RapidMiner Fraud Detection - Locus IT Services

RapidMiner Fraud Detection

Locus IT ServicesFraud AnalyticsRapidMiner Fraud Detection

RapidMiner Fraud Detection

RapidMiner Fraud Detection helps you identify fraudulent activity quickly and end it. Fraud eats at many organizations like financial services companies, healthcare providers, government agencies, etc. It not only negatively impacts the profitability and other business results, but also the ability to serve the customers and achieve mission and purpose.

The faster and more accurately you can detect fraud, the more likely you are to put a stop to it in time. Data science can revolutionize the fraud detection process. Use all the available data to identify non-obvious fraud patterns, and monitor the operations to spot fraudsters when they’d otherwise remain hidden.

Capabilities of RapidMiner Fraud Detection

  • Identify patterns of fraud

Move beyond simple fraud identification methods by identifying the complex patterns to watch for. Update models frequently as the perpetrators shift behavior to avoid detection.

  • Monitor activity for signs of fraud

Apply patterns and the models to large volumes of streaming data, constantly watching for signs of suspicious behavior. Leave no place for perpetrators to hide.

  • Stop fraud quickly

Detect the fraud quickly and early enough to take action before it has a widespread damaging impact.

  • Reduce costs, improve service

Preventing or minimizing the fraud reduces losses and associated costs and protects margin and profit. It also frees organizations to focus on legitimate customers and provide better services.

Using Machine Learning To Detect Fraud

Machine learning in RapidMiner Fraud Detection helps to detect and prevent fraud and make fraud fighters more efficient and effective. The challenge of fighting fraud is that the fraudsters often are intelligent, learn from mistakes, and continuously create new types of fraud.

  • The first step in RapidMiner Fraud Detection is to combine the domain expertise and the known fraud patterns into entity features and fraud risk scores that can be automatically computed and used to systematically and automatically rank suspects.
  • In a second and more sophisticated step, the supervised machine learning, e.g. classification and association learners, can be used to learn detection models for the known types of fraud.

These models can then be deployed to automatically identify the new instances or cases of known fraud patterns/types in the future. In order to also detect the potential new fraud patterns and types, we leverage unsupervised machine learning, e.g. anomaly detection and outlier identification techniques.

In order to not only identify fraud after it has already happened, but to prevent it, we also use machine learning and predictive analytics, e.g. for predicting the expected treatments, medications, quantities, volumes, and costs and comparing them with the actual transactions and requested payments, so that the health insurance can decide to postpone or deny payments, if the transactions or combinations of transactions seem highly suspicious, and perform investigations before processing payments.

Overall, data mining and machine learning can help the auditors and fraud busters to target on the high risk cases instead of wasting time with random checks, allow to combine and consider various data sources, create meaningful features and scores, provide context and the explanations, detect networks of fraudsters and assist the auditors and fraud busters and make them more effective and efficient.

Areas and Types of Fraud

  • Credit Card Fraud
  • Tax Fraud
  • Fraud in Supply Chains, Retail Networks, Purchase Departments, Procurement
  • Insurance Fraud

Challenges

  • Large Number of Potential Types and Areas of Fraud
  • Intelligent and Constantly Improving Adversaries
  • Changing Fraud Patterns and Types
  • Large Amounts of Potentially Relevant Data
  • Large Variety of Potentially Relevant Data Sources & Types
  • Limited Resources for Fraud Detection & Prevention

By indexing relevant machine data and correlating and searching on it to identify the patterns of fraud, an organization can detect and alert on fraud in real time and prevent it before the bottom lines are impacted. Locus IT provides such RapidMiner Fraud Analytics services like Fraud Analytics training, Fraud Analytics support and Fraud Analytics implementation. For more information please contact us.

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