financial services

Developing a quantum computing algorithm that outperforms its classical counterpart is widely viewed as a major milestone for the field. We achieve this milestone by offering an explicit, efficiently implementable algorithm that solves a fundamental problem in investing. One would want faster algorithms for better models at the same scale before worrying about learning the entire wealth distribution. We propose efficient quantum algorithms for both of these key subproblems.For example, quantum computers can efficiently reverse-engineer private shares to attain an accurate estimate of price-sensitive inside information. In recent years, the role of artificial intelligence has grown considerably in the operational decision-making process and, in particular, in the financial services industry. This paper takes advantage of the progress in quantum computing, addressing the problem of wealth distribution prediction in a big data set, a key problem in the deployment of trading strategies. Through a high-performance cloud computing architecture, we assess the impact of quantum computing technologies in comparison with the classical approach through several prediction models and analytical methodologies in both machine and deep learning.

Explainable AI (XAI) has been delivering ground-breaking results in various domains. Emerging in parallel with the rise of powerful machine learning models, how to extend explainability to those black-box systems and promote its integrality have evolved into a blooming research field. Financial services are among the first to highlight the requirement for interpretable and fair algorithms, and the European Union has established the minimum regulatory and supervisory expectations for taking Transparency and Explainability of AI into national law. And XAI seems to be an inevitable future trend in anti-money laundering detection due to the booming applications of machine learning techniques. Thereat, a novel and all-round XAI-Prompted AI-Driven Security and Compliance Platform for Financial Transactions is proposed, providing AI decision uncertainty and traces, disclosing feature attributions, and automatically generating data analytic compliance documentation. A comprehensive comparison of manifold interpretation methods is also conducted to yield salient results, suggesting that a model-specific and post-hoc algorithm can prominently outperform others in this special financial domain. Moreover, adopting the innovative language model to automatically generate explanations of the prediction target is also explored successfully. Fargo is an interdisciplinary team, composed of researchers across computer science, machine learning, natural language processing, and financial regulation. They communicate and cooperate to make Fargo transparent and clearly documented its model, data, techniques, methodology and results. They receive a financial transaction that is not classified as suspicious or unusual.