While not limited to the fintech world, Artificial Intelligence is a crucial aspect for many fintech start-ups and organisations in many different ways. Put as simply as possible, artificial intelligence is intelligence demonstrated by machines. This can take place in a variety of ways, whether it’s the other player in a computer game or even your Alexa, as well as having wider implications in the financial industries, among many others.
AI certainly isn’t a new idea, and has been around since at least the 1950s, and for a lot longer in science fiction terms. Of course, computers weren’t quite the same as they are now, and were big machines that filled rooms rather than being able to sit on your laptop. However, at that time people were starting to broach the idea of what artificial intelligence could be. Claude Shannon published the article “Programming a computer for playing chess”, and Alan Turing first proposes “the imitation game”, later known as the Turing test (the test of a machines ability to exhibit intelligent behaviour equivalent to that of a human. The first artificial neural network is built in 1951 by Marvin Minsky and Dean Edmunds, and in 1952 Arthur Samuel develops the first computer checkers-playing programme and the first programme to learn on its own.
AI has of course come a long way since then and is now used for a wide variety of activities. It is particularly of interest to fintechs, either to develop it or use it themselves, as it has so many beneficial use cases. In this article, we’ll take a look at some of them and help you to understand what AI really is.
The Difference between AI and Machine Learning
Even though the two terms are often used interchangeably, AI and Machine Learning are two different things, and though very similar have slightly different ways of operating. Artificial Intelligence is actually the umbrella that Machine learning comes under, making ML just one type of AI. To put it as simply as possible, ML refers to any analytics that “learn” patterns in data without being guided by a human analyst. So, for example, you need your machine to be able to tell the difference between pictures of dogs and cats. Initially, you would present the bot with a set of pictures and tell it that one is a cat and the other is a dog. It’ll go through and sort the images into “cat” and “dog” finding statistical patterns within the data that enable it to do so and creating its own algorithm. The computer might get a few wrong, so you go back and correct it allowing the machine to learn. In the same way a human can use experiences to learn, so can a computer and the more data the computer receives the more accurate it will become. That, in the most basic of nutshells, is how machine learning works, and the process is very widely applied in our everyday lives in places you may not think. For example, filtering our spam emails, autocorrect suggestions and even in your Netflix recommendations.
Artificial intelligence on the other hand mimics human intelligence, to the point where it would be impossible, or at least very difficult, to be able to tell the difference between the two. You would not need to have to pre-programme AI like with Machine learning and uses algorithms that can work with their own intelligence. AI’s can perform various complex tasks, whereas machine learning has a limited scope, only performing the tasks you have trained it to do. In theory, AI’s will keep on learning and can perform any tasks the same way a human would (if not better). Some applications of AI in the real world are Voice assistants like Siri and computer-generated players in games and even autopilot in planes.
Use cases of AI
AI has a whole host of practical uses not only in the fintech industry but in the wider finance world, and even the wider world beyond that. The general gist of Ai is that it solves problems; it allows companies to save both time and money. According to the prediction of Autonomous Research, AI technology will allow financial institutions to reduce their operational costs by 22% by 2030. Adopting AI enables the industry to create a better environment for the customer, providing better customer service through a variety of different activities.
In many instances, the practical use of AI is to do with data and enable companies to analyse that data in an efficient advantageous way. Organisations, particularly financial institutions, will often have streams of data on their consumers, but will rarely do much with it due to the time it would take to go through and analyse in order to find anything meaningful. This is where artificial intelligence comes in, as AI and machine learning are very effective at analysing large amounts of data in real-time, then taking that data and drawing conclusions or recommending actions.
One particular example of applying AI with data is for banks to decide whether someone is creditworthy. Banks and other financial institutions want to be able to offer credit to their customers, but they want to be able to price for it accordingly, i.e. they don’t want to overcharge trustworthy customers or undercharge customers that may be more of a risk. Traditionally, to determine someone’s creditworthiness you would look at their credit scores, credit bureau data kept by agencies like Experian. However, by utilising AI these institutions can look at their own customer data that they have and draw conclusions from there. From these large portfolios of consumer data AI can infer different kinds of relationships. Details like your job, where you live or where you work are more obvious sources, but there’s an argument that even details like who your email provider is can show more or less creditworthiness.
Another way AI’s data analysis can be used is for fraud detection and prevention. As previously said, AI and machine learning solutions can react to the data they are presented in real-time, finding patterns and relationships and even being able to recognise fraudulent activity. As you can imagine, this is hugely beneficial to the financial world as an unbelievable amount of digital transactions take place every hour, with heightened cybersecurity and successful fraud detection a necessity. AI takes the brunt of the work away from fraud analysts, allowing them to focus on higher-level cases while the AI ticks along in the background identifying the smaller issues. An example of how AI can detect through is by detecting anomalies, so going back to our banking scenario, perhaps a person has tried to apply for 10 identical loans in 5 minutes, the AI computer would be able to detect this as an anomaly and flag it up as suspicious. The machine has a baseline sense of what is “normal” and when something deviates from that it is able to identify it and review it.
Other use cases of AI include automated customer support. We’re all used to seeing chat boxes pop up at the bottom of our screens when we’re browsing the internet, and these are of course AI bots primed and ready to help out. Companies can simply load up their most commonly asked questions and tell the bot what answers to give, also instructing it to refer the customer elsewhere on more complex issues. Being able to answer frequently asked questions about the company or the product/ service it provides gives a better experience for the customer, as they get the answer to their query straight away, as well as saving the company time and money from not having to employ someone to sit and type responses, or can have a worker direct their attention elsewhere.