Research & Insights
Thought leadership, research publications, and case studies from the forefront of AI and financial technology.
Research Publications
In-depth research and analysis on the latest trends in financial AI and technology.
q-Gaussian distributions of leverage returns, first stopping times, and default risk valuations
We analyze daily leverage returns of North American industrial firms during the financial crisis, finding their distributions follow q-Gaussian models indicating higher default risk, and develop a q-Gaussian default model predicting increased default probabilities and wider credit spreads than classical models.
Time-resolved topological data analysis of market instabilities
We apply a novel econometric method based on time-resolved topological data analysis using Takens’ embedding and sliding windows to detect market instabilities across North American sectors, showing that persistence landscape norms from credit default swap spreads serve as leading indicators of financial crashes.
Topological data analysis of financial time series: Landscapes of crashes
This study applies topological data analysis to U.S. stock market indices, showing that persistence landscapes derived from multidimensional time series can reveal early warning signals of major financial crashes such as the 2000 tech bust and the 2007–2009 crisis.
Latest Insights
Thought leadership on AI, finance, and the future of financial technology
Understanding AI in Risk Management and Its Impact on Financial Services
How banks deploy machine learning across credit, market, and operational risk—scenario modeling, anomaly detection, stress testing—and the model validation, bias controls, and human oversight required to manage model risk and improve decisions.
Will 2025 be the year of widespread regulatory automation?
Why 2025 is pivotal for regtech: automated rule monitoring, KYC/AML orchestration, and cloud workflows that cut manual effort, lower compliance costs, and improve auditability across complex, fast-changing regulatory landscapes.
The Vital Importance of AI Trust within Financial Services
Trust as a prerequisite for AI adoption: transparent models, strong data governance, security controls, and ethics frameworks that reduce bias, protect customers, and align with regulatory expectations to sustain confidence and scale, while enabling responsible, scalable innovation.