Big data analysis for financial risk management

A very important area of financial risk management is systemic risk modelling, which concerns the estimation of the interrelationships between financial institutions, with the aim of establishing which of them are more central and, therefore, more contagious/subject to contagion. The aim of this paper is to develop a novel systemic risk model. A model that, differently from existing ones, employs not only the information contained in financial market prices, but also big data coming from financial tweets. From a methodological viewpoint, the novelty of our paper is the estimation of systemic risk models using two different data sources: financial markets and financial tweets, and a proposal to combine them, using a Bayesian approach. From an applied viewpoint, we present the first systemic risk model based on big data, and show that such a model can shed further light on the interrelationships between financial institutions

QuPARA: Query-Driven Large-Scale Portfolio Aggregate Risk Analysis on MapReduce

Stochastic simulation techniques are used for portfolio risk analysis. Risk portfolios may consist of thousands of reinsurance contracts covering millions of insured locations. To quantify risk each portfolio must be evaluated in up to a million simulation trials, each capturing a different possible sequence of catastrophic events over the course of a contractual year. In this paper, we explore the design of a flexible framework for portfolio risk analysis that facilitates answering a rich variety of catastrophic risk queries. Rather than aggregating simulation data in order to produce a small set of high-level risk metrics efficiently (as is often done in production risk management systems), the focus here is on allowing the user to pose queries on unaggregated or partially aggregated data. The goal is to provide a flexible framework that can be used by analysts to answer a wide variety of unanticipated but natural ad hoc queries. Such detailed queries can help actuaries or underwriters to better understand the multiple dimensions (e.g., spatial correlation, seasonality, peril features, construction features, and financial terms) that can impact portfolio risk. We implemented a prototype system, called QuPARA (Query-Driven Large-Scale Portfolio Aggregate Risk Analysis), using Hadoop, which is Apache's implementation of the MapReduce paradigm. This allows the user to take advantage of large parallel compute servers in order to answer ad hoc risk analysis queries efficiently even on very large data sets typically encountered in practice. We describe the design and implementation of QuPARA and present experimental results that demonstrate its feasibility. A full portfolio risk analysis run consisting of a 1,000,000 trial simulation, with 1,000 events per trial, and 3,200 risk transfer contracts can be completed on a 16-node Hadoop cluster in just over 20 minutes.Comment: 9 pages, IEEE International Conference on Big Data (BigData), Santa Clara, USA, 201

Research on Financial Risk Assessment Based on Artificial Intelligence

The effective application of artificial intelligence (AI) models in various fields in the field of financial risk can improve the speed of data processing, deepen the degree of data analysis and reduce the labor cost, thus effectively improving the efficiency of financial risk control. The application of AI in the field of financial risk management puts forward new requirements for the system setup and operation mode of financial supervision. With the rapid growth ofcomputer and network technology, the increase of market transaction frequency, the diversification of data sources and the development and application of big data, it brings new challenges to financial risk management based on massive data. Based on this, this paper analyzes the role of AI in promoting the reform and growth ofthe financial industry, and puts forward some countermeasures for rational use of AI in the field of financial risk management

Knowledge management applied to enterprise risk management

Risk and knowledge are two concepts and components of business management which have so far been studied almost independently. This is especially true where risk management (RM) is conceived mainly in financial terms, as for example, in the financial institutions sector. Financial institutions are affected by internal and external changes with the consequent accommodation to new business models, new regulations and new global competition that includes new big players. These changes induce financial institutions to develop different methodologies for managing risk, such as the enterprise risk management (ERM) approach, in order to adopt a holistic view of risk management and, consequently, to deal with different types of risk, levels of risk appetite, and policies in risk management. However, the methodologies for analysing risk do not explicitly include knowledge management (KM). This research examines the potential relationships between KM and two RM concepts: perceived quality of risk control and perceived value of ERM. To fulfill the objective of identifying how KM concepts can have a positive influence on some RM concepts, a literature review of KM and its processes and RM and its processes was performed. From this literature review eight hypotheses were analysed using a classification into people, process and technology variables. The data for this research was gathered from a survey applied to risk management employees in financial institutions and 121 answers were analysed. The analysis of the data was based on multivariate techniques, more specifically stepwise regression analysis. The results showed that the perceived quality of risk control is significantly associated with the variables: perceived quality of risk knowledge sharing, perceived quality of communication among people, web channel functionality, and risk management information system functionality. However, the relationships of the KM variables to the perceived value of ERM are not identified because of the low performance of the models describing these relationships. The analysis reveals important insights into the potential KM support to RM such as: the better adoption of KM people and technology actions, the better the perceived quality of risk control. Equally, the results suggest that the quality of risk control and the benefits of ERM follow different patterns given that there is no correlation between both concepts and the distinct influence of the KM variables in each concept. The ERM scenario is different from that of risk control because ERM, as an answer to RM failures and adaptation to new regulation in financial institutions, has led organizations to adopt new processes, technologies, and governance models. Thus, the search for factors influencing the perceived value of ERM implementation needs additional analysis because what is improved in RM processes individually is not having the same effect on the perceived value of ERM. Based on these model results and the literature review the basis of the ERKMAS (Enterprise Risk Knowledge Management System) is presented

Big data, big challenges: risk management of financial market in the digital economy

