Nowadays, Artificial Intelligence (AI) demonstrates some unique opportunities to increase prosperity and growth. For the banking sector, it provides great opportunities to develop customer experience, democratize financial services, enhance cyber-security and consumer protection, and manage risks properly. This essay aims to discuss some aspects and reasons for using Artificial Intelligence as an advanced issue in the banking sector.

如今，人工智能(AI)展示了一些促进繁荣与增长的独特机会。 对于银行业，它提供了巨大的机会来发展客户体验，使金融服务民主化，增强网络安全性和消费者保护并适当地管理风险。 本文旨在讨论在银行业中使用人工智能作为高级问题的一些方面和原因。

简介与概述 (Introduction and Overview)

Basically, defining artificial intelligence (AI) would not be an easy task. This means the major is exceedingly broad, which it cannot be limited to a specific area of research. AI seeks to understand how human cognition works by creating cognitive processes that emulate those of human beings. AI is at the crossroad of multiple majors such as computer science, mathematics (logic, optimization, analysis, probabilities, linear algebra), and cognitive science. In addition, these core scientific disciplines require to be mixed with the specific knowledge of the majors they are applied to, and each algorithm in AI is provided by a combination of techniques and methods such as semantic analysis, symbolic computing, machine learning, exploratory analysis, deep learning, and neural networks.

基本上，定义人工智能(AI)并非易事。 这意味着专业非常广泛，不能局限于特定的研究领域。 AI试图通过创建模仿人类的认知过程来理解人类认知的工作方式。 人工智能正处于多个专业的十字路口，例如计算机科学，数学(逻辑，优化，分析，概率，线性代数)和认知科学。 此外，这些核心科学学科需要与所学专业的特定知识相结合，并且AI中的每种算法都通过语义分析，符号计算，机器学习，探索性分析等技术和方法的组合来提供，深度学习和神经网络。

Artificial Intelligence (AI) is defined as “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

人工智能(AI)被定义为“能够执行通常需要人类智力的任务的计算机系统的理论和开发，例如视觉感知，语音识别，决策和语言之间的翻译。

Marvin Lee Minsky, who is considered as one of the founding fathers of AI, defines it as follows: “the science of making machines do things that would require intelligence if done by men. It requires high-level mental processes such as: perceptual learning, memory and critical thinking.” In other words, artificial intelligence is the science of building computer programs that aims to perform tasks that would need some intelligence if they were done by human beings. As a result, human activities seem to completely covered such as moving from one place to another, learning, reasoning, socializing, and creativity. However, we are still far from creating a machine that would be able to match or outperform.

被认为是AI的创始者之一的Marvin Lee Minsky对其定义如下：“制造机器的科学从事的工作如果需要人工完成，就需要智慧。 它需要高级的心理过程，例如：感知学习，记忆和批判性思维。” 换句话说，人工智能是构建计算机程序的科学，该计算机程序旨在执行如果人类完成则需要一些智能的任务。 结果，人类活动似乎被完全覆盖，例如从一个地方移到另一个地方，学习，推理，社交和创造力。 但是，我们离创建能够匹配或胜过性能的机器还有很长的路要走。

Artificial Intelligence is the theory and development of computer systems which are able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation among languages.

人工智能是计算机系统的理论和发展，它能够执行通常需要人类智能的任务，例如视觉感知，语音识别，决策和语言之间的翻译。

AI includes some principles such as problem definitions, algorithms, and processes for extracting non-obvious, useful patterns and actionable insight from large data sets. The term of data science is closely connected to “Machine Learning” as well as “Data Mining”. Machine learning (ML) focuses on the design and evaluation of algorithms for extracting patterns from data, and Data Mining generally copes with the analysis of structured data. Data science; on the other hand, also takes into account other challenges such as the capturing, cleaning, and transforming of unstructured data, the use of big data technologies to store and process big, unstructured data sets, as well as questions related to data ethics and regulation.

