Identifying clusters of anomalous payments in the salvadorian payment system

We develop an unsupervised methodology to group payments and identify possible anomalies. With our methodology, we identify clusters based on a set of network features, using transactional (unlabeled) information from a systemically important payment system of El Salvador. We first preprocess network features, such as degree and strength, through a principal components analysis we reduce the dimensionality of the newly defined data, then we place the main variables into clustering algorithms (k-means and DBSCAN) to analyze anomalous payments. We then analyze, these clusters using random forest to obtain the main network feature. Our results suggest that the proposed methodology works very well to detect anomalous payments, and it is very important to study the case of El Salvador, because of the recent restructuring of the Massive Payment System in El Salvador (promoted by the Transfer365 project), because the authorities want to increase financial inclusion. This change will make the SPM available to the public, to diversify services and incorporate more participants because, historically, it has operated with only three active participants. We expected that Transfer365 will interconnect the LBTR participants' systems with their banking core, the systems of the Ministry of Finance, and other authorized participants to channel large payment flows. Then, identifying possible anomalies through methodology will enhance risk monitoring and management by payment systems overseers

Evaluating multiple causes of persistent low microwave backscatter from Amazon forests after the 2005 drought

Amazonia has experienced large-scale regional droughts that affect forest productivity and biomass stocks. Space-borne remote sensing provides basin-wide data on impacts of meteorological anomalies, an important complement to relatively limited ground observations across the Amazon’s vast and remote humid tropical forests. Morning overpass QuikScat Ku-band microwave backscatter from the forest canopy was anomalously low during the 2005 drought, relative to the full instrument record of 1999–2009, and low morning backscatter persisted for 2006–2009, after which the instrument failed. The persistent low backscatter has been suggested to be indicative of increased forest vulnerability to future drought. To better ascribe the cause of the low post-drought backscatter, we analyzed multiyear, gridded remote sensing data sets of precipitation, land surface temperature, forest cover and forest cover loss, and microwave backscatter over the 2005 drought region in the southwestern Amazon Basin (4°-12°S, 66°-76°W) and in adjacent 8°x10° regions to the north and east. We found moderate to weak correlations with the spatial distribution of persistent low backscatter for variables related to three groups of forest impacts: the 2005 drought itself, loss of forest cover, and warmer and drier dry seasons in the post-drought vs. the pre-drought years. However, these variables explained only about one quarter of the variability in depressed backscatter across the southwestern drought region. Our findings indicate that drought impact is a complex phenomenon and that better understanding can only come from more extensive ground data and/or analysis of frequent, spatially-comprehensive, high-resolution data or imagery before and after droughts

Payment Systems Report - June of 2020

With its annual Payment Systems Report, Banco de la República offers a complete overview of the infrastructure of Colombia’s financial market. Each edition of the report has four objectives: 1) to publicize a consolidated account of how the figures for payment infrastructures have evolved with respect to both financial assets and goods and services; 2) to summarize the issues that are being debated internationally and are of interest to the industry that provides payment clearing and settlement services; 3) to offer the public an explanation of the ideas and concepts behind retail-value payment processes and the trends in retail payments within the circuit of individuals and companies; and 4) to familiarize the public, the industry, and all other financial authorities with the methodological progress that has been achieved through applied research to analyze the stability of payment systems. This edition introduces changes that have been made in the structure of the report, which are intended to make it easier and more enjoyable to read. The initial sections in this edition, which is the eleventh, contain an analysis of the statistics on the evolution and performance of financial market infrastructures. These are understood as multilateral systems wherein the participating entities clear, settle and register payments, securities, derivatives and other financial assets. The large-value payment system (CUD) saw less momentum in 2019 than it did the year before, mainly because of a decline in the amount of secondary market operations for government bonds, both in cash and sell/buy-backs, which was offset by an increase in operations with collective investment funds (CIFs) and Banco de la República’s operations to increase the money supply (repos). Consequently, the Central Securities Depository (DCV) registered less activity, due to fewer negotiations on the secondary market for public debt. This trend was also observed in the private debt market, as evidenced by the decline in the average amounts cleared and settled through the Central Securities Depository of Colombia (Deceval) and in the value of operations with financial derivatives cleared and settled through the Central Counterparty of Colombia (CRCC). Section three offers a comprehensive look at the market for retail-value payments; that is, transactions made by individuals and companies. During 2019, electronic transfers increased, and payments made with debit and credit cards continued to trend upward. In contrast, payments by check continued to decline, although the average daily value was almost four times the value of debit and credit card purchases. The same section contains the results of the fourth survey on how the use of retail-value payment instruments (for usual payments) is perceived. Conducted at the end of 2019, the main purpose of the survey was to identify the availability of these payment instruments, the public’s preferences for them, and their acceptance by merchants. It is worth noting that cash continues to be the instrument most used by the population for usual monthly payments (88.1% with respect to the number of payments and 87.4% in value). However, its use in terms of value has declined, having registered 89.6% in the 2017 survey. In turn, the level of acceptance by merchants of payment instruments other than cash is 14.1% for debit cards, 13.4% for credit cards, 8.2% for electronic transfers of funds and 1.8% for checks. The main reason for the use of cash is the absence of point-of-sale terminals at commercial establishments. Considering that the retail-payment market worldwide is influenced by constant innovation in payment services, by the modernization of clearing and settlement systems, and by the efforts of regulators to redefine the payment industry for the future, these trends are addressed in the fourth section of the report. There is an account of how innovations in technology-based financial payment services have developed, and it shows that while this topic is not new, it has evolved, particularly in terms of origin and vocation. One of the boxes that accompanies the fourth section deals with certain payment aspects of open banking and international experience in that regard, which has given the customers of a financial entity sovereignty over their data, allowing them, under transparent and secure conditions, to authorize a third party, other than their financial entity, to request information on their accounts with financial entities, thus enabling the third party to offer various financial services or initiate payments. Innovation also has sparked interest among international organizations, central banks, and research groups concerning the creation of digital currencies. Accordingly, the last box deals with the recent international debate on issuance of central bank digital currencies. In terms of the methodological progress that has been made, it is important to underscore the work that has been done on the role of central counterparties (CCPs) in mitigating liquidity and counterparty risk. The fifth section of the report offers an explanation of a document in which the work of CCPs in financial markets is analyzed and corroborated through an exercise that was built around the Central Counterparty of Colombia (CRCC) in the Colombian market for non-delivery peso-dollar forward exchange transactions, using the methodology of network topology. The results provide empirical support for the different theoretical models developed to study the effect of CCPs on financial markets. Finally, the results of research using artificial intelligence with information from the large-value payment system are presented. Based on the payments made among financial institutions in the large-value payment system, a methodology is used to compare different payment networks, as well as to determine which ones can be considered abnormal. The methodology shows signs that indicate when a network moves away from its historical trend, so it can be studied and monitored. A methodology similar to the one applied to classify images is used to make this comparison, the idea being to extract the main characteristics of the networks and use them as a parameter for comparison. Juan José Echavarría Governo

