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How Data Analytics Protects Firms by Preventing Fraud
Businesses used to think that running nightly batch fraud detection processes with some advanced statistical models maintained by a few data scientists was sufficient.
FREMONT, CA: Fraud is now common in practically every industry, and it has become more widespread and intricate. The implementation of effective and efficient fraud detection systems is an ongoing struggle for businesses. On the other hand, traditional fraud control solutions have proven ineffectual in this regard, as fraudulent behavior is increasingly becoming a cross-channel problem.
Businesses used to think that running nightly batch fraud detection processes with some advanced statistical models maintained by a few data scientists was sufficient. With the rise of transactional channels (online, mobile, etc.) and a shift toward real-time decision making, real-time fraud detection solutions that can detect patterns across numerous channels and self-learn and update themselves are in high demand. It will eliminate the need for most firms to keep groups of highly experienced but expensive data scientists to protect themselves from fraud.
A New Opportunity for Fraud Detection
In this complicated environment, fraud laws and statistical models are no longer sufficient to detect fraud in real-time. However, to put up an effective fraud detection system, companies need to mix batch analytics, streaming analytics, and predictive analytics with domain expertise. These analytics should also be able to simulate both known and unknown types of fraudulent or strange behavior.
Creating Fraud Rules from Domain Expertise
Because fraud affects organizations of many shapes, sizes, and types, the concept of fraud is open to interpretation. For example, what may appear to be potentially fraudulent behavior in a tiny online retailer could be routine in a major global corporation. As a result, organizations must define what they consider fraud and turn specialist knowledge in their domain into a set of fraud standards. Then, once a situation occurs, all transactions, individually and collectively, will be reviewed in real-time against these fraud criteria and marked as fraud.
Addressing the ‘False Positive Trap’ with Scoring
With the capacity to construct rules that capture all domain-specific fraud reasoning, there's also the risk of overprotective fraud rules losing clients.
Scoring is a straightforward method for addressing these issues. It allows businesses to employ a set of rules, each with its weight, to provide a single number that shows how well a transaction performed against various fraud indications.
Building a Powerful and Comprehensive Fraud Detection System
A robust fraud detection system can be built using a mix of the strategies above. However, if a possible fraudulent occurrence gets identified, an organization must investigate further to determine whether this event gets linked to any other events or relationships. As a result, it's worth looking at other events that have something in common with those that get detected as potentially fraudulent.
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