AI包括一些原理，例如问题定义，算法和从大型数据集中提取非显而易见，有用的模式和可行见解的过程。 数据科学这个术语与“机器学习”以及“数据挖掘”紧密相关。 机器学习(ML)专注于设计和评估用于从数据中提取模式的算法，并且数据挖掘通常可以处理结构化数据的分析。 数据科学； 另一方面，还考虑了其​​他挑战，例如非结构化数据的捕获，清理和转换，使用大数据技术存储和处理大的非结构化数据集，以及与数据道德和法规相关的问题。

Machine Learning (ML) is the study of computer algorithms that enhance automatically through experience. It is seen as a subset of Artificial Intelligence (AI). Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do.

机器学习 ( ML )是对计算机算法的研究，该算法可通过经验自动增强。 它被视为人工智能(AI)的子集。 机器学习算法基于样本数据(称为“训练数据”)建立数学模型，以便进行预测或决策而无需明确地编程。

Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

数据挖掘是在大型数据集中发现模式的过程，该模式涉及机器学习，统计数据和数据库系统相交处的方法。

Additionally, Artificial Intelligence is used in lots of FinTech solutions. It would be a great solution for the some challenges faced by many businesses such as customer experience, personalization, loyalty building, and fraud detection. In the early 90s, AI and Machine Learning (ML) appeared on Wall Street along with the first hedge funds; however, there was still no significant breakthrough. It appeared only with the increased availability of data, generally with the spread of the Internet. Since then, there has been an extremely quick evolution of Operating Systems, taking advantage of the growing capabilities of machines. Recently, AI affects every area of a bank’s operations as well as the work of departments that we often forget about in the context of using technology in the financial sector, such as corporate core aspects, including even human resource team work.

此外，人工智能还用于许多FinTech解决方案中。 对于许多企业面临的一些挑战，例如客户体验，个性化，建立忠诚度和欺诈检测，这将是一个很好的解决方案。 在90年代初期，人工智能和机器学习(ML)与第一批对冲基金一起出现在华尔街。 但是，仍然没有重大突破。 仅随着数据可用性的提高(通常是随着Internet的传播)而出现。 从那时起，就利用了计算机不断增长的功能，对操作系统进行了非常快速的发展。 近年来，人工智能影响着银行运营的各个领域以及我们在金融领域使用技术时常常忘记的部门工作，例如企业核心方面，甚至包括人力资源团队工作。

用例 (Use cases)

Fundamentally, there are some use cases for using AI in banking for developing and improving the process and activities. The use cases are classified in three categories. These topics can highlight the potential areas of opportunities for the banking sector.

从根本上讲，在银行业使用AI来开发和改进流程和活动有一些用例。 用例分为三类。 这些主题可以突出显示银行业机会的潜在领域。

1. Improving customer interaction and experience. For example, chat-bots, voice banking, robo-advice, customer service improvement, bio-metric authentication and authorization, customer segmentation (e.g., by customized website to ensure that most relevant offer is showed), targeted customer offers.

   改善客户互动和体验。 例如，聊天机器人，语音银行， 机器人咨询 ，客户服务改进，生物识别和授权，客户细分(例如，通过定制网站以确保显示最相关的报价)，目标客户报价。

2. Enhancing the efficiency of banking processes and operations. For instance, process automation or optimization, reporting, predictive maintenance in IT, complaints management, document classification, automated data extraction, Know-Your Customer document processing, credit scoring, etc.

2.提高银行业务流程和运营的效率。 例如，流程自动化或优化，报告，IT中的预测性维护，投诉管理，文档分类，自动数据提取，“了解您的客户”文档处理， 信用评分等。

3. Improving security purposes and risk control. For instance, enhanced risk control, compliance monitoring, different types of anomaly detection, Anti-Money Laundering detection and monitoring, system capacity limit prediction, support of data quality assurance, fraud prevention, payment transaction monitoring, cyber risk prevention.

3.改善安全目的和风险控制。 例如，增强的风险控制，合规性监视，不同类型的异常检测，反洗钱检测和监视，系统容量限制预测，数据质量保证支持， 欺诈预防 ，支付交易监视，网络风险预防。

机器人咨询：用于客户交互的AI(示例) (Robo-advice: AI for customer Interaction (an example))

As a matter of fact, Robo-advisors are automated platforms that support algorithm-driven financial and investment management advice. This start from the information collected from individuals, and uses a mixture of various technologies such as cognitive systems, machine-learning, natural language processing, expert systems, and artificial intelligence algorithms. The robo-advisor can be able to suggest, automatically or with a financial advisor’s support, possible investment solutions, tailored to the client’s expectations and needs. This method enables a great consumer-experience particularly for those customers that prefer digital interactions and the “do-it-yourself” approach by providing contextualized products and experiences, providing targeted financial advice, and diminishing the cost for consumers.

