Use of Artificial Intelligence in Banking Industry
Technology Aspect
The banking business has seen significant changes as a result of technological innovation. One of the most notable changes is the use of artificial intelligence (AI). So, what is artificial intelligence? In basic terms, artificial intelligence is the imitation of human intelligence by computer systems. Artificial intelligence learns from data, makes predictions, and facilitates decision making. Thanks to improvements in computer science, including the areas of hardware and software, artificial intelligence is much more popular and widely used in many industries. Almost every business where artificial intelligence has been used, has undergone a transformation, and banking is no different.
Smart Banking
Banking is part of financial services, which is the largest industry in the world with $30 trillion market value. The rest of the industries, including health insurance, e-commerce, oil and gas, car and automotive manufacturing, technology etc., benefit from the financial support of the finance sector. So, the importance of the banking industry is undeniable and indisputable. How can one make the banking sector more efficient, more productive, smarter, and faster? One of the strategies is to implement artificial intelligence in the processes of banking industry. Here are some examples of AI use and how it improves procedures and the customer experience.
1) Security and fraud detection
Since most transactions are now digital, the volume of transactions has been growing at an exponential rate as a result of the popularity of online banking. In banking, enhanced fraud detection algorithms and fraud protection systems are widely used. Artificial intelligence systems can examine massive amounts of data to find unusual patterns and transactions that can point to a fraud. Unauthorized transactions, phishing attacks, identity theft, money laundering are easily detected, while any false positive alert doesn’t result in unhappy customers or a negative brand reputation (a false positive alert means “real” customer transaction which is flagged as fraud).
2) AI-powered chatbots
Long waiting lines and slow response times damage brand reputation in today’s financial services. Companies are looking for innovative methods to engage with customers in more productive ways. Chatbots are self-help and self-service solutions that reduce the workload of call centers in a “conversational” way. Differently from rule-based, and keyword-based chatbots, artificial intelligence chatbots (also known as machine learning chatbots), can respond to advanced and open-ended questions. Through deep-learning algorithms, they can improve their responses over time. Many researchers predict that the global conversational AI market will reach $30 billion by 2030.
3) Credit management
To have a clear picture of a company’s finances and cash flows, it must avoid unnecessary credit risk. Traditional credit scoring methods have repeatedly come under fire for being out-of-date because they don’t take into account individual differences and behaviors. Artificial intelligence-based credit management models allow companies to perform faster analyses with improved accuracy in risk predictions. Furthermore, artificial intelligence algorithms eliminate human prejudice, resulting in more unbiased and fair credit risk evaluations.
4) Document processing
Document processing is crucial in the banking sector because of compliance with regulatory guidelines. Documents must be digitized to improve the accuracy and speed of data processing. This helps to reduce human error and speed up the processing time. Machine learning algorithms can also categorize and classify documents according to predefined rules or machine learning models. With optical character recognition (OCR) technology, text from scanned documents or PDFs can be made editable. This makes data entry simpler and more precise, and it can speed up manual data entry activities significantly.
5) Customer segmentation, personalization, and recommendations
Financial and non-financial transaction data make it possible to create patterns that may indicate a person’s everyday spending patterns, their typical transacting days and hours, and geographical information. We now have an AI application system that keeps track of your income, costs, and spending patterns and makes recommendations for an improved strategy and money-saving advice. The main objective is to accurately suggest the best products and services. As Netflix presents us movies and series that we “might like”, banks are motivated to suggest us products and campaigns we “might like” just in time. For example, the “What do you want to do now?” question at the end of a transaction, customized campaign offers, and personalized “Next transaction” options free customers from navigating mobile banking menus and facilitate the customers’ journey.
Conclusion
The work practices in the financial sector will significantly change as the use and importance of artificial intelligence increase. Several daily tasks that are currently performed by humans can be replaced by artificial intelligence. It is obvious that all banks are not ready to deploy and implement artificial intelligence in their processes and products. But it is certain that financial institutions that invest in artificial intelligence and machine learning will make a difference with their competitors.
