Just like artificial intelligence and machine learning, fintech is a term which is currently in the public eye. More and more companies move away from traditional financial methods and start implementing fintech instead. Logically, AI and machine learning are very efficient tools in doing this. But how to use them in order to reach the goal? And how can they actually help? What benefits can their implementation bring? We are here to answer all these questions in details, but before we will do this, let us provide you with a short introduction to the world of AI and fintech.
AI in Fintech: Definition
The definition of fintech, or financial technology, is pretty simple. This is a developing industry which implies using technology in order to improve and simplify diverse financial activities. Some fintech products are widely used by companies to perform financial services. While the other ones significantly change the way people access their finances. Here are a few examples to illustrate the definition of fintech and use cases of AI in Fintech.
Examples of AI in Fintech
1. Blockchain and cryptocurrency. Different currency exchange platforms (like, for instance, Coinbase) provide users with a secure way to buy and sell cryptocurrencies. In turn, services like BlockVerify help to improve anti-counterfeit measures and, therefore, reduce the fraud as much as possible.
2. Mobile payments. Nowadays, to pay for something, it is not essential to open a bank account first. Thanks to fintech, smartphone owners can transfer money right from their phones. And some services even offer lower currency conversion rates than banks! Apple Pay is a great example here.
3. Loan platforms. Such platforms and apps are among the most popular ways for companies and customers to borrow money. Instead of preparing tons of documents and applying for a loan in an ordinary bank, users of such platforms get a virtually instant access to funding. They can receive money in a very easy way and at the same day. Some of these platforms have already started implementing artificial intelligence in order to improve security. For instance, the OKash app has introduced facial recognition to secure loans and keep its clients safe.
There are much more examples of fintech, but we have mentioned only the most basic ones — otherwise, this article will be much longer. But now, when you better understand what fintech is and why it is useful, let’s move to the main part of our story. In the next section we will explain how machine learning and artificial intelligence (AI) used in fintech companies to become more efficient and successful.
AI and Machine Learning in Financial Technology (FinTech)
When it comes to artificial intelligence and machine learning, many people start thinking about voice recognition, text processing, and other popular tasks they can deal with. However, in fintech, applications of AI and ML are more specific and complicated. And here are some of them.
Risk Management and Loan Underwriting
Implementing machine learning and AI solutions to make financial risk management more efficient has become a popular practice nowadays. And that is pretty logical, as machine learning methods are much more effective than those software applications which are usually used for these goals. Apps can only use static information to make predictions, but machine learning offers much more. Apart from using financial reports and other related documents to predict trustworthiness, it can also identify the latest market trends and news which can have an impact on the ability to pay. A trained model can provide you with a detailed and reliable credit risk assessment. Manulife, a Canadian financial services group, is a pioneer in implementing such solutions, and in 2018 they have even announced a $400,000 AI partnership with the University of Waterloo.
Profiling and Categorizing the Clients
After dealing with customer scoring and credit scoring described in the previous paragraph, banks and insurance companies tend to profile their clients. Artificial Intelligence can be a great help in this case, as it can be used to automate the categorization of the clients on the basis of their scores and risk profiles. Apart from this, when categorizing the clients, the company can decide on what financial services and categories can be associated with each other. When this is done, these services can even be offered to the related categories of clients automatically. To reach this goal, you can use, for example, XGBoost. This classification model can deal with many data science and data engineering challenges. Including customer segmentation and is trained on historical client data.
Customer Churn Prediction
The customer churn rate is a percentage of clients who stop using the company’s services within a specific period of time. This information is used in order to take preventive actions and, therefore, retain clients, or at least some of them. And artificial intelligence is exactly that tool which can help the company to deal with this challenge and become more successful. It is able to compile a list of clients who seem to stop using the offered services in the nearest future. As a result using this list, company can develop better offer for such people, so, as a result, they may change their initial intention and continue being clients. To build a model, it is essential to use customer behavior data. The data of clients who are not clients anymore, and of those ones who have changed their decision and stayed with the company.
Customer LTV prediction (Lifetime Value)
Customer Lifetime Value, or LTV, is another crucial factor you need to track in order to improve your customer experience. As a result it helps make your company more profitable. In short, LTV is the customer’s total worth to your business over an entire period of their stay with you. Knowing this data can significantly help you to develop efficient strategies to attract new clients and retain the existing ones. You may think that doing all these calculations is complicated, but artificial intelligence won’t let you get lost. It is able to process and analyze loads of data. On the basis of this analysis and calculations, you will be able to make metric-driven decisions and improve your business strategies. As a result, customers will join your business faster and more often, and buy more.
Fraud Detection / Fraud Prevention – one of the most popular use cases of AI in Fintech
Any financial services are always related to extremely high security. There is no person in the entire world who would like to be deluded. That’s why it is essential to pay attention to the security and do your best in order to keep your clients safe. And, again, artificial intelligence and machine learning can be a great help. Using them, you can identify and, therefore, prevent any fraudulent operations or transactions. Moreover, that’s the best possible way to do this — on your own. You won’t be able to spot patterns and predict the results with a high accuracy.
