|Type of paper:
|Data analysis Banking Artificial intelligence
In the modern world, many people still associate artificial intelligence with science fiction dystopias due to its complexity and massive ability. However, it has still continued to increase in daily lives. AI engages in humanlike activities through the use of computer software including learning, planning, and different ways of problem-solving. One of the recent and common developments of AI is machine learning (Alzaidi 2018). Machine learning is being used by businesses in processing large amounts of data quickly. These are types of algorithms that seem to learn over time where a large amount of data can be fed in a machine learning algorithm hence facilitating modeling. For example, manufacturing plants have established ways in which computer software algorithms can be used in connected data streams about organizational functionality, production, and more central to the location (Barnhizer 2016). The use of machine learning in such situations proves significant as it can identify the patterns as well as anomalies when rapidly processing the data. Deep learning is the other aspect of AI which is a more specific machine learning that relies on the neural networks to engage in nonlinear reasoning. Deep learning has become important in performing more advanced functions like fraud detection (Bershidsky 2019). This is done through the analysis of wide range factors at once such as the self-driving cars contextualizing information that is picked by their sensors about the objects. These traits have proved that they could be a transformation in different business entities especially the banking industry that deals with huge amounts of data for critical transactions.
The banking sector handles lots of financial data that makes the employment of high speed and superior decision-making technologies like AI crucial. Alzaidi (2018) argues that machine learning, the blockchain, data analytics provided by artificial intelligence tools have the ability to transform the manner in which the banking industry works. The author further notes that further studies should be conducted to examine how the adaptation of AI has in the banking sector. Additionally, it has been established that machine learning has a limitation in the banking sector as it tends to focus on the historical data sets. This implies that it can fall the trap of being repetitive and hence potentially giving way for the conscious or the unconscious bias. For example, there have been questions regarding how fair can a financial system be without the involvement of human beings (Choi and Lee 2018). Although the use of machine learning technology can facilitate massive development and improve the efficiency in the banking sector, one area that cannot be solved by the technology is building strong relationships with the clients. Therefore, studies have demonstrated challenges regarding the usage of AI in the banking sector illustrating that there is a potential of losing clients due to poor customer relationships. The human element is still needed as an important factor for certain job functions that the AI is not capable of replicating. Researchers have demonstrated that despite the efficiency that is built when using this technology, individuals are able to understand the emotions of others hence their ability to show empathy in handling interpersonal relations is imperative in facilitating better engagement (Manser Payne, Peltier and Barger 2018).
Maria (2018) notes that artificial intelligence has a huge potential in the banking industry and can be successfully applied in tailored financial services, underwriting, voice-aided transaction, and data-driven loaning decisions. As the population grows banks customer bases are poised to grow and consequently the transactions hence the need for a computer system that is superior to human experts. Besides insurance, the bank has benefitted immensely from the use of AI. It is crucial for businesses in the modern world to continue with development and think on the best ways that they can improve the manner in which they do their businesses. Instead of becoming complacent, they should continue to reflect on the processes, challenge routines, and a future-facing entity in their approach towards AI. However, while the development of artificial intelligence has been rampant in the recent past, the question of rendering people jobless has been a major concern (Manning 2018). It should be pointed out that AI and automation technologies can take on many tasks as well as transactions a led accountant can undertake or even the data administrator with little or no human involvement. Therefore, these challenges have brought significant concern in the banking sector regarding the survival of the financial sector and how the changes may affect the overall performance.
Other financial entities are merging AI and the Internet of Things in a bid to stretch the extent to which banking system can accommodate other external factors. In analyzing the importance that AI has brought to the banking sector, it is important to examine how it has leveraged on the use of information from other sectors such as insurance, and government agencies. However, before incorporating the AI software, it is imperative for businesses to rethink their strategy and the key objectives that they want to hope to achieve using the technology (Kingdon 2004). Although the technology may be useful, there is a need to evaluate the ways in which it can be used effectively and the role of human input in facilitating a perfect performance.
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