<ul><li>arificial Intelligence (AI), a fast-evolving technology, is gaining popularity globally. The banking sector is one of the leading adopters of Al. Most banks and financial institutions are adopting Al to increase efficiency to their back-office and minimize security risks. In a report published by the Bank of England (BOE), two-thirds of U.K. banks are already using artificial intelligence to run their business. As the BOE further explains, machine learning is not always substantially different from the statistical models; banks have been using, like the credit scores they develop to predict the possibility that a customer may default on a loan, or the models banks use to project if a particular debit or credit card transaction was fraudulent. The fact is that the impact of Al will be most profound in specific banking applications. Artificial intelligence will massively disrupt banks and traditional financial services. Some of its disadvantages are:</li><li><strong>Requires Supervision </strong>Tech systems are like engines: they run, but someone has to ignite them. Banking IT is still very much needed to run the Al system. They are the ones feeding the Al system with all the new information required for them to learn in the first place. This form of supervised learning does not mimic the way a human learns naturally, and this is one of the most significant limitations when it comes to creating a more human-like Al.</li><li><strong> Cost and Maintenance </strong>Production and maintenance of artificial intelligence are highly cost demanding since they are very complex machines. Your Al software will also require regular upgrades to adapt to the needs of the continually changing business. In case of future critical failures, the process of reinstating the system and recovery of lost codes may require a lot of time and cost. Banks should put into consideration the return on investment before the implementation of any Al system.</li><li><strong>Lack of Creativity </strong>Creativity remains a vital component of a successful financial institution. Machines cannot be creative. Unlike computers, humans think and feel, which often guides their decision making when it comes to creativity. Affirmatively, Al assists in determining what sort of imagery, for example, a consumer is likely to click on - from color preferences to style and price. However, when it comes to originality and creative thinking, a machine simply cannot compete with the human brain. Both must work together Al will only do what it is programmed to do. Granted, it usually does this exceptionally well, but unlike a real person, it is unable to make split-second judgments. For instance, a bank may change or cancel any scheduled communication in the event of any tragic occurrence or system failure because we, as humans, are emphatic and reasonable. A machine, however, cannot think beyond the programming, so it could get a bank in an ugly situation in times of crisis if it is not carefully managed or controlled.</li><li><strong>Data </strong>The main limitation of implementing Al is data availability. For Al to work correctly, models need instructions on the information. The larger the architecture, the more data is required to produce viable results. Therefore, If you feed a model poorly, any Inaccuracles in the data will be reflected in the outcome. If the data is inconsistent and of shallow quality, it presents challenges for the businesses looking to create value from the Al. Another data-related con concerns data standards and regulations. Banks have to determine whether the data has the right parameters. Organizations need to ensure that their data can be shared across different financial service providers based on federal, state, and internal requirements.</li></ul>