Better Customer Service
Virtual assistants are usually considered as an integral part of diverse e-commerce websites. But, actually, financial and insurance businesses (and other fintech companies) also tend to use them. They are especially helpful in case your website is more or less big, and it may be challenging for a client to find a specific piece of information. In any case, chatbots and virtual assistants work 24 hours a day, 7 days a week, while your technical support agents may not be able to stay in touch with clients all the time. AI is a key for your chatbot / virtual assistant to become really helpful. Such assistants don’t simply follow your instructions — they learn and “behave” according to the customer behavior. They act almost like humans, so your clients get better experience and feel more satisfied. And that’s very important in case you want to retain them.
Bank Transactions Search
Even if you have done bank transactions categorisation really well, your clients may still need a specific tool to complete the search tasks. A special chatbot is also a solution here — with the help of NLP (natural language processing), it can identify the meaning of the request and then relate it to the specific category (for instance, balance inquiries). Afterwards, the bot processes the search request and shows the results.
Such bot is an amazing tool for users which allows them to get rid of the fuss and any potential challenges related to the search. This trick also improves the customer experience, so the clients’ loyalty increases. In general, loyalty campaigns with machine learning are considered to be more successful and efficient than those ones which are not related to artificial intelligence. Therefore, we recommend you to follow our advice and apply the machine learning techniques to retain your customers.
Trading and Money Management
Algorithmic trading is one of the best solutions for you in case you have to deal with loads of data. Actually, having enough data is a crucial factor — if it is absent, you won’t be able to train the model properly. However, algorithmic trading executes the trades according to your criteria and, as a result, automates the trading process and simplifies the money management. This is how it works: machine learning algorithms analyze the historical market behavior, identify the most efficient strategies, and then make trade predictions (or predict probability of default, for instance). This method is also effective if you have to work with quick price movements. Well-trained model delivers an efficient solution much faster than a human.
A few more words about quick price movements. Dynamic pricing is one of those strategies used by the most successful and famous companies of the world, and you can adopt it as well. Backed with machine learning and AI, dynamic pricing can make your business stand out among the competitors. Here is an example for you to make things a bit easier – Uber.
Uber is well-known for its fair prices. In many cases they are lower than fares of other taxi services. However, for example, when a big football match ends, its visitors will have to pay more than usual to get back home. The demand for cars increases, and so do the prices. And when the demand goes down, the fares decrease as well. That’s what dynamic pricing is — a company changes its fares according to the demand and supply. And, again, AI and machine learning can help you to respond to these changes much quicker than you would ever be able to do on your own.
In this article, we have already mentioned a few ways to automate or at least to simplify diverse business processes, but this paragraph is going to be devoted to more general issues. So, to make your operations more efficient, back your data processes with machine learning. What result will you get? Automation of back-office and client-facing processes, documents interpretation, proposal and execution of intelligent responses, identification and prevention of diverse unpleasant issues, making regulatory compliance and so on. And thanks to the automation of these processes, you and your team will be able to spend more time on more serious and complicated tasks. You should concentrate on tasks which definitely deserve human attention.
There are enough traditional investment models, but they are not that efficient in the modern world. And there are a lot of reasons for that. One of them is market volatility — this factor wasn’t too influential some time ago, but nowadays this is our reality. The market is volatile, and the traditional models are simply not able to meet its requirements. But using a machine learning model, you can quickly and easily identify the market changes and decide how to react. Such a wise strategy will help you to protect your investments and keep your budget safe.
Augmented Research Tools
A great example of AI in fintech is augmented research tools. Again, if your business is somehow related to the investment finance, you definitely have to spend a lot of time on conducting researches. Finding the data you need may take loads of efforts, but machine learning models can significantly simplify this process and speed it up. For instance, sentiment analysis allows checking large sets of data, tracking diverse trends, and so on. In turn, NLP and data science techniques can help you to analyze financial reports of your company and format the financial statements.
Money-Laundering Prevention (AML with AI in fintech)
Money-laundering prevention (also known as AML, or anti-money laundering) is a set of laws, procedures and regulations. It’s main mission is not to allow criminals to keep the illegally obtained income as the legal one. This problem has been existing for a very long time and on a global scale, and there is a high probability that it will never disappear completely. In any case, nowadays there are a lot of ways to prevent this issue from happening, and artificial intelligence and machine learning are also used to minimize money laundering. With their help, it is possible to identify specific patterns related to this sphere of crime. What are the results? The detection rates increase, while the probability of a mistake, on the contrary, decreases. Simplified regulatory compliance is another benefit.
Global Banks are Using AI in fintech
United Overseas Bank (or UOB), a company with a global network, and a leader in Asia, decided to use AI-driven AML technologies. The results turned out to be impressive. Regarding its transaction monitoring, there was a 5% increase in true positives, while the rate of false positives dropped by as much as 40%. These numbers prove that machine learning and artificial intelligence are more than efficient when it comes to money-laundering prevention.
We have already told you that machine learning and artificial intelligence can help to prevent fraudulent transactions. However, it is also important to mention your clients’ data — it should be absolutely secure and protected from hackers. Thanks to the combination of big data capabilities and the intelligent pattern analysis, the AI technology delivers a much higher level of security. Those tools which have nothing to do with artificial intelligence are not that reliable. So we highly recommend you to keep your network secure with the help of machine learning.
To sum up – AI and Machine Learning in Fintech
There are numerous ways to use artificial intelligence (AI) and machine learning in fintech, and now you are familiar with some of them. However, if you still have any questions regarding the probability of delay in payment of invoices, invoice scoring in factoring, how to predict fund trends or anything else, feel free to get in touch with us. We are always ready to provide you with a professional advice and recommend best business decision.