Data scientists across different roles today require capabilities to source data from different sources, build models and operationalize machine learning insights. To meet this growing demand, artificial intelligence and machine learning platforms are evolving quickly to keep pace with a wide range of applications and industry use cases. Let’s take a look at some of these use cases of AI/ML for Oracle E-Business Suite:
Use Cases of AI/ML for Oracle E-Business Suite
1. Effective Loyalty Programs
Loyalty programs are designed to increase customer engagement and decrease customer churn, but they are only effective when customers actively participate in these programs. Implementing the right offers for loyalty programs can significantly improve customer engagement and satisfaction, but it is difficult to predict in advance which activities are most likely to be effective.
With machine learning, businesses can develop personalized loyalty schemes, resulting in increased customer participation, better experiences, and a more active customer base. Automated machine learning platforms can quickly develop accurate models that can predict customer preferences with a single click which ultimately improves your loyalty program adoption, enhancing customer engagement and satisfaction.
2. Customer Churn
Getting new customers is important, but it is equally important to retain existing customers – and also more cost-effective. But customer retention teams are struggling with limited resources and can’t give the same level of attention to every customer. They need to be able to determine which customers are more likely to leave in order to focus their retention efforts.
AI/ML-based algorithms can accurately predict which of your current customers are most likely to leave by identifying patterns in historical customer activity. Your retention team can focus their efforts on these customers who are most at risk and offer them incentives to continue using your products/services. Improving customer retention can help your business scale faster, grow profits, and build brand value.
You can not only predict which customers are most likely to stop purchasing your products or services, but also automatically identify the reasons why each customer is likely to leave. You can get a better understanding of the factors driving your churn rate and modify your business processes accordingly, as well as personalize your retention efforts.
3. Fraud Detection
There is fraud in many departments of both governments and businesses, from mortgage fraud to tax evasion. It’s inefficient, labor-intensive, and time-consuming to investigate every instance of fraud, and examining each case is not feasible due to the overwhelming number of cases. Fraudsters are also getting more sophisticated with their tactics evolving quickly, often making it very difficult for the authorities to keep up.
With AI/ML-powered algorithms, analysts can build the right model to analyze large datasets and detect instances of fraud within minutes. Using the data from previous fraud cases can serve as the historical basis for future model prediction. Governments and businesses can leverage their existing IT investments, resulting in significant time and cost savings.
4. Fraudulent Claim Modeling
Fraudulent insurance claims present a huge challenge, but it is too expensive and inefficient to investigate every suspected fraudulent claim. Investigations can also be a harrowing experience for genuine customers and might cause some churn.
Machine learning can help you build accurate predictive models to identify and prioritize possible fraudulent activity, enabling you to investigate only those incidents that are likely to be fraudulent. This allows you to focus your resources in the right areas with the greatest ROI, and you can also improve the customer experience by processing genuine claims faster.
5. Product Personalization
Consumers are increasingly demanding personalized brand experiences and businesses are expected to cater to these demands – but it is not feasible for your customer service teams to know the individual preferences of hundreds of customers.
Machine learning algorithms can analyze Oracle EBS data to accurately identify the preferences and purchasing behaviors of individual consumers, and predict which offers will be most attractive to each customer, resulting in more targeted marketing campaigns and greater brand value.
With cyber-attacks becoming more sophisticated and widespread, it’s important for both businesses and governments to analyze historical data on any accounts, digital assets, and equipment that may have been attacked. This can help them predict, identify, prevent and prepare for potential new threats.
AI/ML can be applied to Oracle EBS datasets containing historical network threats and penetration data, server log data, application logs, and other information sources to deploy models that can predict incoming threats in real-time. The speed and accuracy of machine learning allow analysts to continually monitor potential cyber-attacks, and to take preventive action against potential cyber threats before they occur.