• The Fintica Team

PEERING THROUGH THE FOG: AI IN THE FINANCIAL SECTOR

The financial technology industry has always been an early adopter when it comes to revolutionary technology. From the mainframe computer, to fiber optic cabling, and now artificial intelligence... the sector has been on the forefront of these ground-breaking shifts.

With that said, AI has been thrown around as quite a broad term, encompassing everything from simple statistical models to advanced pattern recognition platforms. It’s an often misused term. So, throughout this recurring blog we will be diving into artificial intelligence, what it means, and how AI can fundamentally solve the most complex problems across capital markets, compliance, trading, fraud detection, and more.

To start, it’s important to understand the different models of artificial intelligence that exist today - specifically supervised vs unsupervised learning methodologies.

Supervised Learning

Supervised learning is a traditional form of machine learning, also referred to as deep learning, a term much of the industry has become familiar with. With deep learning, the system begins with category definition, which is followed by a tedious process of manually labeling a reference data set. This labeled data becomes the training data. Once that labor-intensive step is completed, a training model is built to generalize and classify new data based upon the specific parameters found within the training set.

Essentially the system is being repeatedly taught a specific task in order to enable its automatic reproduction in the future.

So let’s give a simple real world example: When a developer wishes to teach a machine to identify a cat with deep learning, they need to first find several thousand images with cats, manually tag the cats within each photo, and then pump these images into the machine learning system. Once this training is complete, the system should be able to detect and identify cat images that have something in common with the training set.

The Challenges

The downside here, in a broad sense, is that the creation of the training set takes a substantial amount of human power. But beyond that, the developer needed to define the task in advance. They had to decide what to look for (a cat), then find pictures of cats, label that specific data, and then teach the system to recognize it. But what if you’re not sure what you’re looking for, and you don’t have thousands of examples to use to train the system?

An instance of when this will be the case is in fraud pattern detection. Often these patterns cannot be predefined by developers (if they could, the developer would be able to prevent the fraud!), and there are very few examples to include in a training set. So the challenge becomes: how can a system identify patterns in the absence of rules or prior definition, and how can it be done with limited amounts of data? There lies the noted challenges that banks, agencies, credit card issuers, and many other companies are experiencing with deep learning today.

Unsupervised Learning

Unsupervised learning takes a fundamentally different approach, allowing it to respond to the challenges identified with deep learning. An unsupervised system begins with raw, unstructured and random unlabeled data. The AI first crawls across this random data and identifies any and all commonalities it can find. The system is then able to self-organize and cluster the information based on these commonalities. Clusters will vary in sizes depending on the data, and there will be levels of overlaps between the clusters.

The benefits of unsupervised learning methodologies go well beyond the reduction of manual tagging time and effort. The system can identify clusters and concepts that a developer may not have imagined. As it does not require prior definition, unsupervised AI is able to autonomously crawl through data, find patterns on its own, and bring these to the forefront.

As an unsupervised learning system does not require prior definitions of what one should seek within the data, the outcome often goes far beyond what would have been otherwise pre-defined. Additionally, as the system is able to make loose, broad connections, as well as more fine grain specific commonality detections, the platform is able to learn from substantially less data. Coupling the ability to learn from minimal data sets without the laborious manual tagging necessities found in deep learning, unsupervised Autonomous AI opens a world of possibilities.

A Fundamental Shift

The implementation of unsupervised learning will fundamentally change how financial markets operate, how compliance teams innovate, the ways funds identify trade opportunities, and much more. Fintica is at the forefront of the paradigm shift, leveraging unsupervised, Autonomous AI backed by more than 200 patents and 11 years of research.

To learn more please contact us and be sure to check back on our blog as we dive deeper into AI, fintech, and the groundbreaking work being done at Fintica.

Cheers,

The Fintica Team