Machine Learning Use Case

A Machine Learning Use Case is a data-driven automation use case that includes a significant machine learning task.

• Context:
  • It can (often) be instantiated in an ML Application (a data-driven application).
• Example(s):
  • Machine Learning for Digital Content Search.
  • Machine Learning for Digital Content Recommendation.
  • Machine Learning for Consumer Digital Assistants.
  • Machine Learning for Medical Diagnosis.
  • Machine Learning for Customer Support.
  • Machine Learning for Spam Detection.
  • Machine Learning for Fraud Detection.
• See: Machine Learning System, Use Case.

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References

2017

• https://www.datarobot.com/use-cases/
  • QUOTE:
    • Google AdWords Bidding: Determine the optimal price to bid on each Google AdWord to achieve your target ROI.
    • Product Personalization: Target and personalize content and product recommendations, resulting in increased customer engagement, brand value, and sales.
    • Finding Duplicate Customer Records in Your Database: Make sure your database adheres to best practices.
    • Loyalty Program Usage: Personalize redemption recommendations in loyalty schemes, resulting in increased consumer usage and engagement.
    • Next Best Offer: Recommend the right product to the right person at the right time.
    • Multichannel Marketing Attribution: Accurately determine which of your marketing activities are having the biggest effect on sales.
    • Customer Churn: Understand the factors that lead to customer churn and predict which customers are likely to defect so you can take preventative action.
    • Next Best Action: Understand which marketing activities are most likely to move each individual customer closer to purchase.
    • Blockchain: As a relatively new financial system, blockchain is particularly vulnerable to security threats. Build and deploy machine learning algorithms that can detect anomalous behavior anywhere along the chain.
    • Digital Wealth Management: Machine learning algorithms can help digital wealth advisory companies with portfolio management services.
    • Counterterrorism: Predicting and preventing terrorist attacks is a chief concern for intelligence and agencies, and predictive modeling based on historical data may help prevent them in the future.
    • Fraud detection: Almost every government agency serving the nation's citizens suffers from fraud, costing approximately $80 billion a year. Data analysis and predictive modeling can combat this issue in minutes, not months.
    • Insider threat: Threats can come from all sides, not just externally but from inside government agencies as well. These agencies need to proactively block any potential misuse, using machine learning to identify exploitation of inside information.
    • Cybersecurity: Cybersecurity is emerging as one of the greatest threats of the future, and federal agencies are particularly vulnerable. Build, deploy and refresh models to predict incoming threats in real-time.
    • Drug Delivery Optimization: To increase product adoption, pharmaceutical firms ship millions of drug samples to doctors and hospitals. The orders can be consolidated when the same location requests two or more drug samples. DataRobot can predict which drug samples should wait for consolidation, reducing the overall cost of delivery.
    • Life Insurance Underwriting for Impaired Life Customers: Typically, unless a reinsurance company covers the risk, direct insurance companies do not underwrite life insurance for individuals who have suffered a serious disease and are in a situation of “impaired life." A reinsurance company wants to predict which customers have positive health prospects and are insurable.
    • Disease Propensity: Outreach to patients without analytics is like trying to tie your shoes in the dark. Unfortunately, waiting until they seek care results in higher costs, and potentially poorer outcomes, for everyone.
    • Modeling ICU Occupancy: Forecasting ICU occupancy means being prepared for incoming patients and not staffing empty beds.
    • Estimating Sepsis Risk: Sepsis is a serious condition that often occurs suddenly and with life-threatening impact. Identifying patients most at risk for developing sepsis may mean the difference between life and death.
    • Hospital Readmission Risk: Proactively identifying hospital readmittance means increasing quality of care, decreasing costs, and improving the lives of patients.
    • Finding New Oil and Gas Sources: In the Oil & Gas Industry, upstream companies continually search for potential new oil and gas fields, both underground and underwater. Drilling exploratory wells is a significant investment, and you must be able to predict which locations will produce the most profit at the lowest cost.
    • Insurance Pricing: To be profitable in the insurance industry, you must avoid being adversely selected against. To avoid this and maintain your underwriting margins requires highly accurate predictive models.
    • Credit Card Fraudulent Transactions: The cost of credit card fraud is billions of dollars per year. By accurately predicting which transactions are likely fraudulent, banks can significantly reduce these illegal transactions while providing card holders an excellent customer experience.
    • Fraudulent Claim Modeling: The cost of fraudulent insurance claims is in the billions. Accurately predicting claims legitimacy significantly reduces fraudulent payouts and leaves the insured with a positive customer experience.
    • Direct Marketing: To maximize ROI, it's important to boost marketing response rates and minimize misdirected communication. The most up-to-date modeling algorithms return the best results, but the data science expertise required to implement them can be daunting.
    • Credit Default Rates: Individuals or businesses often need loans. Making accurate judgments on the likelihood of default is the difference between a successful and unsuccessful loan portfolio.
    • Conversion Modeling: The ability to predict which segments are most likely to convert from a quote to a policy allows insurance companies to optimize their pricing algorithm and their marketing spending, leading to data-driven objective business decisions.
    • Claim Payment Automation Modeling: Time is money, for your business and for your customers. Use DataRobot to model when autopaying claims is the best option. Shortening the claim cycle drives costs down and customer satisfaction up.

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