Title: An automatic and personalized strategy for scoring, tracking and monitoring credit risk and collections using machine learning and artificial intelligence methods

Abstract:

A credit and collections strategy aims to assess the risk of a person or company in terms of granting credit and collecting reimbursements. A large number of research is primarily based on the combination and construction methods of credit scoring models. But the credit and collection strategy cannot be limited only to Credit Scoring but goes further through the management of models through their follow-up, monitoring, validation and calibration. In this sense, firstly, different types, sources, quality and quantity of credit and collection data require different model specifications and how to automatically search and construct credit scoring models according to the data available has become essential. Second, once a model is specified, how to automatically update or calibrate it once it no longer works well in the target population has become a key to optimizing time, resources, risk-return and generating correct predictions on time. Finally, how to automatically implement the models and its actualizations in real time has become necessary to ensure business agility. Those three essentials are the main concern of this proposal. In response to the current challenges for credit scoring research and development, we proposed an automatic and personalized strategy for scoring, tracking and monitoring credit risk and collections, designed a credit and collection assessment platform which includes an analytic engine, a monitoring module, a web service module, and finally the user and model management module. The analytical engine includes data import, processing, cleaning and treatment of large volumes of information through data mining, identification, creation and transformation of variables, feature selection, segmentation, classification model automatic search, parameter estimation and classification output. For this module we further incorporate automatic search ways to reduce manual interaction. The monitoring module includes analysis of discrimination, stability, concentration, calibration and risk determinants. In the web service, solutions are implemented instantly from anywhere in the world through the cloud. Lastly, the platform organizes, produces, maintains and manages the analytical models. We utilize public and self-owned credit data sets to conduct experiments and compare them with the latest credit assessment methods. Extensive experiments have demonstrated that our proposal achieves noticeable performance improvements. In addition, the findings show that our platform balances automation and accuracy, which can be implemented to the financial industry to enhance the reliability of credit and collection assessments.

Biography:

Adriana Uquillas has a PhD in Statistics from the University of São Paulo (Brazil). She is also a Mathematical Engineer from the Escuela Politécnica Nacional (Ecuador) and Bachelor in Classical Music from the Latin American Institute of Sacred Music (Ecuador). Adriana was Research and Development Manager, Manager of Credit Models and Credit Risk Manager and Senior Manager at Itaú Unibanco Bank, at Banco Múltiplo HSBC and at Banco Bradesco. Since 2016, she is co-founder and president of EstocasticAnalytics S.A company, which provides analytical services that contribute to business innovation. In the academic field, Adriana is a full professor in the Mathematics Department of the Escuela Politécnica Nacional. She has fourteen years of experience in the financial sector, dedicated to Research and Development in the banking sector. Also, she has collaborated in the Scientific Division of Energy Analysis and Development of the University of São Paulo and in the Machine Learning and Computer Vision research group of Escuela Politécnica Nacional. Currently, she is a member of the Integrative Ecology Research Group and collaborates in the Multidisciplinary Research Group for Public Policy of the Escuela Politécnica Nacional.

Title: Artificial Intelligence and Firm Performance: Does Machine Intelligence Shield Firms from Risks?

Abstract:

Artificial Intelligence (AI) has gained growing attention among different sectors of society, industries, and businesses in the past decade. The coronavirus pandemic (COVID-19) has accelerated and underscored the application of AI technology. The exponential growth of AI adoption has significant benefits for firm performance. However, prior studies mainly focused on theoretical aspects of the benefits of AI adoption on business processes. Empirical research on the impact of AI adoption on the performance of listed firms and security markets is underexplored and mainly focuses on the United States.

Therefore, this study empirically estimates and compares the impacts of the COVID-19 pandemic on the performance of AI-adopted and conventional listed firms globally using stock market indices. The single-group and multiple-group Interrupted Time-Series Analyses (ITSA) with panel data were used with four interventions: when the COVID-19 news spread and the pandemic entered the first, second, third, and fourth months (24/02/2020, 23/03/2020, 20/04/2020, and 18/05/2020, respectively).

