Banking Paper写作范文-数据科学对金融和银行业的影响。本文是一篇Banking银行学专业方向的Paper写作范文，主要内容是分析和报告数据科学对金融和银行业的相关影响，Paper范文通过最广泛的角度来分析，数据科学可以定义为从数据中获得洞察力的过程。它是观察现实世界现象的连续循环；收集和处理原始数据，以创建模型、进行分析或检验假设；得出有数据支持的结论，有助于理解和决策。以下内容就是Paper写作范文的全部内容，供参考。
Paper写作范文
Introduction 简介In the broadest of terms, data science can be defined as the process of deriving insight from data. It is a continuous cycle of observing real-world phenomena; collecting and processing raw data in order to create a model, perform analysis or test a hypothesis; and arriving at conclusions backed by data that aid understanding and help decision-making.
With the ever-increasing prevalence of technology and data in modern society, many companies and industries have begun to realise the need for data science and to reap its rewards. The banking and finance industries are perhaps the most obvious benefactors of these insights, second only to the technology sector itself - although with the recent surge in financial technology (FinTech) start-up companies and advancements, the line between the two is set to become even more blurred.
随着现代社会中技术和数据的日益普及，许多公司和行业已经开始意识到数据科学的必要性并从中获益。银行业和金融业也许是这些见解最明显的受益者，仅次于科技行业本身——尽管随着金融科技（FinTech）初创公司和进步的激增，两者之间的界限将变得更加模糊。
Over the past decade, data science in the finance and banking sector has become less of a trend and more of a necessity in order to maintain a competitive advantage over competitors and a healthy relationship with consumers.
在过去十年中，为了保持与竞争对手的竞争优势以及与消费者的健康关系，金融和银行业的数据科学已不再是一种趋势，而更成为一种必要。
In this paper, I will outline some of the ways data science has been utilised by banks and financial institutions to transform or disrupt the status quo of the industry, and what potential consequences arise both in the present and the future.
在本论文中，我将概述银行和金融机构利用数据科学改变或破坏行业现状的一些方式，以及在当前和未来可能产生的后果。
App-based Banking and FinTechs 基于应用程序的银行和金融科技
Traditionally, in order to open any type of bank account, a person would have to endure a long and arduous process. First, they would have to find an account that suited their needs, which can be a laborious task. Then, they would have to travel to that provider's nearest, often busy high street branch and queue up to speak to someone in person, taking with them a variety of different personal documents. In most cases at least three documents are needed, providing evidence of one's full name, date of birth, national insurance number, proof of address and proof of identity. The list becomes even longer for more complex accounts like joint accounts and some will require a credit check as well. All of this means it can take a substantial amount of time to go from deciding to open an account to actually being able to use it.
传统上，为了开立任何类型的银行账户，一个人必须经历一个漫长而艰苦的过程。首先，他们必须找到一个适合自己需要的账户，这可能是一项艰巨的任务。然后，他们将不得不前往该提供商最近的、经常繁忙的商业街分支机构，排队与某人面对面交谈，随身携带各种不同的个人文档。在大多数情况下，至少需要三份文件，提供全名、出生日期、国家保险号码、地址证明和身份证明。对于更复杂的账户，如联名账户，该列表甚至更长，有些账户还需要进行信用检查。所有这些都意味着，从决定开立账户到实际能够使用账户，可能需要相当长的时间。
However, over the last few years, a rise in digital banking and the formation of challenger banks have allowed the data generated by consumers in the banking industry to be fully utilised. Thanks to the recent introduction of the Open Banking reforms which requires banks to let you share your financial information with authorised providers [1], the creation of purely app-based banks such as "Monzo" or "Starling Bank" has been facilitated. Such banks do not have any branches and can provide near-instant 24/7 support via live chats in the app - something that traditional banks struggle to offer. These live chats use machine learning to check what has been sent from a customer in the chat and generate possible appropriate responses for the customer service agent. By scanning and analysing the language in the written query, the process of submitting requests is sped up and it helps connect users and help team workers more effectively [2]. Also, the machine learning algorithm will learn what responses are most applicable to a given request, further
