Artificial intelligence and financial services

Pau, L. F.

doi:10.1109/69.87994

Cited by 19 publications

(12 citation statements)

References 15 publications

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“…In addition, conflict resolution strategies are also summarized in the context of economics and related areas, including first found, least (or most) recently used, antecedent (or consequent) ordered, most complex first, simplest first and global priority. The importance of specifying conflict resolution strategies to user satisfaction and confidence was also noticed (Pau, 1991), but with no further elaboration.…”

Section: Controlling Of Inferencementioning

confidence: 88%

“…As Pau (1991) identified, the motivations for the use of AI in economics and related areas fall into five categories. Noteworthily, the ways of doing inference associated with them are also categorized.…”

Section: Controlling Of Inferencementioning

confidence: 99%

“…This paper is motivated from two considerations: (1) an original study on user modeling from computer science and information systems perspective which may find important applications in the areas of economics and management, combined with (2) a commentary on AI and management, particularly on two very instructive papers on the role of AI in management (Pau, 1990(Pau, , 1991, for which we are particularly interested in the discussion on reasoning procedures and inference control. We have also noticed that the discussion on inference control is not yet associated with the consideration of user modeling.…”

Section: Introductionmentioning

confidence: 99%

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User modeling for flexible inference control and its relevance to decision-making in economics and management

Chen

1993

Comput Econ

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Abstract. Controlling the order of inference is an important issue for knowledge-based systems in economics and management. Separate from this issue, there is a recent spur on user modeling. In this paper, we establish the connection between these two issues. We investigate the need for flexible control of inference from the perspective of user modelling, and outline the approaches to realize this kind of connection.

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Section: Controlling Of Inferencementioning

confidence: 88%

Section: Controlling Of Inferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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User modeling for flexible inference control and its relevance to decision-making in economics and management

Chen

1993

Comput Econ

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“…The application of AI in financial analytics is also affecting the practice of automated trading [4]. Markets and trades generally comprised a huge amount of well-structured data that lies the foundation from the application of a data-driven model rather than a knowledge-based technique [5]. Experts and decision support systems (DSS) using a portion of AI are widely spread in different domains [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Capital Markets: Decision Support System for Outcome Analysis

Rosati

Romeo

Goday³

et al. 2020

IEEE Access

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Decision support systems using Artificial Intelligence in the context of financial services includes different application ranging from investment advice to financial trading. The analysis of order flow provides many challenges that can be addressed by Machine Learning (ML) techniques in order to determine an optimal dynamic trading strategy. The first step in this direction is represented by the outcome analysis of order flow: the model should identify strong predictors that determine a positive/negative outcome. The aim of this work is the proposal of a closed-loop ML approach based on decision tree (DT) model to perform outcome analysis on financial trading data. The overall approach is integrated in a Decision Support System for Outcome Analysis (DSS-OA). Taking into account the model complexity, the DT algorithm enables to generate explanations that allow the user to understand (i) how this outcome is reached (decision rules) and (ii) the most discriminative outcome predictors (feature importance). The closed-loop approach allows the users to interact directly with the proposed DSS-OA by retraining the algorithm with the goal to a finer-grained outcome analysis. The experimental results and comparisons demonstrated high-interpretability and predictive performance of the proposed DSS-OA by providing a valid and fast system for outcome analysis on financial trading data. Moreover, the Proof of Concept evaluation demonstrated the impact of the proposed DSS-OA in the outcome analysis scenario.

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“…The algorithm provides the guidelines for rules that are to be followed in the learning process. The target here is to look for a solution for network parameters that yield optimised cost function [10,11]. Training is performed by providing the algorithm with complete set of training examples presented and processed once [12][13][14].…”

Section: Artificial Intelligence/machine Learningmentioning

confidence: 99%

Artificial Intelligence in Biological Data

Chakraborty¹,

Choudhury²

2017

J Inform Tech Softw Eng

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Artificial Intelligence (AI) or Machine learning in present era, serves as the primary choice for data mining and big data analysis. With effective learning and adaptation model, it provides solutions to several engineering applications. These include techniques such as Artificial Neural Network modelling, Reasoning based decision algorithms, Simulation models, DNA computing and Quantum computing among several others. With the application of AI in Biomedical research, the fuzziness and randomness in handling such type of data has significantly reduced. Rapid technological advancements have helped AI techniques evolve in manner which promotes handling such fuzzy data effectively and much more conveniently. The review presents a comprehensive view of machine learning and AI computing models, advanced data analytics and optimisation approaches used in Bioengineering such as Drug Designing and Analysis, Medical imaging, biologically inspired learning and adaption for analytics, etc.

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Artificial intelligence and financial services

Cited by 19 publications

References 15 publications

User modeling for flexible inference control and its relevance to decision-making in economics and management

User modeling for flexible inference control and its relevance to decision-making in economics and management

Machine Learning in Capital Markets: Decision Support System for Outcome Analysis

Artificial Intelligence in Biological Data

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