一、开篇
和每个领先行业一样，由于市场需求的变化和技术的进步，金融技术（FinTech）市场经历了长期的演变。因为许多依赖金融技术的公司转换了运营模式，参加这场技术变革的盛宴。
这一演变无疑形成了几个令人兴奋的趋势，从纸制记录日常金融交易到建立模拟计算设备，从开发第一代计算机到将人工智能（AI）和机器学习（ML）纳入金融科技数字产品，该行业经历了前所未有的增长。
全球有超过30,000家金融科技SaaS公司，其中许多品牌现在完全或部分依赖AI和ML技术。
通过这篇文章，让我们深入了解AI和ML是如何重塑现在的SaaS金融技术的，以及这些变化对于我们而言意味着什么。
二、AI和机器学习是什么？
人工智能和机器学习是目前不断成为新闻头条的热门词汇。它们听起来可能并不熟悉，因为大多数人都在交替使用它们，所以让我们先定义它们。
人工智能（AI）是人工智能的缩写，它通过为计算机配备不同的信息，利用人类的智慧来创造自给自足的系统或机制，同时它还可以模仿人类在物理世界中的行动。一个简单的人工智能机器人是iPhone上的Siri或数字家用设备中的Alexa。这些人工智能程序被设计用来解决人类和计算机产生的问题；它们的主要功能是完成任何给定的任务，并在给定的时间范围内成功完成目标。
机器学习是一种技术，使计算机能够理解新的场景，并在面对更复杂的情况时完善其决策能力。机器学习利用计算机算法和分析方法建立预测模型，帮助解决不同的问题，特别是金融领域的问题。
三、AI和机器学习对SaaS金融科技的影响
如前所述，人工智能和机器学习通过开发有助于决策的预测分析，在今天的SaaS金融技术工具中发挥重要作用。这种人工智能的增值可以在各个领域感受到，从专业操作到普通用户。以下是人工智能和机器学习对SaaS金融技术的一些影响。
1、金融风险管理
银行和其他金融技术组织一直在寻找最小化风险的模型。基于人工智能的决策树方法通过为复杂和非线性的财务状况制定简单和可追溯的规则，从而使用这些规则影响风险管理。同时，支持向量技术有助于确定贷款的重要信贷风险。
2、收入预测
许多金融服务部门雇用了机器学习顾问，他们使用深度学习和机器学习技术，为其组织开发预测模型。
3、欺诈检测
由于消费者和资金安全无法得到完全保证，因此欺诈是许多银行都面临的问题。人工智能可以通过分析巨大的交易数据来发现隐藏的欺诈模式，从而帮助减少欺诈行为。它可以实时检测这种模式，并防止其发生。此外，机器学习的 “逻辑回归 “算法可以帮助理解欺诈模式并阻止其发生。
PayPal是使用人工智能进行欺诈检测的典型案例。PayPal使用机器学习算法来分析其平台的数据，并识别潜在的欺诈交易。
人工智能系统查看各种数据点，如交易地点、用于进行交易的设备、交易金额和用户在平台上的历史。
例如，如果交易是从一个通常不与用户账户相关联的设备进行的，或者如果交易金额比平时大得多，系统可能会标记该交易进行审查。PayPal的人工智能系统已被证明在检测欺诈方面非常有效。据该公司称，其系统可以检测出欺诈性交易，欺诈率仅占该公司收入的0.32%。这帮助PayPal每年避免了因欺诈而造成的数百万美元的损失。
4、客户支持
人工智能可以确保客户在正确的时间获得正确的金融信息。通过研究客户数据和重要的分析，人工智能可以根据客户的偏好或要求进行客户响应。SaaS品牌使用AI和ML的典型案例是Zendesk和Salesforce。他们的工具AnswerBot和Einstein可以理解客户的意图，并实时提供相关回应。该算法还能从每次互动中学习，并随着时间的推移变得更加聪明。
5、资产管理
像其他每个部门一样，人工智能和机器学习也影响了专业人士处理或管理金融资产的方式。有了人工智能，资产管理者可以自动制定客户报告和文件，提供详细的账户报表，并准确地执行更多的功能。
四、AI和ML在SaaS金融技术中的主要好处
将人工智能和机器学习纳入SaaS金融技术为整个行业带来了极大的利益。以下是整合人工智能（AI）和机器学习（ML）的一些关键点。
1、提高准确度
在引入机器学习技术之前，每天都有少量的金融交易被记录到账簿中。大量的交易和有限的理解能力导致了一些错误和不平衡的账户。人工智能和机器学习为准确性提供了空间，针对重复性的计算任务包括：账户平衡和账户分析，并保证这些计算工作的正确性。正因为这些新的进展，让结果更加准确，从而减少损失。
2、提高效率
在SaaS金融技术中使用人工智能和ML的另一个好处是提高效率，改善生产力，并减少完成任务所需的时间。使用人工智能聊天机器人来处理客户的要求，有助于提高客户支持的整体效率。
3、增强决策能力
人工智能和机器学习为SaaS技术的决策提供帮助。金融分析师可以很容易地分析数十亿的数据，研究股票的模式和趋势，并使用该技术做出战略性和有益的决定。
4、负担能力
几年前，只有富人才能负担得起个人财务顾问，这些顾问可以帮助富人管理财富和调节开支。但是，在基于人工智能的应用程序的当下，可以为任何人进行账单跟踪、股价预测、市场或加密货币分析，所有这些工作坐在家里就可以完成。
五、SaaS金融科技中人工智能和机器学习的挑战和风险
尽管将人工智能和机器学习纳入SaaS金融技术收益是显而易见的，但值得注意的是，同时也伴随着挑战。
包括如下风险：
1、投入
开发人工智能金融技术应用程序需要花费资金，为了收回这些成本，开发出的应用程序必须被公众使用。然而，与金融科技应用相比，人们更有可能在健身或食谱编撰的应用上花费50美元。
2、数据隐私
需要在应用价值、个人信息和数据隐私之间找到一个平衡点是相当难的。客户已经意识到数据隐私问题，并希望在注册时尽可能少地提供个人信息。如果你问了太多的问题或要求太多的设备访问，客户很可能会离开。如果几乎没有得到任何信息，又如何训练人工智能来开发更多的个性化功能呢？
3、算法和数据的偏见
人工智能和机器学习的成功往往受到数据偏见的挑战。这些偏见大多来自于没有机会接触到金融技术的少数群体，或者是训练人工智能的人类，他们的判断力出现偏差。偏见往往是由人类产生的——一旦输入就会传播到算法中。
六、结论
COVID-19事件以及相关政府举措带来工作场所的巨大变化，加速了全球范围内对尖端技术的采用。在封锁期间，人工智能驱动的企业不仅看到了生产力的提高，并推出了很多新的人工智能产品，跨领域的软件，以及对两者进行融合的用法。
由于人工智能和机器学习的不断发展，SaaS金融技术领域在未来几年可能会经历一场变革。这种变化将使更多的公司获得竞争优势，提高他们的财务业绩，并最终完成他们的财务管理业务目标。
翻译：
How AI and machine learning are reshaping SaaS fintech
The Beginning
