Coding Artificial Intelligence GIF by Matthew Butler

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 from many Research, AI technology will allow financial institutions to reduce their operational costs by 22-~25% by 2030. Adopting AI enables the industry to create a better environment for the customer, providing better customer service through a variety of different business activities.
In many instances, the practical use of AI is to do with data and enable companies to analyze that data in an efficient strategic way. Organizations 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 analyze in order to find anything meaningful. This is where artificial intelligence comes in, as AI and machine learning are very effective at analyzing large amounts of data in real-time, then taking that data and drawing conclusions or recommending actions.
One 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 utilizing 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.
Another way AI’s data analysis can be used is for fraud detection and prevention. 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 recognize the fraudulent activity. As we 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 can identify it and review it.
Other use cases of AI include automated customer support. We are all used to seeing chat boxes pop up at the bottom of our screens when we are 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. 

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