# How Automated Machine Learning Can Improve Business
## 3 Results and Discussion
### Impact of AutoML on Business Operations
#### 3.4.7 Fraud Detection in Digital Payment Systems  
The rise of digital payment systems has brought about new challenges in fraud detection. AutoML has emerged as a powerful tool in combating fraudulent activities. Traditional fraud detection methods often rely on predefined rules and manual reviews, which can be prone to errors [24]. As fraudsters become more sophisticated, there is a growing need for more advanced and dynamic detection techniques. AutoML addresses this need by automating the creation of machine-learning models that can detect fraudulent patterns in real-time, even as they evolve [71]. These models can analyze vast amounts of transaction data, identify anomalies, and flag potentially fraudulent activities with high accuracy. AutoML models can adapt to emerging fraud tactics by continuously learning from new data, ensuring that detection systems remain effective over time [72]. This adaptability is crucial in digital payments, where fraudsters constantly develop new methods to bypass security measures. Moreover, AutoML can enhance the efficiency of fraud detection processes by reducing the number of false positives [73]. This would allow businesses to focus their resources on genuine threats. Ultimately, AutoML helps protect businesses and consumers from financial losses and strengthens the overall security of digital payment systems by providing a more accurate and automated approach to fraud detection.  
3.4.8. Measuring Marketing and Business Performance  
Traditionally, evaluating the success of marketing campaigns and business initiatives has involved collecting vast amounts of data from various sources, analyzing it, and deriving insights that inform future decisions [74]. However, [PERSON] and [PERSON] [75] indicate that AutoML transforms this process by automating the entire workflow, from data collection to analysis and reporting, providing businesses with rapid and accurate assessments of their marketing and business performance. One of the key benefits of AutoML in this context is its ability to process large datasets from multiple channels, including digital marketing platforms, sales records, customer feedback, and social media interactions [76]. By integrating and analyzing this data, AutoML models can identify patterns and trends that would be difficult for human analysts to detect. This enables businesses to understand how different marketing strategies are performing in real-time, allowing for quick adjustments to optimize outcomes [77]. For instance, AutoML can help identify customer segments that are most responsive to specific campaigns. Such insights enable businesses to tailor their marketing efforts for maximum impact.  
Moreover, AutoML facilitates the continuous monitoring of key performance indicators (KPIs), providing businesses with ongoing insights into their operations. This real-time analysis allows companies to measure the effectiveness of their strategies more accurately and make data-driven decisions that enhance overall performance [78]. According to [PERSON] et al. [79], AutoML also reduces the reliance on manual reporting and the potential for human error by automating the process of measuring marketing and business outcomes, ensuring that decisions are based on accurate, up-to-date information. Moreover, AutoML's predictive capabilities enable businesses to forecast future performance based on historical data to anticipate market trends and adjust their strategies proactively [80].  
This forward-looking approach improves the efficiency of marketing and business operations and supports long-term strategic planning. As a result, companies can better align their marketing initiatives and business objectives, leading to improved ROI and sustained competitive advantage [81].