Build an automated B2B investor matching platform for fund managers

The aim and scope of this project is to build an automated technology driven platform for the client to find investors. It is an automated platform to find investors for the client with following features

1. Intelligent investors selection (investor matching):

When the investor database is large, the platform can find suitable investors through structured matching. The platform then takes over the task of manually screening, contacting, and arranging the calls. It uses a keyword in request to match. While the investor accepts, rejects and attends video calls, the platform learns which investor is active and which clients provide a high success rate by providing well-demanded investment products. The platform develops Artificial Intelligence through the screening of interactions between clients and investors.

2. Show the detailed view about the matched investor for premium users and limited view for clients

3. Uploading the match investor contacts to Hunter to start investment request campaign

4. Automatically sends out emails to suitable investors and invites the investor for a video conference call with the client.

a. Platform supports synchronised calendars for clients and investors.

b. After a suitable time has been selected by the investor, the platform confirms the meeting automatically. The platform creates a unique web conference for this meeting.

c. Generates an agenda to discuss before the meeting and share the profile of their counterparts before the meeting

d. The platform will help the client with checkboxes, presentation scripts and questions for the investor to conduct optimal presentations and to convince the investor about their investment product

e. Facilitate Video call which has automated agenda visible at the call time and have facility to give feedback related to the agenda points

f. Supports synchronized calendar for clients and investors

5. Payment Option

6. Integrate AI model

a. Track Email responses and Communication

b. Data-driven recommendation algorithms use attributes of clients and Investors to match based on similarity, relevance and fit. A content-based recommendation module uses the data available on investors and the information provided by clients for an initial match. Over time, using historical matching data, a collaborative filtering module will allow for improved matching of clients and Investors that have successfully been connected by the platform. Both these recommendation modules can then be combined into a hybrid recommender system that uses both the attributes of investors, as well as their past history to provide ever more accurate matches.

c. To further improve the AI matching and recommendation framework, there are two approaches that will be explored. Using targeted feedback forms that will be filled out by GPs after each call between a clients and Investors, as well as keyword-based voice recognition during the clients and Investors calls. Each of these approaches allows for collecting massive and granular datasets that can be mined using AI algorithms for learning semantics and further improving the matching algorithm

7. Show a step-by-step process to clients in a Kanban view to progress the capital raising process. Here the platform sends out automatic emails to the investor with relevant documents, such as the pitch deck, financial model, agreements, etc. Here, the platform sends out emails automatically in optimized time intervals. E.g., the investor will receive relevant documents and the platform

a. Sends out teaser (within 1 day)

b. Sends out company presentation (after 2 days)

c. Sends out fund presentation (after 4 days)

d. Sends out financial model (after 7 days)

e. Sends out Limited Partnership Agreement or Term sheet (after 10 days), etc

Note : Investor and Client contacts are managed in separate Pipedrive and you are expected to use Hunter for Email campaign.

Skills: Python, Java, JavaScript, Web Development, Financial Software Development

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About the Employer:
( 7 reviews ) London, United Kingdom

Project ID: #31919778

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