Phanish Lakkarasu

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.

This paper will take a critical view of issues of investment management, secure transactions, and risk in the digital era. The Financial Services Industry is a product of the Information Technology (IT) revolution. The application of IT have had profound implications for how products are produced, how they’re exchanged, who develops the new products, and how the old products are replaced. The current wave of technological evolution is changing the way in which financial services are produced and delivered. Developments in the World Wide Web sector have accounted for an expansion and strengthening of the online transaction sector, with a growing number of fully automated banking platforms. This transition has been accompanied by considerable public expenditures on new security technologies to protect it. Of particular interest in this context is the provision of data privacy and the prevention of fraud through secure transactions in order to bolster consumer and institutional confidence in the transacting financial markets. This paper will consider investment management, secure transactions, and risk assessment for online activities in the financial sector. Technological advancements in the field of IT have allowed the financial industry to take advantage of networked computing, to automate trading decisions, and to trade very quickly and in large volumes. The financial industry can now use sophisticated high performance computer driven trading platforms. These developments have had a significant impact on the way banks, fund managers, and individuals trade currency and equity. To this day most financial orders are negotiated through people, often using voice linked dealing systems. This experience has decreased considerably due to the rapid growth of automated computer systems capable of almost instantaneous order placement with the corresponding financial institutions. With appropriate technology and clear development the banks and exchanges alike can now record at high frequency virtually all transactions that occur in the financial market. Furthermore, several data vendors are now selling high frequency data in relatively cheap and easy to work formats, essentially lowering the barriers to entry in this field. Some people consider these characteristics to be sufficient to classify financial data (specifically price data) as “Big Data”.

Traffic flow forecasting is a key problem of intelligent transport systems and represents a challenging task due to the spatial-temporal correlation features and long temporal interdependence of the considered data. Conventional methods deal with this either by spatial forecasting given observed counts at previous times or by temporal forecasting given observed traffic counts in neighbouring locations. In order to fully exploit the spatio-temporal properties observed in the data, a hybrid multimodal deep learning method for short-term traffic flow forecasting called HaMDeepT is proposed. Specifically, the HaMDeepT method can jointly and adaptively learn the spatial-temporal correlation features and long temporal interdependence of multi-modality traffic data through an attention-based auxiliary multimodal deep learning architecture. The base module of this method consists of a 1D CNN and GRU with the attention mechanism. The forecasted spatio-temporal traffic demand (counts of traffic passing through different locations at regular time intervals) is dependent on far more critical spatial factors than other point sensors such as weather stations. This HaMDeepT method, in terms of 3D CNN-GRU, which uses a stack of 3D Convolutional Neural Networks (3D CNN) and Gated Recurrent Units (GRUs) combined with the Correlation and Relative Operation layers to model both the spatial context features and the temporal dependencies of traffic count data at all locations, has a better performance compared to other network architectures. It overcomes the drawback of a fixed and handcrafted graph Laplacian matrix representation of the spatial relationships of the locations used by the ST-Graph. It uses the Correlation layer to estimate the spatial correlation features for each traffic count data point with others, focusing on the stations with major impacts on the target location, and the Relative Operation layer to model the relative distances thereafter. Using these novel methods, the traffic flow forecasting results for the miniNYC dataset are more accurate and more intuitive visualisation of the spatial structure that affects the performance of the predictions.