In the financial industry, identifying the location of parties involved in payments is a major challenge in the context of various regulatory requirements. For this purpose address parsing entails extracting fields such as street, postal code, or country from free text message attributes. While payment processing platforms are updating their standards with more structured formats such as SWIFT with ISO 20022, address parsing remains essential for a considerable volume of messages. With the emergence of Transformers and Generative Large Language Models (LLM), we explore the performance of state-of-the-art solutions given the constraint of processing a vast amount of daily data. This paper also aims to show the need for training robust models capable of dealing with real-world noisy transactional data. Our results suggest that a well fine-tuned Transformer model using early-stopping significantly outperforms other approaches. Nevertheless, generative LLMs demonstrate strong zero-shot performance and warrant further investigations.

Predicting the economy's short-term dynamics -- a vital input to economic agents' decision-making process -- often uses lagged indicators in linear models. This is typically sufficient during normal times but could prove inadequate during crisis periods. This paper aims to demonstrate that non-traditional and timely data such as retail and wholesale payments, with the aid of nonlinear machine learning approaches, can provide policymakers with sophisticated models to accurately estimate key macroeconomic indicators in near real-time. Moreover, we provide a set of econometric tools to mitigate overfitting and interpretability challenges in machine learning models to improve their effectiveness for policy use. Our models with payments data, nonlinear methods, and tailored cross-validation approaches help improve macroeconomic nowcasting accuracy up to 40\% -- with higher gains during the COVID-19 period. We observe that the contribution of payments data for economic predictions is small and linear during low and normal growth periods. However, the payments data contribution is large, asymmetrical, and nonlinear during strong negative or positive growth periods.

"Life isn't fair" is perhaps one of the most frequently repeated philosophical statements passed down from generation to generation. In a world increasingly dominated by data, however, groups of people that have already been dealt an unfair hand may see themselves further disadvantaged through the use of algorithms to determine whether or not they qualify for employment, housing, or credit, among other basic needs for survival. In the past few years, more attention has been paid to algorithmic bias, but there is still debate about both what can be done to address the issue, as well as what should be done. The use of an algorithm is not at issue; algorithms are essentially a set of instructions on how to complete a problem or task. Yet the lack of transparency surrounding the data and how it is weighed and used for decision making is a key concern, particularly when the algorithm's use may impact people in significant ways, often with no explanation as to why they have been deemed unqualified or unsuitable for a product, service, or opportunity.

On-street parking is convenient, but has many disadvantages: on-street spots come at the expense of other road uses such as traffic lanes, transit lanes, bike lanes, or parklets; drivers looking for parking contribute substantially to traffic congestion and hence to greenhouse gas emissions; safety is reduced both due to the fact that drivers looking for spots are more distracted than other road users and that people exiting parked cars pose a risk to cyclists. These social costs may not be worth paying when off-street parking lots are nearby and have surplus capacity. To see where this might be true in downtown Vancouver, we used artificial intelligence techniques to estimate the amount of time it would take drivers to both park on and off street for destinations throughout the city. For on-street parking, we developed (1) a deep-learning model of block-by-block parking availability based on data from parking meters and audits and (2) a computational simulation of drivers searching for an on-street spot. For off-street parking, we developed a computational simulation of the time it would take drivers drive from their original destination to the nearest city-owned off-street lot and then to queue for a spot based on traffic and lot occupancy data. Finally, in both cases we also computed the time it would take the driver to walk from their parking spot to their original destination. We compared these time estimates for destinations in each block of Vancouver's downtown core and each hour of the day. We found many areas where off street would actually save drivers time over searching the streets for a spot, and many more where the time cost for parking off street was small. The identification of such areas provides an opportunity for the city to repurpose valuable curbside space for community-friendly uses more in line with its transportation goals.

With new technologies like faster payments taking hold, the explosion of readily available data, and the ever-changing regulatory landscape, staying ahead of financial crime and compliance risk has become more complex and expensive than ever before. As these trends show no sign of abating, the compliance operations and monitoring staff of a financial institution often find themselves a major cost center. Financial institutions must manage compliance budgets without losing sight of primary functions and quality control. To answer this, many have made the move to automating time-intensive, rote tasks like data gathering and sorting through alerts by adopting innovative technologies like AI and machine learning to free up time-strapped analysts for more informed and precise decision-making processes. As financial institutions often benchmark themselves against their competitors, they are increasingly interested in seeing how these technologies are performing, and are asking themselves how to leverage artificial intelligence and machine learning to increase insight, reduce false positives and decrease compliance spend.

– Last Sunday I was at big retail store in Harare and it was a very busy day due to the fact it was month end and people got paid. Grocery shopping was in full swing, I also bought some groceries for my self. When I was in the queue for payment and collection, I saw almost every one making payment either by swiping the magic plastic card or struggling on their mobile handset by punching few numbers etc. The electronic payment queue was moving fast compared to the cash payment queue where I saw only a handful of people with just one/two small item/items. The thought came to my mind out of this whole picture was "Whats happening here besides the payments through mobile and plastic"?

This week saw the placation of GST bill on logistics along with many more news such as evolvement of digital payments and artificial intelligence in E-commerce industry. The meeting of AI and e-commerce could not only transform the way jillions of online transactions are done, but also change the in-store purchase behaviors which are influenced by digital interactions. According to Sachin Bansal, CEO, Flipkart, artificial intelligence is a key differentiator in the fiercely competitive e-commerce business. Digital payments will grow 10 times to reach 500 billion by 2020 and contribute 15% of gross domestic product (GDP). Some of the key reasons of these acquisitions include privacy of customer's payment data, secured payment facility and use of payment data for big data analysis to the company.