- B
- Blockchain
- C
- Customer Churn
- Counterterrorism
- Cybersecurity in the Public Sector
- Credit Card Fraudulent Transactions
- Credit Default Rates
- Conversion Modeling
- Claim Payment Automation Modeling
- Claim Development Modeling
- D
- Drug Delivery Optimization
- Disease Propensity
- Digital Wealth Management
- Direct Marketing
- E
- Estimating Sepsis Risk
- F
- Finding Duplicate Customer Records in Your Database
- Fraud detection
- Finding New Oil and Gas Sources
- Fraudulent Claim Modeling
- G
- Google AdWords Bidding
- H
- Hospital Readmission Risk
- I
- Inventory Forecasting
- Insider Threat in Public Sector
- Insurance Pricing
- L
- Loyalty Program Usage
- Life Insurance Underwriting
- M
- Multichannel Marketing Attribution
- Modeling ICU Occupancy
- N
- Next Best Offer
- Next Best Action
- P
- Product Personalization
- Q
- Quality Assurance
- S
- Supply Chain Management
- View global site search results

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.
Problem/Pain
Fraudulent transactions are costly, but it is too expensive and inefficient to investigate every transaction for fraud. Even if possible, investigating innocent customers could prove to be a very poor customer experience, leading some clients to leave the business.
Solution
Using DataRobot, you can automatically build extremely accurate predictive models to identify and prioritize likely fraudulent activity. Fraud units can then create a data-based queue, investigating only those incidents likely to require it. The resulting benefits are two-fold. First, your resources are deployed where you will see the greatest return on your investigative investment. Additionally, you optimize customer satisfaction by protecting their accounts and not challenging innocent transactions.
Why DataRobot
Automate building an accurate model to predict the likelihood that a financial transaction is fraudulent. With the results you can create a rank-ordered queue of transactions for fraud units to investigate.