- Data Categories
Agriculture Data 
B2B Data 
Biometric Data 
Company Data 
Conservation Data 
Demographic Data 
Economic Data 
Environmental Data 
Geospatial Data 
Healthcare Data 
Individual Data 
Industry-Specific Data 
Internet of Things (IoT) Data 
Legal and IP Data 
People and Employment Data 
Political Data 
Psychology Data 
Real Estate Data 
Retail & Commerce Data 
Social Data 
Space Exploration Data 
Sports & Entertainment Data 
Web Data
- Data Categories
- Agriculture Data
- B2B Data
- Biometric Data
- Company Data
- Conservation Data
- Demographic Data
- Economic Data
- Environmental Data
- Geospatial Data
- Healthcare Data
- Individual Data
- Industry-Specific Data
- Internet of Things (IoT) Data
- Legal and IP Data
- People and Employment Data
- Political Data
- Psychology Data
- Real Estate Data
- Retail & Commerce Data
- Social Data
- Space Exploration Data
- Sports & Entertainment Data
- Web Data
Risk and Fraud
How Is External Data Used in Risk and Fraud Use?
Artificial intelligence technologies deliver tangible results in complex risk situations when the data does not fit clearly into structured rows and columns.
In the past, risk managers identified fraud by analyzing large amounts of structured data against set rules but this led to too many false positives that took hours to sort out. Now, AI models analyze large amounts of both structured and unstructured data for better, faster results.
How are Machine Learning Models Used in Risk and Fraud?
AI algorithms identify unusual activity hidden in complex patterns without generating many false positives. Machine learning, a subset of AI, learns how to perform these tasks better over time.
At the start, analysts teach the models to improve over time with preparation sets. However, the programs take in enough data to improve on their own over time. Similar to the human thought process, machine learning algorithms take in data, process them, and compare the results. Unlike a human, however, ML algorithms do all this in a heartbeat.
Once the model has amassed enough data and experience to properly separate and extract anomalies from its data on customer behavior, it can start to make real-time decisions, like blocking or flagging transactions for further review.
Which Companies Lead the Way in Building Advanced Analytics and Machine Learning Products in this Field?
NuDetect is an award-winning company. Their AI model combines the power of four integrated layers of security to verify users based on their behavior. NuDetect prevents fraud and protects online environments in real time.
DataVisor is another award-winning fraud detection company using AI. DataVisor merges progressive AI with ML technologies to highlight suspicious patterns.
Buguroo works on behavioral biometrics, malware detection, and device assessment to detect online fraud. With its unique approach, Buguroo has made its name as a leader in the fields of online fraud detection and cybersecurity.
Connected Datasets
PipeCandy eCommerce Leads & Insights for Fulfillment
PipeCandy eCommerce Leads & Insights for Fulfillment tracks company data and company shipping details: shipping volume, which companies they use to ship products, whether they ship internationally or not, and so on. You can find company leads easily with this dataset with filter capabilities.
Wikiroutes Transit Data
Wikiroutes Transit Data provides public transport information—routes, stop points, and more—via crowd-sourcing. The data is constantly updated and can be easily converted and integrated into your own software system.
Wikiroute’s Transit Data is used by individuals, private companies, and government agencies of all types and sizes.
TrackStar Predictive Credit Technology
TrackStar’s Predictive Credit Technology uses fifteen years of financial dispute data to create predictive models of future borrowing potential. With this data and AI technology, your bank or other lending company can mitigate the risk of fraud, improve existing customer relations, and reduce your operating costs.