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      "body": "Congratulations @xiangdong! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@xiangdong/birthday1.png</td><td>Happy Birthday! - You are on the Steem blockchain for 1 year!</td></tr></table>\n\n<sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@xiangdong) and compare to others on the [Steem Ranking](http://steemitboard.com/ranking/index.php?name=xiangdong)_</sub>\n\n\n###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!",
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      "body": "When it comes to robots, most people think of Star Wars or the automobile assembly line, the assembly line, mobile heavy huge agile machine parts, welding or fixed bolt is fixed on the chassis.\nNow software robot process automation (RPA) is entering the field of enterprise financing.\n\nIn terms of finance, we have realized automation through the implementation of general ledger and enterprise resource planning (ERP) system and Microsoft Excel and other spreadsheet programs.\nFinancial process, however, many enterprises still stays in the 20th century, the enterprise finance team spent about 80% of the time manually collecting, data verification and consolidation, and advanced tasks such as for analysis and decision-making leaving only about 20% of the time.\nRPA will bring a new wave of digital transformation for enterprise financing.\nInstead of using programming software to automate certain tasks, RPA USES artificial intelligence (AI) to train and teach software robots to handle transactions, automatically monitor compliance and audit processes.\nMachine learning is a form of artificial intelligence, by building can, according to new data and experience in study and improve the system automatically, without the need for a dedicated programming to implement the financial automation, thus further realize financial automation.\n\nAccording to the Grant Thornton, according to a recent survey shows that the enterprise's financial executives are adopting some of these new forms of financial Shared department implementation process automation, arrange and advanced artificial intelligence analysis in their highest priority project investment, and many people have begun to digitize transformation.\n\n\n\nIn transaction processing, the cfo can use RPA to optimize its accounts payable and accounts receivable processes.\nCompanies collect information from customers on how to affect cash flow and customer satisfaction.\nA CFO's company may have done well, but AI has added this expertise to allow the company to gather information as quickly as possible while maintaining good customer relationships.\n\nAt the same time, the customer may be repeated payment, incorporating more than one invoice for a payment or pay the wrong amount, manual correction in the general ledger system, may cause the error will need a lot of time to sift through invoice, find out the source of the error or tracking customers to solve the problem.