Artificial intelligence, or intelligence as demonstrated by machines, is the simplest definition. Artificial intelligence can occur in many ways, including as the other player in a game, or your Alexa. It also has wider implications for the financial and insurance industries.
AI is not a new concept. It has existed since at least the 1950s and even longer in science fiction terms. Computers weren't as powerful and portable back then, and they were often too big to be able to sit in a chair. But, people began to explore the possibility of artificial intelligence at that time.
"Programming computers for playing chess" is Alan Turing's first proposal. This was later called the Turing test, which measures a machine's ability to display intelligent behavior comparable to that of humans. Marvin Minsky, Dean Edmunds, and Arthur Samuel create the first artificial neural network. In 1952, the first computer-checkers-playing program is developed and the first program that can learn by itself.
AI has come a long way since then, and can now be used for many different purposes. Fintech is particularly interested in AI, whether they develop it or use it. It has many benefits. We'll be looking at some of these and helping you understand AI.
What is the difference between AI and Machine Learning?
Despite being often interchangeable, AI and Machine Learning, although they are very similar, are two distinct things that have slightly different operating methods. Artificial Intelligence is the umbrella under which Machine Learning falls, so ML is just one type. ML is an analytics system that can "learn" patterns from data without the assistance of a human analyst. For example, your machine must be able to distinguish between images of cats and dogs. You would first present the bot with a collection of photos and tell it one is a cat, the other a dog. The bot will sort through the images and create its own algorithm by identifying statistical patterns in the data. You can correct any errors that the computer makes, and it will continue to learn. A computer can learn from experience, just like a human can. The more data it receives, the better it will be. This is machine learning in its simplest form. It is used in many areas of our daily lives, including some you might not be aware of. Filtering spam emails, suggesting autocorrections, and even your Netflix recommendations.
Artificial intelligence, on the other side, mimics human intelligence to the point that it would be difficult or very difficult to distinguish between them. Artificial intelligence does not require you to pre-program it like Machine learning. It uses algorithms that are compatible with its own intelligence. AI can do complex tasks while machine learning is limited to performing tasks that you have taught it. AI's can learn and perform all tasks as well as humans. Voice assistants such as Siri, voice-activated players in games, and autopilot in planes are just a few examples of AI that can be used in the real world.
AI uses cases
AI can be used in many areas, not just in fintech but also in the wider financial world and beyond. Ai solves problems and allows companies to save time and money. Autonomous Research predicts that AI technology will enable financial institutions to cut their operational costs by 22% in 2030. AI technology allows the industry to create a better environment and provide better customer service by using a variety of activities.
AI can be used to analyze data and make companies more profitable. Financial institutions, in particular, often have streams of data about their customers. However, they will not do much with that data due to the time required to analyze and go through it to find any meaningful information. Artificial intelligence and machine learning can be used to analyze large quantities of data in real-time, draw conclusions, or recommend actions.
Banks can use AI to determine creditworthiness by using data. This is one example of AI being applied with data. Banks and other financial institutions desire to be able to offer credit to customers. However, they also want to be in a position to price it appropriately, i.e. They don't want customers who are trustworthy to be overcharged or customers who may pose a greater risk. To determine someone's creditworthiness, you would normally look at their credit scores and credit bureau data, which are kept by agencies such as Experian. These institutions can now use AI to look at the customer data they already have and draw conclusions. AI can draw different types of relationships from these huge consumer data sets. While details such as your employment, your location, and where you live are obvious, there is a possibility that details such as your email provider can also be used to show creditworthiness.
Fraud detection and prevention are two other uses for AI's data analytics. Machine learning and AI can be used to analyze data in real-time, identifying patterns, relationships, and even recognizing fraudulent activity. This is hugely advantageous to the financial industry as there is an incredible number of digital transactions every hour. With increased cybersecurity and fraud detection becoming a necessity, it's essential that AI and machine learning solutions can detect fraudulent activity. AI does the bulk of the work for fraud analysts. They can concentrate on more complex cases, while the AI works in the background to identify the smaller issues. AI computers can identify anomalies and flag them as suspicious. Let's say that someone has applied for 10 identical loans within 5 minutes. The machine is aware of what is normal and can identify and review any deviations from this baseline.
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Another use case for AI is automated customer service. Chat boxes are a familiar sight when surfing the web. These chat boxes are AI bots that are ready and waiting to assist. Simply load up your most frequently asked questions, and the bot will give you the answers. It can also be instructed to send the customer to another company for more complicated issues. Customers will have a better experience if they can answer their most frequently asked questions about the company and the products/ services it offers.
