The evaluation of smart contract reputability is essential to foster trust in decentralized ecosystems. However, existing methods that rely solely on static code analysis or transactional data, offer limited insight into evolving trustworthiness. We propose a multimodal data fusion framework that integrates static code features with transactional data to enhance reputability prediction. Our framework initially focuses on static code analysis, utilizing GAN-augmented opcode embeddings to address class imbalance, achieving 97.67% accuracy and a recall of 0.942 in detecting illicit contracts, surpassing traditional oversampling methods. This forms the crux of a reputability-centric fusion strategy, where combining static and transactional data improves recall by 7.25% over single-source models, demonstrating robust performance across validation sets. By providing a holistic view of smart contract behaviour, our approach enhances the model's ability to assess reputability, identify fraudulent activities, and predict anomalous patterns. These capabilities contribute to more accurate reputability assessments, proactive risk mitigation, and enhanced blockchain security.

Representation learning produces models in different domains, such as store purchases, client transactions, and general people's behaviour. However, such models for sequential data usually process a single sequence, ignoring context from other relevant ones, even in domains with rapidly changing external environments like finance or misguiding the prediction for a user with no recent events. We are the first to propose a method that aggregates information from multiple user representations augmenting a specific user one for a scenario of multiple co-occurring event sequences. Our study considers diverse aggregation approaches, ranging from simple pooling techniques to trainable attention-based approaches, especially Kernel attention aggregation, that can highlight more complex information flow from other users. The proposed method operates atop an existing encoder and supports its efficient fine-tuning. Across considered datasets of financial transactions and downstream tasks, Kernel attention improves ROC AUC scores, both with and without fine-tuning, while mean pooling yields a smaller but still significant gain.

High-quality representation of transactional sequences is vital for modern banking applications, including risk management, churn prediction, and personalized customer offers. Different tasks require distinct representation properties: local tasks benefit from capturing the client's current state, while global tasks rely on general behavioral patterns. Previous research has demonstrated that various self-supervised approaches yield representations that better capture either global or local qualities. This study investigates the integration of two self-supervised learning techniques -- instance-wise contrastive learning and a generative approach based on restoring masked events in latent space. The combined approach creates representations that balance local and global transactional data characteristics. Experiments conducted on several public datasets, focusing on sequence classification and next-event type prediction, show that the integrated method achieves superior performance compared to individual approaches and demonstrates synergistic effects. These findings suggest that the proposed approach offers a robust framework for advancing event sequences representation learning in the financial sector.

Effective processing of financial transactions is essential for banking data analysis. However, in this domain, most methods focus on specialized solutions to stand-alone problems instead of constructing universal representations suitable for many problems. We present a representation learning framework that addresses diverse business challenges. We also suggest novel generative models that account for data specifics, and a way to integrate external information into a client's representation, leveraging insights from other customers' actions. Finally, we offer a benchmark, describing representation quality globally, concerning the entire transaction history; locally, reflecting the client's current state; and dynamically, capturing representation evolution over time. Our generative approach demonstrates superior performance in local tasks, with an increase in ROC-AUC of up to 14\% for the next MCC prediction task and up to 46\% for downstream tasks from existing contrastive baselines. Incorporating external information improves the scores by an additional 20\%.

I recently visited with the CIO of a Fortune 500 company. He was touting the advances they had made in IT and corporate culture regarding the use of artificial intelligence and analytics, but he had one major concern: How do you fuse AI and analytics into the rest of your transactional line of business IT infrastructure? It hasn't been that way in his enterprise. His IT organization had started its analytics initiative with an internal Hadoop group that was responsible for processing big data internally. Meanwhile other departments in IT supported transactional data processing on an assortment of mainframes and servers in the data center. Regular IT and the Hadoop groups were somewhat siloed from each other because the parallel processing and storage management needs for big data and AI were notably different than what they were for transactional data and processing management.

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That notion is belied by Scotiabank (officially the Bank of Nova Scotia), which has pursued a results-oriented approach to artificial intelligence over the past two years. While some of its resources are devoted to exploring how new technologies -- including blockchain and quantum computing -- might drive fresh business models and products, the great majority of its data and AI work is focused on improving operations today rather than incubating for the future. By all accounts, this integrated reporting structure is what allowed Scotiabank to move rapidly to gather and manage the necessary data and put analytics and AI capabilities in place. The analytics application uses a machine learning model -- called the Customer Vulnerability Index -- to identify consumers who are likely to have short-term cash-flow issues, using transactional data such as deposits and spending levels. It has found substantial returns from automating tasks in the back office of its global banking marketing division and improving security on the front line.

Some organizations may feel that acquiring AI capabilities is a race, and if a company starts late, it can never catch up. That notion is belied by Scotiabank (officially the Bank of Nova Scotia), which has pursued a results-oriented approach to artificial intelligence over the past two years. While some of its resources are devoted to exploring how new technologies -- including blockchain and quantum computing -- might drive fresh business models and products, the great majority of its data and AI work is focused on improving operations today rather than incubating for the future. As a result, Scotiabank -- one of the Big Five banks based in Canada -- has caught up to competitors in some crucial areas. It has done so by more closely integrating its data and analytics work; taking a pragmatic approach to AI; and focusing on reusable data sets, which help with both speed and return on investment.

Financial institutions use clients' payment transactions in numerous banking applications. Transactions are very personal and rich in behavioural patterns, often unique to individuals, which make them equivalent to personally identifiable information in some cases. In this paper, we generate synthetic transactional profiles using machine learning techniques with the goal to preserve both data utility and privacy. A challenge we faced was to deal with sparse vectors due to the few spending categories a client uses compared to all the ones available. We measured data utility by calculating common insights used by the banking industry on both the original and the synthetic data-set. Our approach shows that neural network models can generate valuable synthetic data in such context. Finally, we tried privacy-preserving techniques and observed its effect on models' performances.

But, as entire economies become more data-driven, with government-enforced tax controls demanding increasingly granular levels of transactional information, there is a growing need for analytics solutions capable of handling this data, securely and at scale. Fortunately, innovations such as artificial intelligence (AI) and machine learning mean that businesses are able to scale up their analysis like never before to ensure the right data is presented in the right format for the right audience. Tax reporting is becoming more complicated as different countries have different requirements. With increasingly strict penalties for non-compliance, businesses everywhere need to consider their analytics capabilities if they hope to keep up. In an effort to close their respective country's VAT gap, tax authorities across the world are using every tool at their disposal to collect all revenue owed to them. Real-time VAT reporting, for example, is growing in popularity, with many tax authorities employing continuous transaction controls – such as electronic invoicing and audit reporting – to insert themselves ever closer to companies' transactions.