Fraud is any situation that exposes consumer and enterprise related data to unauthorized personnel. This data includes information, assets, accounts and transactions and takes away money and property of users. Every year fraud causes billions of dollars in loss and leads to compromised services. Â
Telecom and the finance sector have especially become vulnerable to fraud given the adoption of services by users. Examples of fraud include: phishing, identity theft, money laundering, and terrorist financing.
Fraud detection is a mechanism that uses advanced data analytics technology to uncover fraud attempts. It catches illegal transactions in real-time, near real-time or in-advance. Telecom organizations and financial institutions have started to adopt fraud detection mechanisms. It successfully combats public fraud and gains protection against fraud risk.
In this battle of uncovering fraud, law enforcement agencies also employ fraud detection technology that takes support from the telecom and finance sector. They obtain consumer data where telecom and financial services might be used for fraudulent activities.
Fraud detection technology uses the consumer data and performs statistical analysis over it to extract key information in an efficient manner. Most common fraud detection systems include geo fencing, signaling and voice intercept, and CDR analysis systems.
As the adoption of data analytics technology has advanced further, more complex forms of analytics such as predictive analytics have been utilized to proactively combat fraud. These systems collect telecom and finance data of users in advance and apply big data technology to forecast criminal risk score.
Characterizing fraud, example from Telecom sector
Ever received an SMS that congratulates you on receiving a prize money?
That’s an example of telecom fraud.
It’s a situation when someone tries to scam citizens with fake information and manipulates them to gain financial benefits.
When telecom companies were newly launched, there was no way a fraud user could be caught. But today, this has become possible using data analytics.
Data analytics is the study of data that uses statistics and AI to characterize the data– such as whether the data represents a fraud case or not. At the back, it has a wealth of example data, on both fraud and non-fraud cases that is used for matching.
Now how to characterize a fraud in SMS or call data?
A special property of fraud is that it always counts as an outlier case in a normal dataset.Â
This dataset might include all data related to customer activity; for example, account balance, number of SMS sent in a given time, number of persons contacted in a given time, and average price of package in past 3 months.
The data that falls in the outlier cluster represents suspicious users. Business activities of these suspects are then closely examined by authorities to find and confirm fraud users.
Crime investigation organizations can also obtain user telecom data called the call detail record CDR to analyze the data in a similar way and identify fraud behavior.
To wrap up, fraud detection technology in the telecom and finance sector is the much needed application of data analytics. It gracefully secures consumers from fraud groups and holds their trust in organizations of both sectors.
