Fraud Detection Using Neural Networks and Sentinel Solutions (Smartsoft)
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Fraud detection is a continuously evolving discipline and requires a tool that is intelligent enough to adapt to criminals strategies and ever changing tactics to commit fraud. Despite the best efforts of the FBI and other law enforcement organizations, fraud still costs American companies an overwhelming $400 billion (reference) each year. With the relatively recent growth of the Internet into a global economic force, credit card fraud has become more prevalent.
It is in a company and card issuer’s interest to prevent fraud or, failing this, to detect fraud as soon as possible. Otherwise consumer trust in both the card and the company decreases and revenue is lost, in addition to the direct losses made through fraudulent sales.
How do Neural Networks Help with Fraud Detection?
The inherit nature of neural networks is the ability to learn is being able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. Neural networks resemble the human brain in the following two ways:
- A neural network acquires knowledge through learning.
- A neural network's knowledge is stored within inter-neuron connection strengths known as synaptic weights.
The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. Traditional linear models are simply inadequate when it comes to modeling data that contains non-linear characteristics.
What is Sentinel?
Sentinel is a complete solution designed to prevent, detect, analyze and follow up banking fraud in any entity or corporation in the financial business. Specific fraud detection solutions may include:
With Sentinel your company can monitor the activities of accounts, cardholders and merchants by using a robust and powerful technology based on rules, parameters and indicators. In other words, you can obtain immediate results from the moment you install the software.
Sentinel allows you to:
- Process data from any origin, whether it comes from transactions, merchants or cardholders.
- Monitor issuer, acquirer or banking activities.
- Examine information by strategic business units such as countries, regions, banks, etc.
- Analyze data from a managerial perspective, through a technology known as “Business Intelligence.”
- Evaluate the performance of the rules created in the system and the profit generated by them.
- Minimize risk and loss due to banking fraud.
What is Neural Fraud Management Systems (NFMS)?
The Neural Fraud Management System is a completely automated and state-of-the-art integrated system of neural networks, Fraud Detection Engine, Automatic Modeling System (AMS), supervised clustering, and system retune.
Combined with Sentinel the Neural Fraud Management System (NFMS) can automatically scale the relative importance of fraud to non-fraud, group symbols to reduce dimensionality, and evolve over time to detect new patterns and trend types in frauds.
By adding the intelligence of neural network technology to an already successful rule-based system, you can increase the detection of legitimate fraud transactions up to 80% with as low as 1% false detections or less!
How does NFMS work?
- The Neural Networks are completely adaptive able to learn from patterns of legitimate behavior and adapting to the evolving of behavior of normal transactions and patterns of fraud transactions and adapting to the evolving of the behavior of fraud transactions. The recall process of the Neural Networks is extremely fast and can make decisions in real time.
- Supervised Clustering uses a mix of traditional clustering and multi-dimensional histogram analysis with a discrete metric. The process is very fast and can make decisions in real time.
- Statistical Analysis ranks the most important features based on the joint distribution per transaction patterns. In addition, it finds the optimal subset of features and symbols with maximum information and minimum redundancy.
- The Fraud Detection Engine can apply the generated model by AMS on input data stream and output the detection results by specified model: Neural Networks, Clustering, and Combined. The Fraud Detection Engine supports both Windows and UNIX platforms.
- Retuning the basic model created by AMS to adapt to the recent trend of both the legitimate behavior and fraud behavior and update the model for Fraud Detection Engine.
- The Automatic Modeling System (AMS) chooses the important inputs and symbols, train and create clustering and neural network models.
- Significantly reduces losses due to fraud.
- Identify new fraud methods to reduce fraud losses and minimize false positives.
- It can work in real time, online or batch modes.
- Reinforce customer trust.
- Improve operational efficiencies.
- The system could develop better models by customizing the model to the Banks unique environment.
- Build and update models as the new business requirements or changes in the environment.
- The system gives you the flexibility to easily incorporate data from many sources to the neural models.
- You have the ability to build your own custom model, in house, without being an expert in AI programming. The final user could use the wizard-based interface to create new models or change the existing ones.
- Combine multiple Artificial Intelligence technologies to identify suspicious activity (clustering, neural networks, rules, profiles).
- It provides all life cycle to avoid fraud, including the stages: monitoring, preventing, detecting, registering, learning, self building.
- Boosts analyst productivity and improves effectiveness of fraud operations.
- Non intrusive implementation and easy to integrate with standard protocols: XML, SOAP / Web Services. Additionally NFMS provides API to enable an easy integration in the Bank environment if necessary.
- León Bank in Dominican Republic had reduced fraud by 60% in the first 3-months of the utilization of the system.
- Guayaqyuil Bank in Ecuador had 100% detection of fraud cases in the first month.
- Credit Card Issuer Bank* saved over $3,000,000 (US Dollars) in the first 10-months of 2003 with a fraud reduction of 30% from the prior year. (Full Text PDF)
* This is the success story of one of SmartSoft’s clients, whose name they requested not to be revealed.
NeuroDimension has completed our work with SmartSoft. If you would like to use neural networks in your fraud detection application please download a free evaluation copy of NeuroSolutions. Please contact us with any questions or comments.
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