DataGiri is back with its February meetup with Morgan Stanley where we discuss trends and updates in Data Science from the industry

Date : Saturday, 24th February, 2018, 2:30PM - 5:30PM

Venue : Bldg. No. 5, Athena, MDP Road, Sector 30, Mindspace, Malad West, Mumbai, Maharashtra 400090

Speaker : Shailesh Kosambia
Topic : How Machine Learning Is Helping Morgan Stanley Better Understand Client Needs
Agenda - Morgan Stanley has 16,000 financial advisors (FAs), who historically have maintained strong relationships with their investor clients through such traditional channels as face-to-face meetings and phone calls. However, the firm knows that these labor-intensive channels limit the number of possible relationships and appeal primarily to older investors.

So Morgan Stanley’s wealth management business unit has introduced “Next Best Action” system (NBA) that FAs could use to make their advice both more efficient and more effective. This system employs machine learning to match investment possibilities to client preferences. The next best action system at Morgan Stanley is focused on three separate objectives :

• A set of investment insights and choices for clients. In most existing machine advice, the recommended investments are strictly passive, that is, mutual funds or exchange-traded funds. The Morgan Stanley system can offer those if the client prefers them, but can also present individual stocks or bonds based on the firm’s research.
• The second aspect of the system is to provide operational alerts. These might include margin calls, low-cash-balance alerts, or notifications of significant increases or decreases in the client’s portfolio. They might also include noteworthy events in financial markets, such as the aforementioned Brexit vote.
• Content on life events. If, for example, a client had a child with a certain illness, the system could recommend the best local hospitals, schools, and financial strategies for dealing with the illness.

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Speaker : Shamit Verma
Topic : Use cases of Machine Learning in Financial Services at Morgan Stanley
Agenda -

• Use cases in Financial Services
• Financial Language Models
• Named Entity Recognition (and tagging entities with Unique Identifiers)
• Information Extraction
• Self-service machine learning
• Trend analysis and forecasting
• Word Vectors
• Document Vectors
• Relationship Extraction
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  Speaker : Rangarajan Vasudevan
  Topic : Data science beyond the hype
  Agenda -
  Data is all the rage and data science itself a much hyped skill. While a lot of seminal work has happened of late in creating significant value by rigorous application of data science techniques in many areas of human life, popular media has tended to extrapolate from early stage prototypes or niche applications to exaggerated visions of what the future holds for entire industries. This talk provides a practical view of data science for early-stage engineers looking to make a foray in to this exciting specialization. The talk argues that data science is by no means the most important skill but is indeed one of few core skill sets that are needed to make a difference. The talk explores the question of what does it take for data to bring value for a business and the role that early (and even experienced) engineers play in shaping such applications, in the form of anecdotes accumulated through the last 12 years of doing massive-scale data science across the world.
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Please Note - You will be required to show your ID proofs at the venue.

The event is FREE of cost to attend
RSVP now to reserve your spot at the event!!