SURVEY ON ADVISOR INTELLIGENCE THROUGH PURCHASE PATTERNS AND SALES ANALYTICS

Gayatri K Pradhan; Sarath Gollapalli; JANAKI MEENA M; Syedibrahim Sp

doi:10.22159/ajpcr.2017.v10s1.19743

Authors

Gayatri K Pradhan School of Computing Sciences and Engineering, VIT University, Chennai, Tamil Nadu, India
Sarath Gollapalli Broadridge Financial Solutions, Hyderabad, India
JANAKI MEENA M School of Computing Sciences and Engineering, VIT University, Chennai, Tamil Nadu, India
Syedibrahim Sp School of Computing Sciences and Engineering, VIT University, Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.22159/ajpcr.2017.v10s1.19743

Keywords:

Big data analytics, Investment advisor, Pattern recognition, Evolutionary algorithm, Correlation, Leader-follower

Abstract

In mutual fund, an individual or a firm that is in the business of giving advice about securities to clients is an investment advisor. InvestmentÂ advisers are individuals or firms that receive compensation for giving advice on investing in stocks, bonds, mutual funds, or exchange-tradedÂ funds. Investment advisors manage portfolios of securities. Advisors can use new cognitive and analytics capabilities to better understandÂ their clients and needs and have a stronger ability to deepen relationships with a better portfolio. In this paper, we analyze data points for
each advisor, and distinguish the best prospects, obtain insight into their experience and credentials, and learn about their portfolio, in otherÂ words, to recognize the pattern of portfolio of the advisors. Such analysis helps the sales people to sell the fund company products to theÂ suitable advisors based on the nature of the product they want to sell. This is done by investigating what kind of products advisors have beenÂ buying, and what kind of products they might be looking for. This helps to increase the sales of the products as sales people will be reachingÂ the appropriate advisors.

Downloads

Download data is not yet available.

References

de la Fraga LG, Coello CA. A review of applications of evolutionary algorithms in pattern recognition. Pattern Recognition, Machine Intelligence and Biometrics. Berlin Heidelberg: Springer; 2011

Wu D, Ke Y, Yu JX, Yu PS, Chen LS. Detecting Leaders from Correlated Time Series. The Chinese University of Hong Kong,University of Illinois at Chicago, The Hong Kong University of Science and Technology.

Chandra B, Gupta M, Gupta MP. A Multivariate Time Series Clustering Approach for Crime Trends Prediction. New Delhi: Indian Institute of Technology Kanpur, Institute for Systems Studies and Analyses Metcalfe House, Indian Institute of Technology.

Kumar M, Patel NR, Woo J. Clustering seasonality patterns in the presence of errors. Proceedings of KDD â€™02. Edmonton, Alberta, Canada.