\nArtificial intelligence and machine learning can support financial teams by automatically tracking information, finding the source of general ledger problems, and suggesting which payments match which invoices.\n\nArtificial intelligence can also reduce financial risk by stopping the wrong payments to suppliers almost in real time.\nCompanies that pay $500m a year, for example, could leak anywhere from $500,000 to $2.5m from mistakes, fraud and abuse.\nToday, large organizations rely on manual sampling audits of invoices and payments to reduce risk.\nHowever, these sample audits contain only a small number of transactions, so errors or waste are rarely found in the process.\nHowever, ai can expand the scope of compliance monitoring and analysis, save millions of dollars, prevent duplicate and fraudulent payments, and curb the risk of cash outflows.",
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      "body": "对于许多考虑如何实现业务流程自动化以提高生产力的公司来说，机器人过程自动化（RPA）是他们首先考虑的技术之一。\n\n自动化业务的最快速和最容易的机会 - “低挂果” - 将来自RPA。这是近年来最明智的技术演进之一，它提供了一种降低成本，提高生产力，甚至提高效率和准确性的方法。\n\n然而，对于许多企业来说，关于RPA实际可以实现什么以及它的局限性有很多混淆。在考虑实施RPA时，最好有明确的期望，以便最好地确定如何利用它来获得战略优势。\n\n了解RPA周围的机会的最佳方式是想象一位员工在日常工作中执行数十项平淡无奇的重复性任务。随着时间的推移，这些任务会变得沉闷乏味，而人类工作者的工作效率会降低或出错。\n\nRPA可以更快，更准确地执行这些任务，而不会疲劳。人力资源工作者专注于需要更多批判性思维，创造力和与客户更高层次接触的任务。\n\n作为基于RPA自动化主要目标的流程的一个很好的例子是发票验证流程。这个过程可能涉及一位人类用户从三个不同的供应商门户下载发票，将它们上传到ERP系统中，并使用相应的采购订单验证这些值，并最终在系统上批准或拒绝这些值。通过一定量的脚本，这个过程可以很容易地结合到RPA软件中。\n\nRPA是一种应用技术来配置机器人或计算机以使用和解释现有应用程序来处理事务，操纵数据或触发响应以及与其他数字系统通信的方式。\n\n为了简化，RPA是一种“记录和回放”技术，其中机器人（实际上是安装在机器上的软件）被教授如何使用软件来执行一些任务并且机器人周期性地执行它，使其变得简单有效的自动化方法\n\n传统上，同一发票验证流程的自动化将涉及与三个不同供应商系统的集成，这些供应商系统必须与一些验证逻辑集成到ERP系统中。虽然这两种流程的结果都是相同的，但传统的自动化路线还伴随着软件开发的额外任务，与IT团队合作开放防火墙端口，专用于运行软件的服务器以及一些维护工作。\n\nRPA是一个更简单的过程。该软件还可以自动执行许多其他流程，因为它可以运行24 x 7，并且很可能每周只需要处理发票验证几个小时。\n\nRPA的路障\n\n虽然RPA提供了非常快速，非侵入性的自动化手段，但它有其局限性。为了最大限度地提高RPA的实用性，自动化流程需要基于定义明确的标准操作程序（SOP），其中决策规则具有非常明确的参数。在任何基于RPA的自动化战略中，将较大的流程分为“可自动化”和“人为干预”区域并更新流程以实现最大程度的自动化非常重要。然而，将每个过程完全自动化可能并不现实这是因为某些过程是相当程序化的，而有些过程可能需要应用人类工作者的知识并作出判断。如果大量的知识不能有效地编入算法，RPA将无法自动化某些过程，这将需要路由到人类用户。对于需要应用人类判断的领域，RPA无效。\n\n由RPA驱动的过程的可自动化部分由此完全变为规则和算法驱动，很难使它们变得“智能”或“智能”。在某些情况下，这可以将过程的可自动部分缩小为因为回报可能无法证明RPA投资的合理性，因此应用RPA是不可行的。\n\n这样做的一个例子就是将问题票据路由到合适的组的自动化过程。如果路由基于票证类别和发行票证的某些字段，RPA肯定能解决自动化问题。另一方面，如果它涉及读取票据描述的内容并应用人类知识来决定哪个组应该处理该票证，那么突然自动化该过程看起来是不可行的。\n\n机器学习（ML）如何提供帮助\n\n机器学习（ML）是一项正在成熟的新兴技术，可以应用于解决现实生活中的问题。ML的工作原理是将大量数据（或知识）捕获到某种形式的数学模型中。该模型可以用来应用知识来解决问题。\n\n例如，如果我们开发一个具有一年票券描述及其相应路由信息的ML模型，它可用于预测新票证的路由信息​​。ML有能力根据先前的行动进行学习，并运用这些知识对当前行动做出决定。\n\nML可应用于存在大量历史数据的大量问题，这些数据可以预测某些区域或做出决定。这不像开发一种算法; 它基于建立知识库。如果我们考虑票务路线问题，考虑到过去一年中每次票价增加的算法，开发算法将非常困难。不过，ML方法可以非常有效地解决这种情况。\n\n强大的组合\n\nML与RPA的结合有助于克服RPA的局限性，因为它可以从历史数据中建立知识库，并将其用于决策制定和预测。\n\n如果我们采用票务路由过程的相同示例，则现在看起来完全可行，RPA过程将采用票证描述并调用ML预测服务来获取路由信息以继续自动化过程。这就像是在其他程序性RPA过程中引入智能代理。通过定期重新训练并保持最新知识，保持丰富ML模型也是可行的。\n\n\n\n机器人过程自动化是一项激动人心的技术，与机器学习相结合，推动节约和提高效率的可能性是无止境的。虽然这些组合技术构成了一个强大的自动化解决方案，但还需要完成一些工作才能让最终用户访问。但是，凭借正确的专业知识，企业可以实现真正智能的自动化解决方案，从而获得他们一直在寻找的结果。",
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