How AI and Machine Learning are Reshaping SaaS FinTech

Like every leading industry today, the Finance Technology (FinTech) market experienced evolution over time due to the change in market demand and the advancement in technology. This undoubtedly developed several exciting trends as many fintech-dependent companies switched operational models to match meetups. 

From recording daily financial transactions on paper to building analog computing devices, from developing first-gen computers to including Artificial Intelligence (AI) and Machine Learning (ML) to fintech digital products, the industry has undoubtedly experienced unprecedented growth.

There are over 30,000 FinTech SaaS companies globally, and many of these brands now rely wholly or partially on AI and ML.

For this article, let’s dive into how AI and ML are reshaping the present SaaS financial technology and what these changes mean.

Artificial Intelligence and Machine Learning are the current buzzwords that continually headline the news. These may not sound familiar to a vast number of people as most people use them interchangeably, so let's define them. 

AI, which stands for Artificial Intelligence, involves equipping computers with diverse information and utilizing human intelligence to create self-sufficient systems or mechanisms that can mimic human actions in the physical world.

A simple AI robot is Siri on the iPhone or Alexa in digital household devices. These AI programs are designed to solve human and computer-generated problems; their main function is to complete any given task and accomplish the goal successfully within the given time frame.

Machine Learning is the technology that allows a computer to understand new scenarios and perfect its decision-making prowess when faced with more complex situations. Machine Learning builds predictive models using computer algorithms and analytics that help address different problems, notably those in the financial sector.

As mentioned earlier, AI and machine learning play important roles in today's SaaS Financial technology tools by developing forecasting analytics that helps in decision-making. This AI value-add can be felt in every sphere, from professional operations to common users. Here are some of the few impacts of AI and machine learning on SaaS Financial technology:

Banks and other Fintech organizations are constantly looking for models that can help minimize risk. The AI-based decision tree method has impacted risk management by developing easy and traceable rules for complex and nonlinear financial situations. Also, support vector technology helps to determine important credit risks in terms of loan allocation. 

Many financial services employ machine learning consultants who use deep learning and other ml technologies to develop forecasting models for their organizations.

Fraud is a major drawback facing many banks, as consumer and fund security cannot be fully guaranteed. AI can help reduce fraud by analyzing huge transactional data to uncover hidden fraud patterns. It detects this pattern in real time and prevents it from happening. In addition, machine learning's "Logistic Regression" algorithms can help understand fraud patterns and stop them from happening.

One example of a company using AI for fraud detection is PayPal. PayPal uses machine learning algorithms to analyze data from its platform and identify potentially fraudulent transactions.

The AI system looks at various data points, such as the location of the transaction, the device used to make the transaction, the transaction amount, and the user's history on the platform. 

For example, if a transaction is being made from a device that is not typically associated with the user's account or if the transaction amount is significantly larger than usual, the system may flag the transaction for review.

PayPal's AI system has been shown to be highly effective in detecting fraud. According to the company, its system can detect fraudulent transactions with a fraud rate of only 0.32% of the company’s revenue. This has helped PayPal to prevent millions of dollars in losses due to fraud each year.

AI plays a huge role in ensuring customers can access the right financial information at the right time. By studying customer data and important analytics, AI can recommend replies and services according to the customer's preference or request.  A prime example of SaaS brands using AI and ML includes Zendesk and Salesforce. Their tool, AnswerBot, and Einstein, can understand the customer’s intent and provide a relevant response in real time. The algorithm also learns from every interaction and gets smarter over time.

Like every other sector, AI and machine learning have also affected how professionals handle or manage financial assets. With AI, asset managers can automatically develop client reporting and documentation, provide a detailed statement of accounts, and perform many more functions accurately.

Incorporating AI and machine learning into SaaS Financial technology has offered tons of benefits. Here are some of the key importance of the integration of artificial intelligence (AI) and machine learning (ML):

Before the advancement of technology, a handful of financial transactions that go in daily were recorded in ledgers by a selected few. The high influx of transactions and the inability of human intelligence to fully comprehend them led to errors and unbalanced accounts. 

AI and machine learning has provided room for better accuracy as repetitive calculative tasks like account balancing, and analysis are now error-free. With these new advancements, results are more accurate, reducing loss.

Another key 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 customers' requests helps improve overall efficiency in customer support.

AI and machine learning has benefited decision-making in SaaS technology. Financial analysts can easily analyze billions of data, study stock patterns and trends, and use the technology to make strategic and beneficial decisions. 

A few years ago, only the rich could afford a personal financial advisor to help manage their wealth and regulate their expenses. But AI-based applications now allow for bill tracking, stock price predictions, and market or crypto analytics all from the comfort of your home. 

Although the benefits of incorporating AI and Machine learning into SaaS financial technology have proved to be beneficial, it's worth noting that there are some accompanied challenges. Some of these risks include:

Developing AI Fintech apps cost money, and to recuperate these costs, the apps must be used by the public. However, people are more likely to speed $50 on fitness or recipe-compilation apps than Financial Technology apps. 

It is difficult to find a balance between offering values, requesting personal information, and promoting data privacy. Customers are already aware of their privacy and would love to give as little information as possible during registration. If you ask too many questions or demand too much device access, customers are likely to leave, and if you get little to no information, how do you feed the AI to develop more personalized features? 

The success of AI and machine learning is often challenged by data bias. Most of these biases come from minority groups with no access to financial technology or poor human judgment that is being fed to the AI. These biases are often human-derived — once inputted — they propagate into the algorithm. 

The massive change in workplace practices brought about by COVID-19 and other related government initiatives accelerated the adoption of cutting-edge technology on a global scale. During the lockdowns, the AI-powered businesses only saw an increase in productivity also, there were lots of new AI products, cross-disciplinary software, and mergers.

The SaaS financial technology sector may undergo a transformation in the next years as a result of AI and machine learning. This change will allow more companies to gain a competitive edge, enhance their financial performance, and ultimately accomplish their objectives in their financial management operations.

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