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Springbox AI

springbox.ai

Stage

Unattributed | Alive

Total Raised

$2M

Last Raised

$2M | 2 yrs ago

About Springbox AI

Springbox AI is a financial forecasting application designed to replace financial market investment service and aimed at the average financial markets trader

Headquarters Location

Switzerland

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Expert Collections containing Springbox AI

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

Springbox AI is included in 3 Expert Collections, including Artificial Intelligence.

A

Artificial Intelligence

9,442 items

This collection includes startups selling AI SaaS, using AI algorithms to develop their core products, and those developing hardware to support AI workloads.

F

Fintech

4,872 items

Track and capture company information and workflow.

D

Digital Health

8,838 items

Startups recreating how healthcare is delivered

Latest Springbox AI News

Creating a prediction machine for the financial markets

Feb 19, 2021

Creating a prediction machine for the financial markets 18th February 2021 Fayze Bouaouid is co-founder and CEO of financial intelligence application Springbox AI . He has a Master’s of Science in Banking and Finance and nearly two decades of experience in banking and asset management. Artificial intelligence and machine-learning technologies have evolved a lot over the past decade and have been useful to many people and businesses, especially in the realm of finance, banking, investment and trading. In these industries, there are many activities that machines can perform better and faster than humans, such as calculations and financial reporting, as long as the machines are given the complete data. The AI tools being built by humans today are becoming another level more robust in their ability to predict trends, provide complex analysis, and execute automations faster and cheaper than humans. However, there has not been an AI-powered machine built yet that can trade on its own. There are many activities that machines can perform better and faster than humans, such as calculations and financial reporting, as long as the machines are given the complete data. Even if it was possible to train such a system that could replace human judgment, there would still be a margin of error, as well as some things that are only understandable by human beings. Humans are still ultimately responsible for the design of AI-based prediction machines, and progress can only happen with their input. Data is the backbone of any prediction machine Building an AI-based prediction machine initially requires an understanding of the problem being solved and the requirements of the user. After that, it’s important to select the machine-learning technique that will be implemented, based on what the machine will do. There are three techniques: supervised learning (learning from examples), unsupervised learning (learning to identify common patterns), and reinforcement learning (learning based on the concept of gamification). After the technique is identified, it’s time to implement a machine-learning model. For “time series forecasting” — which involves making predictions about the future — long short-term memory (LSTM) with sequence to sequence (Seq2Seq) models can be used. LSTM networks are especially suited to making predictions based on a series of data points indexed in time order. Even simple convolutional neural networks, applicable to image and video recognition, or recurrent neural networks, applicable to handwriting and speech recognition, can be used.

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Springbox AI Frequently Asked Questions (FAQ)

  • What is Springbox AI's latest funding round?

    Springbox AI's latest funding round is Unattributed.

  • How much did Springbox AI raise?

    Springbox AI raised a total of $2M.

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