Artificial Intelligence (AI) has become the driving force that will make a difference in the finance industry, as in many other areas. Actually, the finance sector has proven itself an early adopter of AI in comparison to other industries. The most popular and prominent AI capabilities are available to banks, insurance firms, and other financial institutions. An AI system for finance sector can analyze massive amounts of data in a faster, efficient, and quality way. It can also identify the hidden details and trends that people may not infer easily. Lets talk about 4 areas where AI can be used in the finance sector.
1) Automation in Banking
With the help of AI, it is now possible to automate processes such as following and understanding the new rules and regulations, or creating personalized financial reports. For example, Watson, developed by IBM, can understand complex rules and regulations, such as “Financial Instruments Market Directive” and “Mortgage Statement Act.”. It may take hours or even days to ask financial experts questions about the new regulations and expect answers from them. However, tools like Watson can give you the answer you are looking for in just a few minutes.
2) Improvement in ERP Systems
The C-suite is under constant pressure from financial and accounting professionals to elevate the strategic relevance of their function. At the same time, they are under constant pressure to help their companies stay ahead of the requirements for audit and compliance, report on finanical reports, and coordiante ongoing accounting activities. Such practices are all vital to corporate growth because enterprise resource planning (ERP) is intended to perform these tasks. Therefore, ERP systems play a critical role in enhancing the ease and precision with which they are completed.
An ERP system having machine learning (ML) features can benefit companies through several aspects. Firstly, it can be used in root cause anaylsis. The system can detect potential risks earlier so that companies can take timely measures to avert any danger. Secondly, the data found in ERP systems are actually human input, so it can improve the accuracy of the insights by using ML algorithms. Additionally, the companies can also customize their insights according to their needs. Last but not least, the systems can make predictive analytics by using the historical data. Hence, ML gives a chance to forecasting and optimizing the processes.
3) Use of Natural Language Processing (NLP)
Wealth managers, traders, and investment bankers could make use of NLP tools for data mining. A common example can be “Investment Research and Analysis”, which means to determine how an investment is likely to perform and how suitable it is for a particular investor. Companies can do web crawling to obtain news about mergers and acquisitions. Then, by using NLP software, they can analyze the collected data to get an idea of how consumers are reacting to them. What NLP can provide is not limited to this. It can be used in analysis of words and phrases in annual reports of the companies to obtain insights. This is also known as sentiment analysis.
4) Smart Assistants, Chat Bots
AI can also be used to develop applications that enable customers to communicate and perform specific actions with smart assistants or chat robots. Chatbots are not only a money-saving tool in banking, but also tools to automate simple tasks such as opening a new account or transferring money between accounts. For example, a Turkish banking company named “İş Bankası” is using “Pepper”. Pepper is a humanoid robot companion which can do several tasks. It is also able to track eye contact and express emotion. You can check the videos below to meet with Pepper:
