Portfolio Management Using Artificial Intelligence

2020 ◽  
pp. 207-215
Author(s):  
Rakshit Gupta ◽  
Yogesh Mahajan ◽  
Punit Mukesh Ahuja ◽  
Jyoti Ramteke
Keyword(s):  
Artificial Intelligence ◽  
Portfolio Management

scholarly journals Early Adopters to Early Majority - What's Driving the Artificial Intelligence and Machine Learning Powered Transformation in Financial Services?

2021 ◽  
Vol 12 (4) ◽  
pp. 43
Author(s):  
Srikrishna Chintalapati
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Portfolio Management ◽  
Value Chain ◽  
Financial Services ◽  
Retail Banking ◽  
Early Adopters ◽  
Customer Life Cycle ◽  
Digital Disruption ◽  
The Impact

From retail banking to corporate banking, from property and casualty to personal lines, and from portfolio management to trade processing, the next wave of digital disruption in financial services has been unleashed by the concepts and applications of Artificial Intelligence (AI) and Machine Learning (ML). Together, AI and ML are undoubtedly creating one of the largest technological transformations the world has ever witnessed. Within the advanced streams of research in AI and ML, human intelligence blended with the cognitive reasoning of machines is finally out of the labs and into real-time applications. The Financial Services sector is one of the early adopters of this revolution and arguably much ahead of its leverage compared to other sectors. Built on the conceptual foundations of Innovation diffusion, and a contemporary perspective of enterprise customer life-cycle journey across the AI-value chain defined by McKinsey Global Institute (2017), the current study attempts to highlight the features and use-cases of early-adopters of this transformation. With the theoretical underpinning of technology adoption lifecycle, this paper is an earnest attempt to comment on how AI and ML have been significantly transforming the Financial Services market space from the lens of a domain practitioner. The findings of this study would be of particular relevance to the subject matter experts, Industry analysts, academicians, and researchers focussed on studying the impact of AI and ML in the financial services industry.

Artificial Intelligence in Portfolio Management

2002 ◽  
pp. 403-409 ◽  
Author(s):  
Man-Chung Chan ◽  
Chi-Cheong Wong ◽  
W. F. Tse ◽  
Bernard K.-S. Cheung ◽  
Gordon Y.-N. Tang
Keyword(s):  
Artificial Intelligence ◽  
Portfolio Management

scholarly journals Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art

2015 ◽  
Vol 5 (2) ◽  
pp. 121-139 ◽  
Author(s):  
Opeyemi Bello ◽  
Javier Holzmann ◽  
Tanveer Yaqoob ◽  
Catalin Teodoriu
Keyword(s):  
Artificial Intelligence ◽  
Portfolio Management ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Petroleum Exploration ◽  
Well Performance ◽  
Gas Industry ◽  
Well Production ◽  
Personnel Needs ◽  
Reservoir Simulator

AbstractArtificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses.Artificial Intelligence (AI) is an area of great interest and significance in petroleum exploration and production. Over the years, it has made an impact in the industry, and the application has continued to grow within the oil and gas industry. The application in E & P industry has more than 16 years of history with first application dated 1989, for well log interpretation; drill bit diagnosis using neural networks and intelligent reservoir simulator interface. It has been propounded in solving many problems in the oil and gas industry which includes, seismic pattern recognition, reservoir characterisation, permeability and porosity prediction, prediction of PVT properties, drill bits diagnosis, estimating pressure drop in pipes and wells, optimization of well production, well performance, portfolio management and general decision making operations and many more.This paper reviews and analyzes the successful application of artificial intelligence techniques as related to one of the major aspects of the oil and gas industry, drilling capturing the level of application and trend in the industry. A summary of various papers and reports associated with artificial intelligence applications and it limitations will be highlighted. This analysis is expected to contribute to further development of this technique and also determine the neglected areas in the field.

Artificial Intelligence

2009 ◽  
Author(s):  
David L. Poole ◽  
Alan K. Mackworth
Keyword(s):  
Artificial Intelligence
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