Purpose– The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It’s a big challenge for the government to carry out financial market risk management in the big data era. Design/methodology/approach– In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value. Findings– Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike Information Criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored. Originality/value– Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy

How do the global stock markets influence one another? Evidence from finance big data and Granger causality directed network

The recent financial network analysis approach reveals that the topologies of financial markets have an important influence on market dynamics. However, the majority of existing Finance Big Data networks are built as undirected networks without information on the influence directions among prices. Rather than understanding the correlations, this research applies the Granger causality test to build the Granger Causality Directed Network for 33 global major stock market indices. The paper further analyzes how the markets influence one another by investigating the directed edges in the different filtered networks. The network topology that evolves in different market periods is analyzed via a sliding window approach and Finance Big Data visualization. By quantifying the influences of market indices, 33 global major stock markets from the Granger causality network are ranked in comparison with the result based on PageRank centrality algorithm. Results reveal that the ranking lists are similar in both approaches where the U.S. indices dominate the top position followed by other American, European, and Asian indices. The lead-lag analysis reveals that there is lag effects among the global indices. The result sheds new insights on the influences among global stock markets with implications for trading strategy design, global portfolio management, risk management, and markets regulation

How can Big Data contribute to improve the financial performance of companies?

Objective: We propose a comprehensive methodology that combines a set of Big Data Analytics tools (BDA) with prospective analysis, risk analysis and strategic analysis with the aim to improve the firm’s financial performance measured through Key Performance Indicators (KPIs).Methodology: The methodology consists of five (5) stages: financial modeling, prospective analysis, risk analysis that includes BDA, strategic analysis and monitoringResults: This methodology allows directing the BDA towards the characterization of the critical variables that create value for the company, designing contingent strategies and evaluating their impact on the selected financial indicators (KPI) all this in a multidimensional wayRecommendations: We require constant monitoring to generate different forms of innovation and flexibility in the company and improve its financial performance.Limitations: The success of the methodology depends on the company's ability to improve, adapt, adjust, or innovate to gain, sustain, or reconfigure a competitive advantage. This skill is called process-oriented dynamic capabilities (PODC)Originality: The proposed methodology is comprehensive since it allows the inclusion of various areas of the company in order to improve its financial performance represented by the KPIs. Furthermore, the analysis can be performed for specific areas and business units.Conclusions: The proposed methodology promotes innovation and flexibility that will improve the financial performance of the company as long as there is a good fit among Big Data activities, the organizational structure, the commitment of senior management and support for the development of PODC.¿Cómo puede contribuir el Big Data a mejorar el rendimiento financiero de las empresas?Objetivo: Proponemos una metodología integral que combina un conjunto de herramientas del análisis de Big Data (BDA) con el análisis prospectivo, análisis de riesgo y análisis estratégico con la finalidad de mejorar el desempeño financiero de la empresa medido a través de Key Performance Indicators (KPI)}Metodología: La metodología está compuesta por cinco (5) etapas: modelación financiera, prospectiva, análisis de riesgo que incluye BDA, análisis estratégico y monitoreoResultados: Esta metodología permite dirigir el BDA hacia la caracterización de las variables críticas que crean valor para la empresa, diseñar estrategias contingentes y evaluar su impacto en los indicadores financieros seleccionados (KPI) todo esto de forma multidimensional.Recomendación: Se requiere un monitoreo constante del modelo para generar diferentes formas de innovación y flexibilidad en la empresa y mejorar su desempeño financiero.Limitación: El éxito de la metodología depende de la habilidad de la empresa para mejorar, adaptarse, ajustarse o innovar para ganar, sostener o reconfigurar una ventaja competitiva. A esta habilidad se le denomina capacidades dinámicas orientadas a procesos (PODC)Originalidad: La metodología propuesta es integral ya que permite la inclusión de diversas áreas de la empresa con el objetivo de mejorar su desempeño financiero representado por los KPIs. Asimismo, el análisis se puede realizar para áreas específicas y unidades de negocio.Conclusión: La metodología propuesta promueve la innovación y la flexibilidad que mejorarán el desempeño financiero de la compañía siempre que exista un buen ajuste entre las actividades de Big Data, la estructura organizacional, el compromiso de la alta gerencia y el apoyo para el desarrollo de PODC

Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc

Growing e-waste management risk awareness points towards new recycling scenarios: The view of the Big Four's youngest consultants

The e-waste sector is characterised by a rapid growth at global level and therefore involves an area not yet sufficiently investigated in its risk management dimension. This research fills the gap of the absence of a holistic approach to risk identification and assessment in e-waste management, suggesting a new Risk Awareness Indicator (RAI). An integrated Multi-criteria decision analysis (MCDA)-Analytic Hierarchy Process (AHP) is proposed to calculate the new index. Weights and values will be proposed by twenty Big Four's youngest consultants (generation-Z and millennials). For e-waste, cyber risks related to personal data are critical in the collection phase, environmental risks in the transport phase, and financial and economic risks in the processing phase. Recycling scenarios pose less overall risk than landfill alternatives. The results can help policy makers to meet the circular economy targets set at the European Union level by implementing administrative and regulatory simplifications to support recycling supply chains and make them more efficient and resilient after the pandemic disruption. This work focuses on e-waste and the opinion of screenagers consultants, however the methodology used to design the RAI index makes it easy to replicate the analysis to other social settings and other waste supply chains