Solutions / Use cases

Use cases in all industries

Solutions /  Use cases / Banking
Machine Learning in Banking
Banks are using machine learning to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from risk analysis and fraud detection to marketing, in order to make data-driven decisions that lead to increased profitability.

Use cases in Banking

Solutions /  Use cases / Fintech
Machine Learning in Fintech
Fintech is using machine learning to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from risk analysis and portfolio optimization to marketing, in order to make data-driven decisions that lead to increased profitability.

Use cases in Fintech

Solutions /  Use cases / Healthcare
Machine Learning in Healthcare
Healthcare companies are using machine learning to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from readmission risk and occupancy rates to marketing, in order to make data-driven decisions that lead to increased profitability.

Use cases in Healthcare

Solutions /  Use cases / Insurance
Machine learning in insurance
Insurance companies are using machine learning to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from underwriting to marketing in order to make data-driven decisions to lead to increased profitability.

Use cases in Insurance

Solutions /  Use cases / Marketing
Machine Learning in Marketing
Marketing departments and organizations are using machine learning to determine the effectiveness of their marketing activities and operations, accurately target customers, move them further down the funnel toward purchase, and improve customer relationships. Machine learning allows marketers in every industry to accurately determine and improve ROI, resulting in tangible bottom-line value.

Use cases in Marketing

Solutions /  Use cases / Oil and Gas
Machine Learning in Oil and Gas
Banks are using machine learning to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from risk analysis and fraud detection to marketing, in order to make data-driven decisions that lead to increased profitability.

Use cases in Oil and Gas

Solutions /  Use cases / Public Sector
Machine Learning in the Public Sector
The public sector is using automated machine learning to reduce the time and level of expertise needed for agencies to move into artificial intelligence. Automation delivers the power of machine learning to the domain experts who need it without needing to learn a programming language or hire cost-prohibitive data scientists. By using automation machine learning, agencies can more effectively serve their citizens in important areas such as safety, health, fraud, defense, justice, and public services. Our many uses case examples in these areas demonstrate the power of DataRobot.

Use cases in Public Sector

Google AdWords Bidding

Determine the optimal price to bid on each Google AdWord to achieve your target ROI.

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Product Personalization

Target and personalize content and product recommendations, resulting in increased customer engagement, brand value, and sales.

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Finding Duplicate Customer Records in Your Database

Make sure your database adheres to best practices.

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Loyalty Program Usage

Personalize redemption recommendations in loyalty schemes, resulting in increased consumer usage and engagement.

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Next Best Offer

Recommend the right product to the right person at the right time.

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Multichannel Marketing Attribution

Accurately determine which of your marketing activities are having the biggest effect on sales.

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Customer Churn

Understand the factors that lead to customer churn and predict which customers are likely to defect so you can take preventative action.

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Next Best Action

Understand which marketing activities are most likely to move each individual customer closer to purchase.

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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.

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Digital Wealth Management

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Modeling ICU Occupancy

Forecasting ICU occupancy means being prepared for incoming patients and not staffing empty beds.

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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.

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Hospital Readmission Risk

Proactively identifying hospital readmittance means increasing quality of care, decreasing costs, and improving the lives of patients.

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Finding New Oil and Gas Sources

In the Oil and 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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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Claim Development Modeling

Out with the old, in with the new....newer machine learning algorithms are allowing insurance companies to build more robust mechanisms for predicting, once a claim occurs, how much it will ultimately cost.

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