Like every leading industry, the financial technology (FinTech) market has undergone a long period of evolution due to changing market demands and technological advances. Because many companies that rely on financial technology have shifted their business models to participate in this technological revolution.
This evolution has undoubtedly formed several exciting trends, with the industry experiencing unprecedented growth, from recording daily financial transactions on paper to building analog computing devices, from developing the first generation of computers to incorporating artificial intelligence (AI) and machine learning (ML) into fintech digital products.
There are over 30,000 fintech SaaS companies worldwide, and many of these brands now rely fully or in part on AI and ML technologies.
In this article, let’s take a closer look at how AI and ML are reshaping SaaS financial technology today, and what these changes mean for us.
What is AI and machine learning?
Artificial intelligence and machine learning are hot words that keep making headlines right now. They may not sound familiar, since most people use them interchangeably, so let’s define them first.
Artificial Intelligence (AI), short for artificial intelligence, uses human intelligence to create self-sufficient systems or mechanisms by equipping computers with different pieces of information, while it can also mimic human actions in the physical world. A simple AI robot is Siri on an iPhone or Alexa in a digital home device. These AI programs are designed to solve problems created by humans and computers; Their primary function is to complete any given task and successfully complete the goal within a given time frame.
Machine learning is a technique that enables computers to understand new scenarios and refine their decision-making abilities when faced with more complex situations. Machine learning uses computer algorithms and analytics to build predictive models that help solve different problems, especially in the financial sector.
Third, the impact of AI and machine learning on SaaS fintech
As mentioned earlier, AI and machine learning play an important role in today’s SaaS financial technology tools by developing predictive analytics that aid decision making. This added value of AI can be felt across a wide range of sectors, from professional operations to the average user. Here are some of the impacts of AI and machine learning on SaaS financial technologies.
Financial risk management
Banks and other financial technology organizations have been looking for models that minimize risk. The AI-based decision tree approach influences risk management by developing simple and traceable rules for complex and non-linear financial situations, thereby using those rules. At the same time, support vector technology helps to determine the important credit risks of loans.
