CRAFT Previous Research

Fast Quantum Methods for Financial Risk Management

We investigate how quantum simulation and optimization can deliver a fast, scalable, and secure computing platform for (1) assessing systemic market and credit enterprise risk, (2) pricing complex financial/insurance instruments for risk analysis/regulatory reporting, (3) executing portfolio analysis/robo-advising.

Causal Inference for Fairness and Explainability in Financial Decision

Properly explained and fairness adjusted algorithms are becoming trending demands in business decisions, from seemingly minor issues like same-day delivery eligibility to getting access to life-changing opportunities such as education, employment, housing, and creditworthiness. Interpreting decisions help to build trust with users, ultimately improving their public images and strengthening their market presence and influence.

Risky Business? Deep Dives into DeFi

CRDecentralized Finance (DeFi) is an emerging new financial ecosystem built on the back of blockchain technology. With over $2 trillion locked in cryptocurrencies and the rapid adoption of new DeFi products by retail users and institutions, we need to understand how the volatile DeFi ecosystem may potentially disrupt the traditional financial sector. We seek to investigate current patterns of usage in DeFi lending protocols and quantify risk and user behaviors across various protocols.

Explainable ML for Credit Risk Analytics

This research project aims to assess the validity of explainable AI/ML(XAI) tools and models in the context of credit analytics by comparing the time-series and cross-sectional stability of XAI algorithms (LIME, Shapley values, etc.) with regard to several common ML algorithms (including logistic regression, tree-based models, and deep neural networks) on real consumer credit data.

High-dimensional Portfolio Design and Optimization using an Explainable Ensemble Learning Framework

Our research is going to deliver a framework and methodology to personalize portfolios. We propose a novel two-step approach to design an optimal portfolio by building an ensemble learning framework. Investment portfolio design requires combining various risky assets with appropriate weights to provide an acceptable trade-off between the portfolio return and risk to the investor, while satisfying policy and diversification requirements.

Risk Mitigation in Cross-Platform Decentralized Finance

The current leading solutions for blockchain interoperability in DeFI, such as Cosmos, Polkadot, and Chainlink, provide inter-blockchain communication to bridge various incompatible technologies, messaging, identity, and data formats. However, there are varying degrees of security, trust, and identity mechanisms utilized in these solutions, which has resulted in several cross-blockchain exploits in the recent past and hesitation by the financial industry in adopting blockchain solutions. The primary goal of this project is to see how cross-chain protocols can be strengthened to minimize such risks in DeFi.

Predictive Learning from Long Financial Documents

The aim of this project is to leverage the vast troves of textual data spanning disclosures, reports, news articles and reviews to extract insights and features for predictive learning. The focus is on effective textual models for prediction of quantitative performance indicators from long documents, which is a particularly challenging domain. Our goal is to develop novel language modeling techniques for the representation of long financial documents, advancing the state of pre-trained language models as well as graph neural networks.