Fraud detection refers to any activity used to identify (and prevent) attempts to obtain money, assets, data, or anything of value by falsifying information. Many industries such as insurance, banking, credit cards, telecommunications, and healthcare are particularly vulnerable to fraud.
Online financial systems are always susceptible to attacks, if not adequately secured. With the increasing digitization of currency, stocks, trading and financial transactions the complexities of securing fintech systems has also increased - thus demanding comprehensive and reliable solutions.
Edge computing is a distributed architecture which brings the data processing stations closer to the endpoints and devices. These end devices could include mobile phones, personal computers, security cameras and other Internet of Things (IoT) devices. Edge computing helps reduce latency because the data is faster to access through a local server than a cloud one. Network traffic is also reduced due to the distributed processing. This also reduces vulnerability as edge computing supports faster responses to behavioral anomalies.
Edge security is the practice of securing data that lives at the edge as well as data transfers between endpoints, edge data centers, and the cloud.
Fraud detection techniques
The techniques used include but are not limited to
- Cryptography and encryption involves masking data with an encryption which can be decrypted by only specific means or personnel. This way, even if the data is accessed by an outsider they cannot use it.
- Access control is an essential method which limits the number of individuals who can access certain information. This makes the system less vulnerable to attacks or misuse.
- Automated monitoring is critical to detecting fraud in real-time.
Automating fraud detection
Automated models can be deployed to enable fraud detection. There are two main branches of automated fraud detection - AI based and statistical analysis based.
This is carried out by using behavioral detection techniques. It flags any anomaly in spending behavior and frequency of transactions because such deviations might be fraudulent.
Artificial Intelligence or Machine Learning models including Neural Networks can be trained and used for this purpose as well. Historical data allows the ML models to learn the habits and patterns of an individual such as location, purpose of use and more.
When these models are deployed at the edge, the technique is known as Edge Machine Learning.
These systems are capable of performing reasoning with the help of if-then logic. They can detect anomalies using statistical and rule-based analysis. Statistical techniques are based on traditional mathematical methods, such as logistic regression and Bayesian theory.
Rule-based analysis is also performed by these systems which involve intelligence such as Neural Network models and Support Vector Machines.
Ecommerce, online trading applications, insurance, crypto currency mining and exchanges have created a fast paced information technology environment with a constant stream of ever increasing data. More ways of accessing data combined with the increased sophistication of attack vectors makes data systems vulnerable to attacks and misuse. Fraud detection is a crucial aspect of cyber security which uses AI, access control, cryptography and other monitoring tools to flag any fraudulent activity.
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