Research Papers

In the dynamic landscape of the banking sector, marked by increased operational complexity and regulatory scrutiny, the pursuit of innovation demands a strategic orientation. While offering advantages in cost, quality and speed, this strategic approach to innovation should mandate a cautious consideration of the risks inherent in the integration of emerging technologies. With the continuous advancement of artificial intelligence (AI), particularly generative AI (GenAI), as a method for emerging banking technologies, its wider adoption in the financial industry demands careful consideration of associated risk implications. This paper advocates for a software testing approach that delves into the complexities of banking technology platforms by leveraging generative algorithms to attain extensive test coverage and simultaneously employs rule-based analytics to refine the generated datasets, optimising coverage for faster execution and efficient resource utilisation. Such an approach is in line with a risk-averse innovation strategy, as it balances out the smart creativity.

This paper outlines an experiment in building an obfuscated version of a proprietary financial transactions dataset. As per industrial requirements, no data from the original dataset should find its way to third parties, so all the fields were generated artificially, including banks, customers (including geographic locations), and particular transactions. However, we set our goal to keeping as many distributions and correlations from the original dataset as possible, with adjustable levels of introduced noise in each of the fields: geography, bank-to-bank trans-action flows, and distributions of volumes/numbers of transactions in various subsections of the dataset. The article could be of use to anyone who may want to produce a publishable dataset, e.g., for the alternative data market, where it’s essential to keep the structure and correlations of the proprietary non-disclosed original dataset.

This paper looks at the industrial experience of using automated model-based approach for the testing of trading systems. The approach, used by Exactpro, is described using two existing taxonomies. Then, the main future applications of the models in the test automation paradigm are outlined. In our approach a model is kind of a virtual replica of the test system generating expected results from the input (built based on the specifications). Models are created in python which provides flexibility of describing complex financial systems behaviors. Models are integrated into the Exactpro th2 test automation framework, and expected results from the system under test and model are compared automatically.

The paper describes authors' experience of implementing machine learning techniques to predict deviations in service workflows duration, long before the post-trade system reports them as not completed on time. The prediction is based on analyzing a large set of performance metrics collected every second from modules of the system, and using regression models to detect running workflows that are likely to be hung. This article covers raw data preprocessing, dataset dimensionality reduction, applied regression models and their performance. Problems to be resolved and project roadmap are also described.

The focus area of this research is around tools and methods of reconciliation testing, an approach to software testing that relies on the data reconciliation concept. The importance of such a test approach is steadily increasing across different knowledge domains, triggered by growing data volumes and overall complexity of present-day software systems. The paper describes the software implementation created as part of the authors’ industrial experience with data streaming analysis for the task of reconciliation testing of complex financial technology systems. The described solution is a Python-based component of an open-source test automation framework build as a Kubernetes-based microservices platform. The paper outlines the advantages and disadvantages of the approach as well as compares it to existing state-of-the-art solutions allowing for data streaming analysis and reconciliation checks.

We propose a hybrid technique of black-box testing of virtual assistants (VAs) in the financial sector. The specifics of the highly regulated industry imposes numerous limitations on the testing process: GDPR and other data protection requirements, the absence of interaction logs with real users, restricted access to internal data, etc. These limitations also decrease the applicability of a few VA testing methods that are widely described in the research literature. The approach suggested in this paper consists of semi-controlled interaction logging from the trained testers and subsequent augmenting the collected data for automated testing.