Use Cases

Case Studies

Get inspired by these AI case studies and turn your business problem into a business opportunity with LogicPlum.

Use Cases

All Industries

AI has many potential uses, including external (client-facing) applications like customer service, product recommendation, and pricing forecasts, but it is also being used internally to help speed up processes or improve products that were previously manual and time-consuming.

Next Best Action

Modeling your customers’ characteristics and needs can be challenging and time-consuming.

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Blockchain

LogicPlum’s platform provides the right tool to quickly build algorithms that can detect vulnerable spots by using multiple sources of data and a variety of algorithms.

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Digital Wealth Management

LogicPlum can help financial services institutions to quickly develop and test algorithms...

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Counterterrorism

LogicPlum consolidates data that is then used by its automated machine learning...

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Fraud Detection

Because it is open-source, the LogicPlum platform can be easily integrated with existing systems...

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Insider Threat

LogicPlum allows organizations to optimize the use of internal policies and employee data in the development...

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Cybersecurity

LogicPlum can run multiple algorithms that, combined with numerous data sources...

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Drug Delivery Optimization

LogicPlum’s platform allows for the use and consolidation of different complex data sources, effectively creating algorithms...

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Life Insurance Underwriting for Impaired Life Customers

LogicPlum can classify customers according to risk levels by automatically analyzing significant amounts of data...

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Disease Propensity

By using several data sources, such as demographic and health-related information...

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Modeling ICU Occupancy

With LogicPlum, organizations can be modeled in their entirety, patients’ movements estimated, and resources’ usage optimized.

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Estimating Sepsis Risk

Predictive models benefit from using large amounts of data.

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Hospital Readmission Risk

LogicPlum provides a platform that helps to automatically create process models...

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Finding New Oil and Gas Sources

With LogicPlum, organizations can build effective prediction models that can help them to know where and how to extract oil and gas...

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Insurance Pricing

By using LogicPlum’s platform, insurance companies can increase effectiveness and reduce unnecessary costs...

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Credit Card Fraudulent Transactions

LogicPlum helps organizations to automatically create models that can detect potentially fraudulent transactions.

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Fraudulent Claim Modeling

LogicPlum helps organizations to automatically create efficient models, which then can be used to optimally select...

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Direct Marketing

LogicPlum’s automated machine learning model generation means that organizations can build effective models...

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Credit Default Rates

LogicPlum provides the right environment to quickly and easily create and deploy default-risk predictive...

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Conversion Modeling

LogicPlum provides a platform whereby to build a conversion rate prediction model is very easy and fast.

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Claim Payment Automation Modeling

With LogicPlum, it is effortless and quick to develop an accurate model that can differentiate between those claims...

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Claim Development Modeling

LogicPlum platform allows organizations to develop models that can predict the final cost of a claim at the time of filling it in.

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Machine Learning in Banking

Improving efficiencies, reducing expenses, and gaining a competitive advantage is why banks are using machine learning. From risk analysis and fraud detection to optimizing all areas of marketing, banks are making the data-driven decision that increases profitability.

Blockchain

LogicPlum’s platform provides the right tool to quickly build algorithms that can detect vulnerable spots by using multiple sources of data and a variety of algorithms.

See the usecase →

Digital Wealth Management

LogicPlum can help financial services institutions to quickly develop and test algorithms...

See the usecase →

Credit Card Fraudulent Transactions

LogicPlum helps organizations to automatically create models that can detect potentially fraudulent transactions.

See the usecase →

Credit Default Rates

LogicPlum provides the right environment to quickly and easily create and deploy default-risk predictive...

See the usecase →

Machine Learning in Fintech

Improving efficiencies, reducing expenses, and gaining a competitive advantage is why fintech is using machine learning. From risk analysis and fraud detection to optimizing all areas of marketing, fintech is making the data-driven decision that increases profitability.

Digital Wealth Management

LogicPlum can help financial services institutions to quickly develop and test algorithms...

See the usecase →

Credit Card Fraudulent Transactions

LogicPlum helps organizations to automatically create models that can detect potentially fraudulent transactions.

See the usecase →

Credit Default Rates

LogicPlum provides the right environment to quickly and easily create and deploy default-risk predictive...

See the usecase →

Machine Learning in Healthcare

Improving efficiencies, reducing expenses, and gaining a competitive advantage is why healthcare is using machine learning. From readmission risk and early detection of disease to marketing, healthcare is making the data-driven decision that increases patient care and profitability.

Drug Delivery Optimization

LogicPlum’s platform allows for the use and consolidation of different complex data sources, effectively creating algorithms...

See the usecase →

Disease Propensity

By using several data sources, such as demographic and health-related information...

See the usecase →

Estimating Sepsis Risk

Predictive models benefit from using large amounts of data.

See the usecase →

Hospital Readmission Risk

LogicPlum provides a platform that helps to automatically create process models...

See the usecase →

Machine Learning in Insurance

Improving efficiencies, reducing expenses, and gaining a competitive advantage is why insurance companies are using machine learning. From underwriting to marketing, they are optimizing all areas of their business to make data-driven decisions that increase profitability.

Life Insurance Underwriting for Impaired Life Customers

LogicPlum can classify customers according to risk levels by automatically analyzing significant amounts of data...

See the usecase →

Insurance Pricing

By using LogicPlum’s platform, insurance companies can increase effectiveness and reduce unnecessary costs...

See the usecase →

Fraudulent Claim Modeling

LogicPlum helps organizations to automatically create efficient models, which then can be used to optimally select...

See the usecase →

Conversion Modeling

LogicPlum provides a platform whereby to build a conversion rate prediction model is very easy and fast.

See the usecase →

Claim Payment Automation Modeling

With LogicPlum, it is effortless and quick to develop an accurate model that can differentiate between those claims...

See the usecase →

Claim Development Modeling

LogicPlum platform allows organizations to develop models that can predict the final cost of a claim at the time of filling it in.

See the usecase →

Machine Learning in Marketing

Effectiveness of marketing activities and operations, correctly targeting customers, moving customers through the funnel toward purchasing, and improving customer relationships is why marketing departments are using machine learning. From improving ROI to emotionally intelligent communication with customers, marketers are making data-driven decisions that increase profitability.

Next Best Action

Modeling your customers’ characteristics and needs can be challenging and time-consuming.

See the usecase →

Direct Marketing

LogicPlum’s automated machine learning model generation means that organizations can build effective models...

See the usecase →

Machine Learning in Oil and Gas

Oil and gas companies are using machine learning to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. From exploration and production to seismic interpretation, oil and gas companies are upending traditional methods with machine learning. They are optimizing to make data-driven decisions that lead to increased profitability.

Finding New Oil and Gas Sources

With LogicPlum, organizations can build effective prediction models that can help them to know where and how to extract oil and gas...

See the usecase →

Machine Learning in the Public Sector

Safety, health, fraud, defense, justice, and public services are all changing due to the power of machine learning. Automation that focuses on these areas allows for a more effective way to serve communities and doesn’t put reliance on domain experts with programming to drive results.

Counterterrorism

LogicPlum consolidates data that is then used by its automated machine learning...

See the usecase →

Fraud Detection

Because it is open-source, the LogicPlum platform can be easily integrated with existing systems...

See the usecase →

Insider Threat

LogicPlum allows organizations to optimize the use of internal policies and employee data in the development...

See the usecase →

Cybersecurity

LogicPlum can run multiple algorithms that, combined with numerous data sources...

See the usecase →