实际上，机器人顾问是支持算法驱动的财务和投资管理建议的自动化平台。 这始于从个人收集的信息，并结合了多种技术，例如认知系统，机器学习，自然语言处理，专家系统和人工智能算法。 机器人顾问可以自动或在财务顾问的支持下为客户的期望和需求量身定制可行的投资解决方案。 这种方法通过提供上下文相关的产品和体验，提供有针对性的财务建议并降低消费者的成本，特别是对于那些更喜欢数字交互和“自己动手”方法的客户，提供了极大的消费者体验。

信用飙升：用于银行运营的AI(示例) (Credit soaring: AI for bank operation (an example))

In fact, credit scoring is not a new issue, and was actually one of the first application of statistical modelling in the banking sector. Nowadays, with the objective of measuring the credit worthiness of their clients, banks rely on gathering transnational data, statistical analysis, decision trees, and regression to better estimate a consumer’s credit risk and evaluate whether they will be able to repay a loan. The use of AI technology enables more accurate scoring and allows for enhanced access to credit by decreasing the risks and the number of false-positives and false-negatives. This will help banks to illustrate the most suitable debt plan for their customers. Furthermore, it ensures banks appropriately handle credit risk that is vital for financial stability. This is notably significant as there exist a number of supervisory requirements in this area, including the European Banking Authority Regulatory Technical Standards On Assessment Methodology for internal rating based (IRB) Approach . These technical standards aim to make sure consistency in models’ outputs and comparability of risk-weighted exposures.

实际上，信用评分并不是一个新问题，实际上是银行业中统计建模的最早应用之一。 如今，为了衡量其客户的信用水平，银行依靠收集跨国数据，统计分析，决策树和回归分析来更好地估计消费者的信用风险并评估他们是否能够偿还贷款。 AI技术的使用可实现更准确的评分，并通过降低风险和错误肯定和错误否定的数量来增强获得信贷的机会。 这将帮助银行为客户说明最合适的债务计划。 此外，它确保银行适当地处理对金融稳定至关重要的信贷风险。 这是非常重要的，因为在此领域存在许多监管要求，包括《欧洲银行管理局基于内部评级的评估方法论的监管技术标准》(IRB)方法。 这些技术标准旨在确保模型输出的一致性和风险加权风险的可比性。

防欺诈：出于安全目的的AI(例如) (Fraud prevention: AI for security purposes (an example))

As we know, identity theft, fraud, and security breaches are common to the banking sector because the sensitive personal data and money involved. Data security is essential to have a successful bank operations and maintain customer trust. Naturally, organizations use AI banking that is able to detect fraud quickly and more accurately without the risk of human errors overlooking any data or misunderstanding patterns. AI in banking detects fraud by referring to a pre-defined set of rules and by analyzing an individual’s past behavior. For instance, if someone who has previously made just only small purchases suddenly makes a very large one, the machine would flag that as fraud and contact the customer right away. Besides, AI is being used to authenticate and identify customers when they engage with their bank. So, banks are interested in investing in AI as a cyber-security tool to better prevent future cyber-attacks.

众所周知，身份盗窃，欺诈和安全漏洞对于银行业很常见，因为涉及敏感的个人数据和金钱。 数据安全对于成功开展银行业务并保持客户信任至关重要。 自然，组织使用能够快速，准确地检测欺诈的AI银行业务，而不会因人为错误而忽略任何数据或误解模式。 银行中的AI通过参考预定义的规则集并分析个人的过去行为来检测欺诈。 例如，如果以前只购买少量商品的人突然购买了很多商品，则该机器会将其标记为欺诈并立即与客户联系。 此外，当客户与银行打交道时，还可以使用AI进行身份验证和识别。 因此，银行有兴趣投资AI作为一种网络安全工具，以更好地预防未来的网络攻击。

