How We Developed an MVP for the Diligence Fund Distribution Platform 

# Fintech # Web development
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Highlights

  • Digital Information and Connection Hub Development: delivering the first MVP version of the diligence fund distribution platform for our client, enabling him to pitch to investors and raise funds for the next iterations of the startup.
  • Implementation of MVPSMART or Hybrid Search: leveraging historical data on user interactions to devise a data collection strategy aimed at constructing a more sophisticated matching algorithm.
  • Intelligent Search Implementation: providing our Artificial Intelligence and Machine Learning expertise to introduce a highly efficient matching process for users of the product, as well as smart search functionality.

Client

The client, possessing nearly twenty years of expertise in asset and fund management, has created a new diligence fund distribution platform. The company was established to tackle the challenge faced by asset managers in efficiently reaching out to financial advisors on a large scale and serves as a connection point between asset managers and financial advisors.

The goal of the client’s venture is to offer technologically advanced distribution services driven by data. These services encompass various asset classes such as equities, fixed incomes, commodities, alternatives, and emerging new asset classes. They are provided through different investment vehicles like separately managed accounts, mutual funds, interval funds, exchange-traded funds, and limited partner structures. The primary objective is to simplify the process of fund discovery and distribution.

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Product

The web solution is tailored to give financial advisors direct access to the precise products their clients demand. The product consists of key features including a landing page, a news thread, search and filtering capabilities, user cabinets, a mechanism for matching community members based on business objectives and experience, and a messaging system for platform users.

The overarching goal of the product is to harness artificial intelligence in creating a seamless platform that bridges asset managers and financial advisors. It provides them with data-driven insights and efficient tools to streamline the process of fund discovery and distribution, thereby eliminating friction in the process.

Goals and objectives

The client contacted us to enhance connectivity between asset managers and financial advisors and to improve data-driven insights for more informed decision-making. To make that possible, we set the following goals:

  • Build an MVP of a Connection Hub for Experts in the Investment Industry: Create a centralized platform that  fosters a collaborative environment and facilitates the formation of partnerships.
  • Empower the Platform with Effective Search Mechanism: Leverage AI/ML technologies to build a web platform that will help asset managers easily match with wealth managers, shortening their time for searching for a reliable business partner. 
  • Track User Data Activities: Provide the client with information on user activities to find possible bottlenecks and optimize the platform’s performance in the future. 
  • Drive Conversion Rates: Achieve increased efficiency in matching asset managers with relevant financial advisors to maximize financial deals.
  • Enhance User Engagement and Satisfaction: Implement a seamless, AI-driven platform that improves the overall user experience for both asset managers and financial advisors.

Project challenge

  1. Limited to No User Data and History:
    Develop a solution to match investors with wealth managers and asset managers, despite the initial lack of necessary information.
  2. Noise Data:
    Manage the crawled data properly, as the AI-powered data crawling process produces a lot of irrelevant data that should be filtered to obtain accurate end results.

Solution

The most interesting part of this project was introducing AI SMART search for matching members of the community when they were looking for potential business partners with common goals. 

In contrast to general search, which typically filters users by basic information such as country, state, years of experience, generation, gender, etc., smart search leverages machine learning and AI to generate embeddings (numerical representations) of data items such as documents, images etc.. These embeddings capture semantic information about the data, enabling more refined and filtered search results based on their semantic similarity. The AI Search included other variables that could be of value when choosing the potential business partner and increased the likelihood of positive cooperation like vehicle preferences, ESG, Asset class, AUM, and specialty as well as the description text in the bio of each user. 

We started by working on the PoC concept of the ML module for matching community members (asset managers, wealth managers, and investors), outlining ideas for matching algorithm approaches based on user profiles. When historical data about successful matching between platform users were accumulated, MVPSMART or Hybrid search and data embeddings were enforced with historical data about interactions between users. Finally, it came to creating a strategy for data collection in the future for building a more advanced matching algorithm.

After the ML module PoC was completed, we received a green light from the client to start on the software development of Digital Information and Connection Hub for Investment, which included implementing a web crawling mechanism with Apify, developing a news line and chat, adding HubSpot CRM integration and user tracking activities with Heap io, Segment, and Google Analytics. 

Tech Stack

Web
  • React.js WebReact.js
  • TypeScript  WebTypeScript
  • Material UI WebMaterial UI
API
  • Java API Java
  • Quarkus API Quarkus
  • GraalVM API GraalVM
  • Hibernate API Hibernate
  • PostgreSQL API PostgreSQL
Third Parties
  • Auth0 Third PartiesAuth0
  • Apify Third PartiesApify
  • Segment Third PartiesSegment
  • Heap.io Third PartiesHeap.io
  • HubSpot Third PartiesHubSpot
  • Google Analytics  Third PartiesGoogle Analytics

Our results

We successfully delivered the planned functionality within the allocated time and budget. This led to enhancements in fund discovery and distribution processes, minimizing manual efforts and broadening asset managers’ access to financial advisors, thereby potentially expanding client bases. Additionally, we provided data-driven insights to facilitate better decision-making.

  1. Introduced AI-based Matching Process for Wealth and Asset Managers: developed key product functionality by effectively implementing AI/ML technologies. 
  2. Created a Robust Web Crawling Mechanism in 1 Week: successfully implemented it with Apify and resolved the challenge of noise data. 
  3. Developed a Fully-Functional Website: delivered the first version of the website including essential features like User Cabinet, NLP-powered Search, and Filters. 
  4. Enhanced User Experience: added personalized recommendations. Thanks to our fruitful collaboration, SPD Technology has contributed to the overall growth of the business, potentially resulting in heightened revenue and cost savings.
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