Revenue forecast
Many financial services sectors employ machine learning consultants, who use deep learning and machine learning techniques to develop predictive models for their organisations.
Fraud detection
Fraud is a problem for many banks because the safety of consumers and money cannot be fully guaranteed. Ai could help reduce fraud by analyzing huge amounts of transaction data to spot hidden patterns of fraud. It can detect this pattern in real time and prevent it from occurring. In addition, machine-learning “logistic regression” algorithms can help understand fraud patterns and prevent them from occurring.
PayPal is a prime example of the use of artificial intelligence for fraud detection. PayPal uses machine learning algorithms to analyze data on its platform and identify potentially fraudulent transactions.
The AI system looks at various data points, such as the location of the transaction, the equipment used to make the transaction, the amount of the transaction and the user’s history on the platform.
For example, if the transaction was made from a device not normally associated with a user’s account, or if the transaction amount is much larger than usual, the system may flag the transaction for review. PayPal’s AI system has proven to be very effective at detecting fraud. According to the company, its system can detect fraudulent transactions, with the fraud rate accounting for only 0.32 percent of the company’s revenue. This has helped PayPal avoid millions of dollars in fraud losses each year.
Customer support
Ai can ensure that customers get the right financial information at the right time. By studying customer data and important analytics, AI can make customer responses based on customer preferences or requirements. Examples of SaaS brands using AI and ML are Zendesk and Salesforce. Their tools, AnswerBot and Einstein, can understand customers’ intentions and provide relevant responses in real time. The algorithm also learns from each interaction and gets smarter over time.
Asset management
Artificial intelligence and machine learning, like every other sector, affect the way professionals handle or manage financial assets. With AI, asset managers can automatically formulate client reports and documents, provide detailed account statements, and accurately perform more functions.
Fourth, the main benefits of AI and ML in SaaS financial technology
The integration of AI and machine learning into SaaS financial technologies has brought significant benefits to the entire industry. Here are some key points for integrating artificial intelligence (AI) and machine learning (ML).
Improve accuracy
Before machine learning was introduced, a small number of financial transactions were recorded into the ledger every day. The large number of transactions and limited understanding led to some errors and unbalanced accounts. Artificial intelligence and machine learning provide space for accuracy, for repetitive computing tasks such as account balancing and account analysis, and ensure that these calculations are correct. Because of these new developments, results are more accurate, thus reducing losses.
Improve efficiency
Another benefit of using AI and ML in SaaS financial technology is increased efficiency, improved productivity, and reduced time required to complete tasks. Using AI chatbots to handle customer requests helps improve the overall efficiency of customer support.
Enhance decision-making ability
Artificial intelligence and machine learning aid decision-making in SaaS technologies. Financial analysts can easily analyze billions of pieces of data, study patterns and trends in stocks, and use the technology to make strategic and beneficial decisions.
Affordability
A few years ago, only the wealthy could afford personal financial advisers, who could help them manage their wealth and regulate their spending. But in the current world of AI-based apps, bill tracking, stock price forecasting, market or cryptocurrency analysis can be done for anyone, all from home.
Challenges and risks of AI and machine learning in SaaS Fintech
While incorporating AI and machine learning into SaaS financial technology benefits is obvious, it is worth noting that it also comes with challenges.
Include the following risks:
Input
It costs money to develop AI financial technology applications, and in order to recoup those costs, the applications developed must be used by the public. However, people are more likely to spend $50 on fitness or cookbook apps than on fintech apps.
Data privacy
Finding the right balance between app value, personal information, and data privacy can be tricky. Customers are aware of data privacy issues and want to give as little personal information as possible when signing up. If you ask too many questions or require too many device access, the customer is likely to leave. How can AI be trained to develop more personalised features if there is almost no information?
Bias of algorithms and data
The success of artificial intelligence and machine learning is often challenged by data bias. Most of these biases come from minority groups who don’t have access to financial technology, or from humans trained in AI whose judgment is flawed. Biases tend to be created by humans — once input spreads to algorithms.
Conclusion
COVID-19 and related government initiatives have brought about dramatic changes in the workplace, accelerating the adoption of cutting-edge technology worldwide. During the lockdown, AI-powered enterprises not only saw increased productivity, but also introduced many new AI products, cross-domain software, and uses to blend the two.
The SaaS financial technology space is likely to undergo a transformation in the coming years due to the ongoing development of artificial intelligence and machine learning. This change will allow more companies to gain a competitive advantage, improve their financial performance and ultimately accomplish their financial management business objectives.
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