  Lead recommendations for financial products such as funds or ETF is
potentially challenging in investment space due to changing market scenarios,
and difficulty in capturing financial holder's mindset and their philosophy.
Current methods surface leads based on certain product categorization and
attributes like returns, fees, category etc. to suggest similar product to
investors which may not capture the holder's investment behavior holistically.
Other reported works does subjective analysis of institutional holder's
ideology. This paper proposes a comprehensive data driven framework for
developing a lead recommendations system in holder's space for financial
products like funds by using transactional history, asset flows and product
specific attributes. The system assumes holder's interest implicitly by
considering all investment transactions made and collects possible meta
information to detect holder's investment profile/persona like investment
anticipation and investment behavior. This paper focusses on holder
recommendation component of framework which employs a bi-partite graph
representation of financial holders and funds using variety of attributes and
further employs GraphSage model for learning representations followed by link
prediction model for ranking recommendation for future period. The performance
of the proposed approach is compared with baseline model i.e., content-based
filtering approach on metric hits at Top-k (50, 100, 200) recommendations. We
found that the proposed graph ML solution outperform baseline by absolute 42%,
22% and 14% with a look ahead bias and by absolute 18%, 19% and 18% on
completely unseen holders in terms of hit rate for top-k recommendations: 50,
100 and 200 respectively.