人工智能在银行业中的优势 (The Benefits of AI in Banking)

There are some benefits can be indicated for utilizing AI in banking as follows:

在银行中使用AI有以下一些好处：

1. Decrease in operational costs and workload

1.减少运营成本和工作量

Initially, merging AI banking into operations, banks will diminish the needs for manual data entry and other human processes, which can probably lead to some errors. This not only saves time for the individual and the bank, but also eliminates costly mistakes. So, moving to conversational AI choices like virtual assistants will free workers from answering standard questions and managing basic transactions more appropriately. In contrast, bank workers can focus on higher-value tasks, such as deepening customer relationships and matching customers to the right services for their needs.

最初，将AI银行业务合并到运营中，银行将减少对手动数据输入和其他人工流程的需求，这可能会导致一些错误。 这不仅为个人和银行节省了时间，而且消除了代价高昂的错误。 因此，转向诸如虚拟助手之类的对话式AI选择将使工作人员从回答标准问题和更适当地管理基本交易中解放出来。 相反，银行工作人员可以专注于更高价值的任务，例如加深客户关系并为客户提供适合其需求的正确服务。

2. A new age of regulatory control

2.监管控制的新时代

It is clear that banks are already one of the most highly regulated institutions in the world and must comply with strict government regulations in order to prevent defaulting or not catching financial crimes within their systems. With using AI’s, the ability to better detect fraud through behavioral analysis and integration with cyber-security systems would be improved, and banks can catch financial crimes much more faster with greater accuracy than humans, which puts them in increasingly greater compliance with regulations. In addition, it decreases the bank’s risk. On top of auditing customer behavior, AI in banking can log key patterns and other information for reporting to regulatory systems. Also, as Machine Learning in banking is used more frequently, expect to see financial regulations evolve with these changes.

显然，银行已经是世界上监管最严格的机构之一，必须遵守严格的政府法规，以防止违约或在其系统内未发现金融犯罪。 通过使用人工智能，将可以改善通过行为分析以及与网络安全系统集成来更好地检测欺诈的能力，并且银行可以比人类更快地更准确地查处金融犯罪，这使它们越来越遵守法规。 另外，它降低了银行的风险。 除了审核客户行为，银行业的AI可以记录关键模式和其他信息，以向监管系统报告。 另外，随着银行中机器学习的使用越来越频繁，人们期望看到金融法规随着这些变化而发展。

3. Enhanced customer experience

3.增强客户体验

Generally, AI in banking will be able to serve their customers faster with more productivity at all times of the day. Answers to questions and the ability to enact basic transactions will be at the customer’s fingertips. The trust between customers and their bank will likely grow over time via securer data and better regulatory compliance. By using AI, we can be able to provide personalized insights and connect customers to the right products and services for their needs at the time that they need them. Thus, the relationship between banks and customers will be evolved.

通常，银行中的AI将能够在一天中的所有时间更快地为客户提供更高的生产率，并为其提供服务。 问题的答案和进行基本交易的能力将在客户的指尖。 随着时间的推移，客户和银行之间的信任可能会通过更安全的数据和更好的法规遵从性而增长。 通过使用AI，我们能够提供个性化的见解，并在客户需要时将其连接到满足其需求的正确产品和服务。 因此，银行和客户之间的关系将得到发展。

4. Boosting customer engagement

4.促进客户参与

Another significant point is that Artificial intelligence can assist in the creation of customized and intelligent products and services, with new features, more intuitive interactions like speech and advisory skills like personal financial management.

另一个重要的意义是，人工智能可以通过新功能，更直观的交互(例如语音)和咨询技巧(例如个人财务管理)来帮助创建定制的智能产品和服务。

结论 (In conclusion)

Nowadays, even though Artificial Intelligence has had some outstanding aspects for baking sector, there are some challenges are still remained to tackle like ethical considerations. This article some aspects and reasons for using Artificial Intelligence as an advanced issue in the banking sector.

如今，尽管人工智能在烘焙领域具有一些杰出的表现，但是仍然需要面对一些挑战，例如从伦理角度考虑。 本文将人工智能作为银行业中的高级问题的一些方面和原因。