Machine learning (ML) has recently achieved impressive success in many applications. As ML starts to penetrate into safety-critical domains, security/robustness concerns on ML systems have received lots of attention lately. Very surprisingly, recent work has shown that current ML models are vulnerable to adversarial attacks, e.g. by perturbing the input slightly ML models can be manipulated to output completely unexpected results. Many attack and defence algorithms have been developed in the field under the convenient but questionable Lp attack model.
Detection of financial fraud is a priority for financial institutions. There are a variety of techniques and models that can be used to address the problem of financial fraud. However, as fraudsters are becoming more inventive and adaptive, they have been able to penetrate the conventional protective methods. This is one of the main reasons for the growth in financial fraud activity, regardless of the efforts of financial institutions and government and law enforcement agencies.
Financial fraud is a serious issue that is taking place globally and causing considerable damage at great expense. Statistical analysis and machine learning tools can help financial institutions detect different types of fraud. In some cases however, mislabeling and the cost of classification may actually increase the volume of ‘false positives’ for supervised methods. As the number of normal transactions in financial domains far outweigh the number of anomalous transactions, it is challenging to classify the anomaly labels.
With the increasing popularity of digital assets such as cryptocurrencies, many financial technology (FinTech) systems have become safety critical. However, current FinTech system development approaches often lack the rigorous safety practices found in the aerospace, nuclear, automotive, and military industries.
To have a strategic advantage over competitors, companies have been encouraged to adopt customer-centric, value added processes and capabilities. Firms allocate resources to train their employees in the necessary skills to build and maintain healthy relationships with their customers, yet little is understood on how investments in training impacts a firm?s performance. The objective of the proposed research is to investigate (1) Which customer management training activities have a positive impact on profitability? (2) How frequently should companies offer training to their employees?
During the past two decades in Canada, use of electronic payment has steadily increased. However, despite the downward trend in the volume, the value of cheques has steadily increased, with the five-year average volume growth increasing by about 2% due in large part to their common presence in the business-to-business space. These trends indicate that even with emerging of EFT payment instruments and online transfer options as substitutes for cheque, there are yet some barriers to electronic payment adoption specially for small and medium businesses.
Modular and offsite construction, where a module of project or complete house is manufactured in a factory, requires a large upfront capital (working capital) investment in order to procure materials in advance of manufacturing and to deliver modules on time and on schedule. Thus, modular fabricators need to receive deposit and progress payment before the assembly process. In the eyes of a bank, a prefab house is “just materials”.
In this research we will identify current types of customer, taking into account people who prefer to use a variety of platforms and different preferences in terms of how actively they manage their money. . We will carry out focus groups and interpret the results of a survey in terms of their implications for a set of factors that differentiate between banking customers. Using the factor scores obtained in a survey we will segment into meaningful groups (personas).
Models used for Wildfire catastrophe insurance as of today are not considering substantial information, such as geographic information and environmental constraints. The objective of the project is to establish a theoretical framework and an empirical process to enhance Aviva Canada’s current Wildfire Economic Capital (EC) model, to be able to determine the amount of capital needed to be allocated to ensure the company remains solvent, in case of occurrence of risks.
This project aims to develop a dynamic financial computable general equilibrium model (CGE) with interaction between real and financial side of the world economy. It seeks to understand how monetary policy changes such as interest rate changes, QE measures, and exchange rate changes affect the real economy by applying the financial dynamic CGE model. This project collaborates with the partner organization--the Infinite-Sum Modeling Inc.—to build a CGE-FDI database and to develop the financial CGE model.
