Financial decision support: an overview of developments and recent trends

Zopounidis, Constantin; Doumpos, Michael; Niklis, Dimitrios

doi:10.1007/s40070-018-0078-3

Cited by 17 publications

(8 citation statements)

References 78 publications

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“…The fintech concept (Schueffel 2016;Gai et al 2018;Das & Ali 2020) currently transcends electronic banking and consumer digitisation services and focuses on the development and implementation of innovative financial instruments to meet the financial requirements of the end-users. Digital technologies (Skinner 2018;Jagtiani & Kose 2018;Tanda & Schena 2019) and their impact on consumer decisions currently stand at the forefront of international consumer policy discourse (Zopounidis et al 2018;Bhatia, 2019). The use of modern technologies presents a clear opportunity to accelerate the transformation of the banking sector and give users greater control over their finances and increase the value of their investments.…”

Section: Introductionmentioning

confidence: 99%

Attitudes towards artificial intligence in the area of personal financial planning: a case study of selected countries

Waliszewski¹,

Warchlewska²

2020

JESI

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The financial sector's focus on simplifying decision-making processes, maximally shortening procedures via cooperation with the fintech industry, robotisation and the use of artificial intelligence are a response to market needs and becoming an important element of how financial service groups compete on the market. The theory of consumer behaviour assumes that consumers have needs that they will hierarchise, and that they will make choices to maximise their own satisfaction. The purpose of the article is to diagnose the sociological and economic determinants underlying consumer satisfaction in terms of planning personal finances using modern technologies. Comparisons of international data were conducted via quantitative analysis of robo-advice using Mann-Whitney U tests, the Chi-square test and Spearman's rho correlation. The survey results show that the majority of socioeconomic characteristics of households are statistically significant when considering satisfaction with robo-advisory financial services and spending analysis, as well as with artificial intelligence suggesting improvements. This study is a contribution to the literature on consumer behaviour in the modern world.

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Section: Introductionmentioning

confidence: 99%

Attitudes towards artificial intligence in the area of personal financial planning: a case study of selected countries

Waliszewski¹,

Warchlewska²

2020

JESI

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“…Supervised and unsupervised learning techniques for classification and regression are the ones most commonly used, along with intelligent optimization systems (e.g., reinforcement learning, Bloembergen, Tuyls, Hennes, & Kaisers, 2015). Decision analysis and decision support systems: In contrast to AI approaches, which usually adopt automated procedures for decision making, decision analysis techniques rely on the domain knowledge and expertise of financial decision makers (Zopounidis, Doumpos, & Niklis, 2018). Incorporating this type of information to financial models enhances their comprehensibility and makes them more realistic, which may not be fully covered by pure data-driven approaches.…”

Section: Financial Decision Makingmentioning

confidence: 99%

Computational approaches and data analytics in financial services: A literature review

Andriosopoulos

Doumpos

Pardalos

et al. 2019

Journal of the Operational Research Society

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The level of modeling sophistication in financial services has increased considerably over the years. Nowadays, the complexity of financial problems and the vast amount of data require an engineering approach based on analytical modeling tools for planning, decision making, reporting, and supervisory control. This article provides an overview of the main financial applications of computational and data analytics approaches, focusing on the coverage of the recent developments and trends. The overview covers different methodological tools and their uses in areas, such as portfolio management, credit analysis, banking, and insurance.

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“…As in water resources systems analysis, some researchers have attempted to provide more realistic decision support using multi‐objective formulations (de Almeida‐Filho et al., 2020; Marqués et al., 2020; Salas‐Molina et al., 2018; Zopounidis et al., 2015), model‐free information (Sun et al., 2011), heuristic solution methods (Aguilar‐Rivera et al., 2015; da Costa Moraes & Nagano, 2013; Ponsich et al., 2013; Tapia & Coello Coello, 2007), and visual analytics (Flood et al., 2016; Savikhin et al., 2011). Beyond the academic literature, the use of quantitative decision support tools by financial firms (e.g., banks, hedge funds, insurers) has proliferated in recent years, driven by growth in computing power, big data, algorithms, and visualization software (Fabozzi et al., 2007; Rundo et al., 2019; Zopounidis et al., 2018). However, these firms generally employ proprietary and highly problem‐specific technologies that are not readily adoptable by organizations outside of the financial sector, such as water and power utilities, which nevertheless face significant financial risks.…”

Section: Introductionmentioning

confidence: 99%

From Stream Flows to Cash Flows: Leveraging Evolutionary Multi‐Objective Direct Policy Search to Manage Hydrologic Financial Risks

Hamilton

Characklis

Reed

2022

Water Resources Research

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Reservoir control and financial risk management share strong similarities. The principal task in each is to reduce the risk of negative impacts from variable inflows (either hydrologic flows or cash flows), through the use of a buffer stock (either a reservoir or a reserve fund) that is filled in times of abundance and drawn down in times of scarcity (Figure 1). Other risk management tools may also be used to limit the impact of low-flow periods, but at a cost (e.g., water desalination or demand management for streamflow deficits, and borrowing or financial hedging for cash flow deficits). In both cases, the manager must make decisions under an array of uncertainties, and may need to navigate tradeoffs between conflicting objectives (e.g., flood control vs. water supply for reservoir control, risk vs. cost for financial risk management). And in both cases, as systems dynamically evolve, managers will have to adapt to new information as it becomes available. In other words, reservoir control and financial risk management can be formulated as very similar Markov Decision Processes (MDPs) (Bertsekas, 2019;Powell, 2019), whether managers attempt to solve this problem explicitly, using programmatic approaches such as stochastic dynamic programming, or implicitly, relying on expert specified rules. Additionally, reservoir control and financial risk management are strongly interdependent activities for water-reliant organizations in the Food-Energy-Water Nexus, such as hydropower producers, municipal water utilities, and irrigation districts (Cai et al., 2018;D'Odorico et al., 2018;Scanlon et al., 2017). Such organizations rely on water for the provision of services, and as a result, their revenues and/or costs can be highly dependent on hydrologic inflows (Blomfield & Plummer, 2014;Larson et al., 2012). This suggests that an understanding of complex water resource system dynamics can be used to better characterize and adaptively manage financial risks borne by water-reliant organizations.

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Financial decision support: an overview of developments and recent trends

Cited by 17 publications

References 78 publications

Attitudes towards artificial intligence in the area of personal financial planning: a case study of selected countries

Attitudes towards artificial intligence in the area of personal financial planning: a case study of selected countries

Computational approaches and data analytics in financial services: A literature review

From Stream Flows to Cash Flows: Leveraging Evolutionary Multi‐Objective Direct Policy Search to Manage Hydrologic Financial Risks

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