regulatory compliance

Enterprise resource planning (ERP) systems are often viewed as a necessary burden that usually delivers less value than originally anticipated. Enterprise resource planning systems are supposed to provide organizations with the information they need for central control, regulatory compliance, and business processes that are supported by information flowing through the system. Big Data and AI models present an opportunity to redefine how ERP is implemented and maintained. It has the potential to move the ERP space from being mundane to being a significant driver of future business performance. AI, by automating human performance, can also automate system ownership and improve customer satisfaction. In this way, it is a force multiplier that changes the economics of the ERP ecosystem. The data being fed into the model and the results of that model present new challenges for ensuring that all of this can be done at the proper level of security. This is a central challenge for ensuring the long-term security of both the ERP implementation and the enterprise itself. This paper will examine the protocols that must be observed to retain security and maximize this potential transformation associated with the AI-driven future of ERP solutions. Both the opportunities and the potential pitfalls will be discussed in the hopes that this will enable a secure and efficient path to that future, minimizing the number of disruptions that a company will have to encounter on that path.

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.