Machine Learning is an application of artificial intelligence where machines can learn from data, recognize patterns, and make judgments with little or no human intervention. In easy words, it’s a subset of data science that allows machines to learn and improve without being programmed. The value of machine learning technology has been acknowledged by most businesses that deal with big amounts of data. Organizations can work more effectively or gain an advantage over competitors by the use of machine learning. Machine learning in finance is becominga critical component for a variety of financial services and applications, including asset management, risk assessment, credit scoring, and loan approval. When significant amounts of data are entered into the system, machine learning tends to be more accurate in gaining insights and producing predictions. The financial services industry, for example, deals with massive amounts of data from everyday transactions, invoices, payments, vendors, and consumers, all of which are ideal for machine learning. Machine learning and other artificial intelligence (AI) principles are used in the banking industry in a variety of ways. The use of algorithms to create better trade decisions is referred to as algorithmic trading. Unlike human traders, algorithmic traders can examine massive amounts of data at the same time and execute thousands of deals per day. Human traders have an advantage over the market average because machine learning creates quick trading selections. And also, it does not make trading judgments based on emotions, which is a common flaw among human traders whose judgment is influenced by emotions or personal goals. With the advent of technology, fraud in the financial industry is now regarded as a serious threat to sensitive data. Machine learning works by comparing a transaction to other data points, such as the customer’s account history, IP address, and location, to see if the flagged transaction matches the account holder’s behavior. The system can then automatically deny a withdrawal or purchase until a human makes a decision, based on the nature of the transaction. Robo-advisors are online applications that use machine learning to give investorsautomated financial advice. The programs employ algorithms to construct a financial portfolio based on an investor’s objectives and risk tolerance. Low account minimums are required by Robo-advisors, and they are typically less expensive than human portfolio managers. Companies in the banking and insurance industries have access to millions of consumer records, which can be used to train machine learning algorithms to make the underwriting process easier. Machine learning algorithms can make quick judgments on underwriting and credit scoring, saving businesses both time and money that would otherwise be spent on humans. Kona Software Lab Ltd., the R&D center and global solution business wing of the South Korean smartcard, payment and security industry pioneer Kona I Co., Ltd, has been carrying out research on machine learning over the last few years and incorporating it in its various business ventures. The company is working towards the expansion of machine learning inthe Bangladesh market as well.
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