The results show that the AI stock market outperformed the conventional stock market pre-COVID-19 period. The negative impacts of COVID-19 on the AI stock market were less severe than the conventional stock market in the first month of the pandemic. The performance of the AI stocks recovered quicker than the conventional stocks as the pandemic entered the third month. The results suggest that the AI stock market is more resilient than the non-AI stock market. Our study provides important evidence of the success of firms adopting AI in response to risks. Thus, the firms’ adoption of AI is a crucial driver for sustainable performance in challenging environments.

Artificial Intelligence (AI) has gained growing attention among different sectors of society, industries, and businesses in the past decade. The coronavirus pandemic (COVID-19) has accelerated and underscored the application of AI technology. The exponential growth of AI adoption has significant benefits for firm performance. However, prior studies mainly focused on theoretical aspects of the benefits of AI adoption on business processes. Empirical research on the impact of AI adoption on the performance of listed firms and security markets is underexplored and mainly focuses on the United States.

Therefore, this study empirically estimates and compares the impacts of the COVID-19 pandemic on the performance of AI-adopted and conventional listed firms globally using stock market indices. The single-group and multiple-group Interrupted Time-Series Analyses (ITSA) with panel data were used with four interventions: when the COVID-19 news spread and the pandemic entered the first, second, third, and fourth months (24/02/2020, 23/03/2020, 20/04/2020, and 18/05/2020, respectively).

The results show that the AI stock market outperformed the conventional stock market pre-COVID-19 period. The negative impacts of COVID-19 on the AI stock market were less severe than the conventional stock market in the first month of the pandemic. The performance of the AI stocks recovered quicker than the conventional stocks as the pandemic entered the third month. The results suggest that the AI stock market is more resilient than the non-AI stock market. Our study provides important evidence of the success of firms adopting AI in response to risks. Thus, the firms’ adoption of AI is a crucial driver for sustainable performance in challenging environments.

Biography:

Linh Tu Ho is a Lecturer in Finance, Faculty of Agribusiness and Commerce, Lincoln University, New Zealand. Her research focuses on investment, risk, and financial markets. With the applied quantitative approach, Linh has expanded her area to risk management, sustainable investment, finance technology, and green finance. She has won several research awards including the Sustainable Finance – INFINZ Prize and Semi-finalist of the InSPiR2eS Global PITCHING RESEARCH® Competition (IGPRC) 2022. Linh presented at several leading conferences, including the Accounting and Finance Association of Australia and New Zealand Conference (AFAANZ), International Congress on Modelling and Simulation (MODSIM) in Australia, and New Zealand Association of Economists Annual Conference (NZAE).

Title: Cultural heterogeneity and risk taking of multinational firms

Abstract:

In this paper, we examine whether cultural differences are associated with the operational risks of multinational firms. Research shows that multinational operations give rise to higher risk premiums (Fillat et al., 2015; Fillat and Garetto, 2015). We argue that cultural diversity stemmed from geographical operations contributes to this increased risk. We find that cultural diversity is associated with both operational uncertainty regarding cash flows and informational uncertainties concerning cash flow estimation. The results provide support for Fillat et al. (2015) and Fillat and Garetto (2015) and suggest that investors demand higher returns from multinational firms that operate in culturally unfamiliar countries.

Biography:

John Fan Zhang obtained his PhD degree in 2018 from Auckland University of Technology with a thesis title, “The financial impact of cultural diversity on multinational firms”. In this thesis, Doctor Zhang investigates the influence of national cultural diversity on multinational entrepreneurs (MNEs). Culture is a significant influencing factor on the financial aspects of MNEs. It not only indirectly affects MNEs through formal institutions such as legal and political frameworks, but also directly governs the daily routine of MNEs through individual values, norms, and preferences. Nowadays, MNEs play an increasingly large and important role in the economic development of global markets. MNEs dynamically establish operations in different countries and economies, where national cultures may vary from one another. In this case, MNEs need to develop management and financial practices for each subsidiary in accordance with the national culture in which the subsidiary is operating, this would unavoidably influence financial decisions, performance, and value of the MNEs. Understanding the effect of cultural diversity on a firm is not only important to academic researchers but also matters to managers and investors. Doctor John Fan Zhang has also been a CFA chartholder since 2012.