CASE STUDY
AI FOR FINANCIAL SERVICES
Stocks Crypto Fx Currencies
In capital markets, sentiment is everything. Imagine, as a trader, being able to deploy thousands of analysts to read every social post, news report and analyst reports, in real-time, before trading. That’s what ElliSense’s AI does.
Detailed Description
Powered by artificial intelligence, this simple to use application gives an accurate reading of market sentiment that financial professionals can use to inform every trade. Market sentiment changes quickly and often unpredictably. The smartest trades, whether stocks, crypto, or forex, rely on up-to-the-minute sentiment analysis. Time is of the essence, which is why ElliSense gathers and digests many thousands of data points per second to give traders the intel they need to make smart decisions
The Artificial Intelligence (AI) analyses messages from financial analysts for various assets including currencies, crypto, and stocks. Using natural language processing (NLP) and sentiment analysis, the AI, with its 100+ million parameters, can classify analyst reports, news and social posts as bearish, neutral and bullish. The dataset uses over a quarter of a million messages that have been manually labeled to train the AI model. Currently it achieves over 97% accuracy and is getting better by the day.
After aggregating this information, ElliSense produces real-time indices of whether the market considers an asset to be bearish, neutral or bullish. While not a crystal ball, the results are surprisingly effective.
The ElliSense application is accessible from a laptop, tablet and mobile device or via an API framework for trading robots.
400+
Analysts
30 SECONDS
After a post is published, it is analyzed and integrated into our indices
25M+
Posts analysed per month
Classifies posts with over
97% ACCURACY
Dataset of more than a
QUARTER MILLION
Posts
Artificial Intelligence with
100+ MILLION
parameters
Main Features
User management (On boarding, Off Boarding , communication tools, etc )
CRM connectivity to Hubspot
Payments via Stripe
Connection to real time market feeds
Connection to multiple real-time social posts & data sources
Real time market news feeds
Desktop app, mobile app and partner widget interfaces
System Monitoring tool (uptime, capacity, optimisation)
Dedicated AI neural networks using NLP and Sentiment Analysis to analyze social posts
Use of different AI technics to optimize meta parameters
Producing real-time sentiment indices for 350+ financial assets
Alerts and notification management
API framework for developers of trading robots
Historical data visualization
CMS to manage FAQ, tutorials etc.
Software & Technical Architecture
The frontend is written in React JS, using the very latest technology such as browser notifications. This frontend exchanges information with the backend through REST API for the classic service requests, and with Websockets for the real-time information.
The backend is written in Java for the standard services. Each service is encapsulated in docker containers for scalability and portability.
We have used different AI techniques in building the engine for this product. All development was done in Python, using packages such as PyTorch and TensorFlow.
We have trained a Neural Network made of 125 Million parameters to analyze and interpret social media posts about financial assets. We were able to reach an unprecedented 97+% accuracy. This Neural Network uses the same technology as ChatGPT.
We have also used some other AI technology (regression, Montecarlo, topic modelling etc) for example to optimize the sentiment index hyper parameters or to categorize social posts.
UX & Product Design
The challenge with data is how to present it and how to explain it. Users need to trust an interface and we can build trust by having an intuitive, easy to use UI that fits into their workflows.
Like any great UX process, we start with discovery and understand what our users need. We let them tell us about the good and the bad of their current tools and watch and ask them about their daily tasks to understand the define the problem that needs solving. From there we come up with solutions and test test test, iterate, iterate, iterate until we give them the product solution that they didn’t know they needed.
With complex data applications we need to let them explore, but also need to give them quick bites of information that they can use to get their work done. This paradigm is know as the SNACK, BITE, MEAL approach.
Our UX Design Process
Discovery User / Stakeholder interviews, competitive research, workshops, heuristic evaluations, persona development
Define Define the problem(s) so we know what we need to solve for using user centered design thinking
Design Design solutions for the problem using wireframes & rapid prototyping tools
Test Test our solutions in low fidelity / workshops and monitor results to see if we are on the right track, if not return to define
Production Once we have our solution tested and are moving forward, we design in high fidelity for handoff to development
Consulting Engagement
In order to deliver ElliSense, Spring AI set up teams of experts in the disciplines of:
C Level
Oversight
UX & Product
Design
Frontend
Development
Backend
Development
Cloud &
Security
Data
Science
Product
Owner
Working closely with the client, our team was able to successfully deliver this ground breaking project. The first features were delivered within a few weeks. To ensure a successful project, we made investments in methodologies.
With design and project management we used well known methodologies, for AI we created and customised our own methodology from standard Agile.
THE FINAL PROJECT
