FinTech industry is known to use artificial intelligence for a wide range of purposes. Digital enterprises use it for efficient chatbot response systems. Some businesses offer AI as an assistant for asset management and market analysis. The use cases of AI are widespread among the industry, and we can safely assume that technology will be further used. According to Mordor Intelligence, the AI market in fintech is projected to grow beyond $7 billion from only $1.2 bln in 2017.
We have reached the point where standalone branches of artificial intelligence are making their impact on the industry, providing solutions to the issues that were previously operated by human employees or unattended to at all.
Machine learning emerges as a branch of AI that allows utilizing the power of said intelligence for deeper, contextual purposes. A few years back, the companies were forced to do everything with the help of human analysts, model builders and other highly-skilled and experienced staff.
Today the ML is capable of assisting in the analysis of the market patterns, taking into account previous cases and building a successful analytical model with little to no intervention from humans.
Fintech companies are already reaping the benefits of machine learning, yet we believe, that the industry will evolve further. It usually results in breakthrough solutions as well as a significant improvement of the existing ones (we’ll cover both further down the article).
This article will try to elaborate on machine learning as a soon-to-become integral part of the whole system. Let’s delve into the matter of ML within fintech.
With its vast potential in optimization, analysis, customization and more, machine learning becomes a powerful tool for data-based solutions. Those that strive under the banner of fintech.
Here’s a quick preview of 5 significant advantages of ML in fintech:
- Unique customer experience
- Marketing opportunities
- Fraud detection
- Stock market forecasting
- Custom solutions based on machine learning
Let’s savor all the details and see why exactly this tech is helping financial services live the dream life.
UI and all kinds of assistants stand at the forefront of all fintech as a service. No matter how complex the formulae are, how extravagant the analysis is or how advanced mobile banking technologies used — the customer still needs to navigate it and use everything properly.
The business (regardless of the industry) will perform better if the client feels valued. And that value is brought by unique experience. Starting as a set of commands and pre-written responses, AI and machine learning, in particular, transformed the support chatbots into full-scale robotic assistants.
The general public is still unsure about fully-automated financial processes, but more and more people are ready to take advice from a non-human assistant, as BI Intelligence report suggests:
Image source: Robo-Advisors Trust Rate in Europe
Language recognition beats new records steadily, and data analysis allows to convert these advancements into a brand new experience for each client every time they use the service. We have already covered chatbots and their role in one’s business in our blog. Check this out, if you feel like adding an AI assistant to your weapons locker.
Moving beyond customer support for a product, meet Clark, a robo-advisor. This German startup is an advisor platform that offers knowledge on insurance and other related issues, with the list being updated continuously. TechCrunch has already covered it’s $29 mln Series B funding. All the clients have to do — is ask. And Clark will come up with a solution (that is within his field of operations, obviously).
Analytics has always been a crucial part of any marketing campaign. AI and machine learning take that to the next level. There is a countless number of data to take into account while pulling off a successful marketing effort.
Visit times, ROI, behavioral patterns on website or apps, purchase preferences, age/gender influence on purchasing activity within the niche and so much more. It used to take a ton of manual micro-management and analysis. Today, machine learning enables swift data collection and analysis. While processing previous queries, purchases, and other aspects, the AI can offer better solutions and models for different audiences or fulfill various goals (landing leads, generating sales, increasing the UI comfort and overall quality of UX.
The beauty of machine learning in these circumstances is that one can teach the AI to deal with any precise type of situations to come out on top and eliminate unwanted losses.
According to LexisNexis, financial enterprises are paying a hefty fee for each security failure — up to triple the costs lost to recover from the breach.
Large banking institutions have been slow to adapt to new threats, fighting only the symptoms, if anything at all, based on how outdated the security measures may have been. While the company can focus heavily on security (authentication, encryption, etc.), there will always be a way to lose clients’ money.
Transaction approvals were seen to miss many fraudulent activities and instead block legitimate transfers throughout the world, causing more problems to the clients without actually dealing with the issue.
AI and machine learning allow taking the fight to the nemesis. Analysis of the patterns that predate the frauds allows much more security on all lines of defense. An AI can be taught to notice the similarities between scams and root them out before their inception. With AI’s huge potential of multi-tasking and real-time processing, the whole set of protective activities would go unnoticed for the human eye.
Furthermore, an in-depth analysis allows us to spot frauds without placing hundreds of regulations on routine transactions, which means better security and less trouble for us, people who just decided to purchase an item from eBay.
Here’s an excellent overview of what aspects are taken into account by an ML-based security solution, visualized by Guardian Analytics:
Source image: Metrics of ML-Based Security Solutions in Fintech
Today’s stock exchanges are worth about $69 trillion. To navigate this vast pool of money and companies, to determine the most productive activities, one must be no less than a stock mastermind, especially when it comes to predicting the behavior of the stock markets.
Machine learning makes the job much more manageable. By analyzing millions of units fo stock data simultaneously, the AI can adapt to the market condition and come up with a much more accurate prediction for much less time. The data about the companies is only one side of the coin, with other being media mentions of the businesses that may take effect on stock value, like that interview of Elon Musk by Joe Rogan not so long ago.
It is fair to say that the analytical capabilities will grow further, as the proficiency of an ML-based AI increases with each processed operation. The more companies it analyses, the more predictions it makes, and more accurate these will become in the future, with an ever increasing layer of auxiliary factors that may influence the stock value.
Machine learning is an insanely powerful tool to automate processes, cut expenses and come up with much better analytics and predictions. However, every business is a unique enterprise and has its own needs, vision, budgets, etc.
The technology has to yet offered a universal solution to all issues of financial market (or any other market). All machine learning solutions are still created and maintained by human professionals, and it will remain that way unless a full-scale sentient AI emerges.
Machine learning algorithms allow building a solution that would fit any particular need (from fraud detection to face swap on pictures and videos). Big companies make custom ML-based solutions to meet the demand of their audience — for example, account and asset management for digitally active clients (Weatherfront).
But only you know what kind of machine learning solutions you need for your business. Try to define a set of goals to complete or issues to fix, and start from that. Many companies offer help in building software using these technologies.