Frequently Asked
Questions

An algorithm is a sequence of instructions or commands carried out in a systematic way with the aim of solving a problem or performing a task.
The word “algorithm” refers to al-Khwārizmī , a famous 9th-century Persian mathematician who first defined the rules of algebra for its universal and applicable use.

Artificial Intelligence (AI) is a branch of computer science that deals with the creation of intelligent agents, which are systems that can potentially reason, learn, and act autonomously.

Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make predictions with minimal human intervention. There are many ways that machines aim to learn these underlying patterns.
If you want to learn more about how machine learning helps us improve the way investment is managed, check out our Research Line.

Deep learning is a subset of machine learning that is concerned with algorithms loosely inspired by the structure and function of the brain called Artificial Neural Networks. With accelerated computing and large data sets, deep learning algorithms are able to self-learn highly nonlinear patterns and make accurate predictions on unseen data.
Techniques such as deep learning delegate to algorithms the choice of the best functional form or a probability distribution, with significantly better with fewer a priori assumptions.
Deep learning technology underpins many of the predictive models that are included in Axyon AI products.

Natural Language Processing is a branch of artificial intelligence that uses machine learning to help computers learn the meaning of texts. It is used to fill the gap between human communication and computer understanding and has several applications.
Across the financial industry, NLP is used from retail banking to corporate investment: its applications range from risk assessment, sentiment analysis, portfolio assessment and document classification, among others.

Artificial Neural Networks are a type of AI algorithm that is loosely modelled after the brain of mammals. They are composed of a large graph of interconnected processing nodes, or artificial neurons, that can learn to recognize patterns in the provided data. The learning process depends on the given task, such as classifying objects into categories, denoising dirty data, etc.

Supervised Learning is the machine learning approach defined by its use of labelled datasets to train algorithms to classify data and predict outcomes.
With a labelled dataset, the algorithm knows the correct output for each input, and its task will be to generalise this knowledge to new unlabelled inputs.
Unsupervised Learning is a type of machine learning in which the algorithms are provided with data that does not contain any labels or explicit instructions on what to do with it. The goal is for the learning algorithm to find structure in the input data on its own.

Identifying these hidden patterns helps in clustering, association, and detection of anomalies and errors in data.

In theory, the term Fintech is quite simple as it comes from the combination of the words FINancial and TECHnology. In practice, however, the true meaning of what is fintech goes far beyond. Fintech is used to refer to startups or companies that develop fully digital financial products, in which the use of technology is the main differentiator compared to traditional companies in the sector. It describes any technology that enhances financial services or enables new financial products and services to be offered. Fintech is a broad category that encompasses many different technologies, but the primary objectives are to change and improve the way consumers and businesses access their finances.

It does not require any coding or programming knowledge to access our web-based AI platform Axyon IRIS®. It has a user-friendly interface and can be accessed online from multiple devices.
Alternatively, its results can be programmatically integrated into your investment process by using APIs or SFTP.
Learn more here about how Axyon IRIS® can help you improve investments are managed.

AI and machine learning enable fund and asset managers to save time and manage risk to protect their investments. It can also help businesses navigate challenging conditions by detecting anomalies in the market before any crisis occurs. By implementing AI, fund and asset managers can also monetise data and improve automation from the front to the back office.

We have a very strong connection with academia and we value the contribution of research to the development of our AI solutions.
If you are a student, please fill in the form below with detailed information about your project and you will be answered as soon as possible.

There are three fundamental differences in the way of making financial predictions between artificial intelligence (AI) and the traditional statistical methods that have been used for decades by quantitative finance.
– the mass of data available is analyzed
– by the size of the computing power used. In both cases, they are certainly superior when artificial intelligence is used.
– the ability to extract relationships from the data. While traditional models are usually based on the results of the data available and on the evident relationships between them, modern technologies that make use of artificial intelligence are flexible when they have to analyze these relationships. The most advanced technologies, such as deep learning, delegate the choice of the best functional form to be adopted to algorithms and make the relationships that emerge from the available data significantly more precise.