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

The Return of the Rogue

The “rogue trader”—a famed figure of the 1990s—recently has returned to prominence due largely to two phenomena. First, recent U.S. mortgage market volatility spilled over into stock, commodity, and derivative markets worldwide, causing large financial institution losses and revealing previously hidden unauthorized positions. Second, the rogue trader has gained importance as banks around the world have focused more attention on operational risk in response to regulatory changes prompted by the Basel II Capital Accord. This Article contends that of the many regulatory options available to the Basel Committee for addressing operational risk it arguably chose the worst: an enforced selfregulatory regime unlikely to substantially alter financial institutions’ ability to successfully manage operational risk. That regime also poses the danger of high costs, a false sense of security, and perverse incentives. Particularly with respect to the low-frequency, high-impact events—including rogue trading—that may be the greatest threat to bank stability and soundness, attempts at enforced self-regulation are unlikely to significantly reduce operational risk, because those financial institutions with the highest operational risk are the least likely to credibly assess that risk and set aside adequate capital under a regime of enforced self-regulation

A lightweight prototype of a magnetometric system for unmanned aerial vehicles

Detection of the Earth’s magnetic field anomalies is the basis of many types of studies in the field of earth sciences and archaeology. These surveys require different ways to carry out the measures but they have in common that they can be very tiring or expensive. There are now several lightweight commercially available magnetic sensors that allow light-UAVs to be equipped to perform airborne measurements for a wide range of scenarios. In this work, the realization and functioning of an airborne magnetometer prototype were presented and discussed. Tests and measures for the validation of the experimental setup for some applications were reported. The flight sessions, appropriately programmed for different types of measurements, made it possible to evaluate the performance of this detection methodology, highlighting the advantages and drawbacks or limitations and future developments. From the results obtained it was possible to verify that the measurement system is capable of carrying out local and potentially archaeological magnetometric measurements with the necessary precautions

Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems

Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in isolation, and few provisions exist for data sharing due to fears of unintentional leaks and adversarial attacks. Collaborative learning advances in finance are rare, and it is hard to find real-world insights derived from privacy-preserving data processing systems. In this paper, we present a collaborative deep learning framework for fraud prevention, designed from a privacy standpoint, and awarded at the recent PETs Prize Challenges. We leverage latent embedded representations of varied-length transaction sequences, along with local differential privacy, in order to construct a data release mechanism which can securely inform externally hosted fraud and anomaly detection models. We assess our contribution on two distributed data sets donated by large payment networks, and demonstrate robustness to popular inference-time attacks, along with utility-privacy trade-offs analogous to published work in alternative application domains

Transforming Data into Meaning. Data-Driven approaches for Particle Physics, Nuclear Power Safety and Humanitarian Crisis Situations.

Machine learning and data intensive methods can be applied to a plethora of research domains. We apply supervised and unsupervised machine learning, Monte Carlo simulations and statistical tools to three diverse areas of research, tackling a range of computational and data analysis challenges unique to their respective environments. Using SHERPA-a Monte Carlo event generator-as a Standard Model machine we generate thousands of particle collision events. We employ a range of neural network architectures to determine the most powerful discriminating features which eliminate vast numbers of background events enabling us to calculate new constraints on the charm Yukawa coupling at the Large Hadron Collider and future projections. Hartlepool Nuclear Power Station has a rich array of instrumentation that continuously monitors reactor health as frequently as every second, at all times. We apply unsupervised machine learning and Bayesian tools to scrutinise anomalous behaviour in the data which is indicative of instrumentation degradation prior to instrumentation failure. JUNE-an agent based epidemiological simulation-is used to extract novel social mixing matrices at Cox's Bazar, a refugee camp in Bangladesh containing ${\sim}600,000$ displaced people. These contact matrices can be used to understand social interactions and disease spread and therefore provide better utilisation of limited resources