Exploring Real-World AI Use Cases

Unlike other energy sources, electricity cannot be saved or used later. Thus, there needs to be consistent predictions for daily usage. Otherwise, electricity distribution companies could incur massive hits to their bottom line and reputation..

Energy suppliers are obligated to share their forecasts with the system operator to maintain system balance. Renewable energy production is especially hard to predict because it depends on uncontrollable factors such as weather conditions.

By analyzing vast amounts of data, FinTechs can use machine learning algorithms to identify patterns and anomalies—empowering proactive AI-powered fraud detection. AutoML helps FinTechs protect customers, reduce financial losses, and maintain the integrity of their platforms—and the security of their customers.

FinTechs specializing in wealth management want to augment their targeted marketing to attract and retain high-net-worth clients. By leveraging AutoML, FinTechs can analyze customer data—including risk tolerance, demographics, investment preferences, and financial goals. The more FinTechs can tailor personalized marketing campaigns—the better the conversion rates and long-term relationships with clients.

Insurers want to hit the maximum cost optimization in claims—without reducing their standards of quality. Collecting the most up-to-date data for an insurance policy and benchmarking similar cases is an outstanding way to meet optimization benchmarks.

People can claim the cause of loss that is not covered by their insurance or try to obtain some advantage they are not entitled to with the fraudulent actions.

In automated underwriting, the risk perspective of the system and the classification models of the client profiles are so crucial to determine the right assessment and approval path of the related issue. If the model construction of the system doesn’t cover all the issues, there will always be misevaluated cases. Keeping the model always up-to-date is another challenge.

The primary concern of pricing the policies is that one approach does not fit them all with traditional methods while calculating the payout price. Meaning that people with different backgrounds and incomes should be evaluated with different proposed prices.

Forecasting sales is essential for retail companies since it directly affects identifying benchmarks and determining incremental impacts of new initiatives, planning resources in response to expected demand and projecting future budgets.

The traditional market research into sales and customer satisfaction done by companies to optimize their prices can be very slow and may not serve companies’ best interests profit-wise in the long term.

Mass production has an issue with a poor quality because of the human-driven quality control processes. In addition, there is a need to control the quality of products without interrupting (or disrupting) the production process.

The predictive maintenance is related to the equipment’s condition rather than expected life statistics. For this prediction, constant monitoring of the equipment, tracking of anomalies and other equipment operation-related data collection should be carried out to predict the maintenance periods to avoid equipment failure.