Disruptive technologies like the internet, e-mails, mobile phones, and social media services have always made ripples across the commercial and consumer markets. Similarly, artificial intelligence (AI) has made its way into numerous business applications over the last few years. The financial services sector is not far behind; from customer service to digital ID verification and compliance solutions, AI has solved many problems for the banking sector.
AI requires rich amounts of data to function and happens to be the perfect solution for augmenting banking procedures and functions. The financial sector thrives on large amounts of data and so does AI, which allows FinTech companies to come up with remarkable systems. There are FinTech firms dedicated solely to creating banking solutions based on AI technology.
The artificial intelligence enables banks and financial institutes to process data in real-time, thereby increasing their overall efficiency and reducing overheads. The financial sector is fast coming to terms with the numerous benefits that AI technology presents as it can enable them to analyze and process vast amounts of data in no time.
AI Solutions for the Finance Industry
Trading Algorithms
It is a widely known phenomenon that computer systems can perform mathematical functions much more efficiently than humans. Machine learning, which is a subset of AI, takes the functioning of computer algorithms to a whole new level. Drawing on complex algorithms, machine learning is being used to develop trading solutions. Different hedge fund companies and investment banks have been investing in the tech and have been able to draw results that far outshine human analysis and comprehension.
An AI-based company Sentient has managed to develop a trading algorithm that uses large data sets to derive trading patterns and forecast the trends most likely to prevail in the market. The technology literally takes trillions of trading situations from public sources to predict the outcome of a particular stock. The data collected is also used to provide insightful patterns and work out strategies. These strategies are then used by a trading company in live trading.
Another approach adopted by a hedge fund is to outsource the development of trading algorithms. The company, Numeral, assigns the task of developing machine learning algorithms to anonymous data scientists. The one with the best solution ends up earning digital coins for their algorithm.
Although stock trading is a complex and intricate task and is impacted by a number of random variables. Experts are still doubtful about the impact trading algorithms can have on the trade market. But efforts are being made to refine the technology and the valuable insights into trading that it provides cannot be denied.
Fraud Detection
Online fraud and cybercrimes are at an all-time high. As is suggested by many cybercrime experts, fraud has to be tackled proactively rather than reactively. Artificial Intelligence has played an important role in developing fraud detection solutions, that enable banks and financial institutes to pinpoint fraudulent behavior. Old solutions for detecting fraud resulted in a large amount of false positive, thus declining legitimate transactions. The declined transactions caused businesses to lose more customers rather than prevent fraud.
AI-enabled solutions have been able to provide better results in this area. It has enabled FinTech companies to develop solutions that can detect suspicious activity based on past behavior of a client, rather than a fixed rule-based analysis. It is also able to detect fraudulent transactions that would otherwise go unnoticed by humans.
Another fraud detection solution, Sift Science, uses a different approach to detect fraudulent behavior. It collects data from different websites where the solution is implemented and uses it to analyze transactions. By using multiple channels to refine its decisions, it can generate a better idea of consumer behavior, thus allowing it to make more accurate decisions.
Other fraud detection solutions use real-time ID verification through an AI-powered system. They can use a person’s ID and biometric scans to detect if they are using a fake or stolen ID to open a bank account or make a transaction through a stolen credit card.
Chatbots
Perhaps the most popular application of Artificial Intelligence is chatting robots. Chatbots are practically being used in every business to enhance customer service experiences. They usually use Natural Language Processing (NLG) and machine learning to provide a human-like chatting experience to customers.
The banking sector, in particular, is using chatbots to improve its banking functions. A chatbot by the name of Plum gives advice on how to increase your savings. It is connected to your bank account and analyses your spending behavior, income and savings to give you advice on making better savings.
Cleo is another chatbot that uses your income and spendings data to allow you to manage your wealth. You can seek advice from it in a completely conversational manner and present any queries that bother you about your wealth management. You can further use it to get tips about managing your expenses, saving more money and even seek advice about future plans.
Banks and financial institutes are also using chatbots to improve their services. Self-service functions in most banks have outdated technology and are hard to navigate by customers. Not long ago Bank of America launched its chatbot by the name of Erica. It is basically a digital assistant for banking that can be used to ask any query that a customer might have. It is enabled by both voice messages as well as text and can be used to make decisions more efficiently. Just like a person issues a voice command to their digital assistant (Siri, Google, Alexa) they can do so with this app. It can help the customer to navigate through different services including personal finance and wealth management.
Hand presenting business finance concept
