI - Oshani Seneviratne 

CoPI - Aparna Gupta       

Team Member - Inwon Kang

Comprehensive Financial Disclosure Lexicon

The vast amounts of narrative disclosure – whether mandated by accounting regulations or voluntarily disclosed by firms’ management – creates a demand for natural language processing (NLP) in accounting research. In general, researchers have applied either lexicon-based approaches or machine learning approaches.[1] While machine learning approaches are better tools for prediction and are more appropriate in certain contexts (especially where researchers lack ex ante business knowledge), the lexicon-based approaches have the advantages of greater transparency and replicability. Some research combines the approaches, for example, by utilizing machine-learning based approaches to create a prediction and then “reverse-engineering” the outcome to create or expand dictionaries relevant to the specific domain of disclosure.

This project will study the feasibility and potential benefits of a website with a comprehensive repository of the wordlists (aka dictionaries) that have been developed for specific NLP analyses of text-based information relevant to financial markets. The website could serve as a resource for researchers involved in NLP applications for text-based information.

[1] We define lexicon-based approaches as those that treat a textual disclosure as a bag of words and apply dictionaries to capture content. Bochkay et al. (2022) group NLP models into four categories: (i) simple transformations, (ii) text comparisons, (iii) traditional machine learning, and (iv) deep learning.

PI - Elaine Henry                              

CoPI - Jing Chen, Joon Ho Kong  

Team Member - Arion Cheong

Efficient, Private, and Explainable Federated Learning for Financial Crime Detection

We propose to develop a resource-efficient federated learning solution for financial crime detection that preserves data privacy, both during training and inference. We will validate the performance and privacy guarantees of our method through formal analysis and experimental evaluation on datasets from the Privacy-Enhancing Technologies Prize Challenge on Transforming Financial Crime Prevention, co-sponsored by the National Institute of Standards and Technology and the National Science Foundation. Finally, we will construct and evaluate novel privacy-preserving explainability mechanisms for the federated setting.

PI - Stacy Patterson         

CoPI - Oshani Seneviratne and Aparna Gupta

Extending, Simulating and Scaling Decentralized Exchanges Made by Automated Market Makers

Automated market makers [AMMs] are a decentralized approach to creating a financial market. This project has two goals: (i) constructing new decentralized market structures and (ii) simulating dynamics of these decentralized exchanges. This project will create a mathematical and computational platform to test trading strategies, market construction, and regulatory measures before they are introduced in practice.

PI - Zachary Feinstein                     

CoPI - Ionut Florescu and Ivan Bakrac

Federated Learning for Fairness-aware and Privacy-Preserving Financial Risk Assessment

The objective of this project is to study and design new federated learning techniques to preserve privacy and improve fairness in machine learning applications in financial domains. Financial data are generally distributed which means different financial entities save their own data locally and do not share data for learning purposes. However, machine learning algorithms benefit from learning large-scale datasets that cover diverse distributions. Thus, federated learning has become a popular architecture for distributed learning where data are saved locally. The goal of federated learning is to be able to harness data without a third party ever directly interacting with the data, thus ensuring users’ data remain secure. We will study federated learning algorithms for heterogeneous data with fairness constraints. We will design new dynamic and adaptive strategies for parameter sharing to handle heterogeneous financial data distributions. To guarantee different clients receive fair predictions, we will develop random matrix-based aggregation methods to prevent privacy leaking in fairness-constrained federated learning.

PI - Yue Ning                                      

CoPI - Nan Cui

Systemic Risk Implications of Central Bank Digital Currencies

Within the United States, wholesale clearing takes place through Fedwire. This clearing system is centralized and requires significant oversight. Central bank digital currencies [CBDCs] introduce a novel approach for wholesale clearing that could reduce overhead costs. This study aims to investigate the systemic risk implications of introducing CBDCs into the financial system. We develop a clearing model of both the current system (i.e., Fedwire) as a baseline to compare a novel CBDC clearing systems model. In doing so, we investigate the risk of both individual agent failure and systemic failures. These models can be used for stress testing purposes as well as provide a framework for studying the optimal design, from a systemic risk perspective, of the agent network associated with this new asset class.

PI - Zachary Feinstein                     

CoPI - Rui Fan and Stephen Taylor