Fintech is growing rapidly across the globe and adopting advance technologies to meet the market demand. From developing first-gen fintech digital products to record basic transactions computers to including Artificial Intelligence (AI) and Machine Learning (ML) to enhance their digital products and experience, the industry has undoubtedly experienced unprecedented growth and technical advances.
There are more than 30,000 FinTech SaaS companies globally, and many of these products rely wholly or partially on AI and ML. Let’s see how AI and ML are reshaping the present SaaS financial technology and what these changes mean.
What is AI and Machine Learning?
Artificial Intelligence, involves equipping computers with diverse data points and utilizing human intelligence to create self-sufficient systems or mechanisms that can mimic human actions on the data. A simple AI tool can be 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.
The Impact of AI and Machine Learning on SaaS FinTech
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:
Financial risk management
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.
Key Benefits of AI and ML in SaaS Financial Technology
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.
Enhanced Decision-Making Capabilities
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.
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