Rubikon is your one stop solution for Fraud Detection through Data Analysis
Fraud has become a trillion dollar business today. 500,000 identity thefts and around $1.5 billion lost due to Cyber Fraud and Money laundering in the past year. As the world moves towards complete digitization, the number of hackers and online thieves go up. Nothing you post on the internet is completely secure; no amounts of passwords really secure your private texts or email. Somewhere, in the dark web, there is a hacker who can easily access your data.
Organizations spend millions on cyber security to keep their data safe, yet fall prey to these thieves. Today, fraud is prevalent across almost all industries and has become more rampant and complicated. The quick detection of fraudulent behavior to avoid such losses has become critical in a variety of industries. In particular, many service industries are using versatile approaches to detect fraud in various kinds of customer activities.
What might be perceived as potential dishonorable behavior in a small online store might be business as is common in an exceedingly massive transnational organization. Therefore, it is important to allow businesses to define what they perceive as fraud and hence enable them to convert expert knowledge in their domain into a set of fraud rules. All transactions, one by one and jointly, can then be compared against these fraud rules in real time and get flagged as fraud once a rule is desecrated.
Take for example the fraud in the Telecom Industry. It is estimated that more than 200 variants of telecom frauds exist and this number is growing with the development of new technologies and services. Detection of such frauds can be challenging because of many reasons. One is the existence of a humongous amount of complex patterns in the customer records. Telecom customer records usually contain multivariate attributes like customer’s usage, originating and destination phone numbers, block statuses, networks etc. These records are often affected by different seasons and holiday effects and the outcome of marketing campaigns, which increase the difficulty of analysis and detection. We need to consider many parameters to detect an anomaly and fraud in real time.
With an increase in transactional channels (online, mobile, etc.) and the shift towards real-time decision making, there is a pressing need for real-time fraud detection solutions that are able to detect patterns over multiple channels and are able to self-learn and update itself.
Traditional statistical models alone are no longer sufficient to detect fraud in real time within this complex landscape. Combining batch, streaming, and predictive analytics is key for real-time fraud detection. These three predictive analytical models can help you pinpoint fraud risk areas so you can make wise detection and deterrence decisions with available resources.
So how can Rubikon help your business fight against fraudsters? Rubikon Labs houses a team of creative Data Scientists who more often than not have the solution to almost everything data related. We believe your data is the key to unlock all the potential that your business has which you don’t know, while keeping it secure from thieves. How do we do it? We build a model for you that churns your data at infinite levels and spits out the tiniest of shady information that persists. All this is done with our state of the art Machine Learning Algorithms which act smarter than your smartphone.
Starting with, one of the basic checks that we perform is against abnormal transaction quantities. The threshold does not have to be defined by you, our system takes care of it.
To illustrate, let’s say there’s a couple shopping at the FirstCry store in your neighborhood. They’re expecting their first offspring and so the enthusiasm of buying everything baby related is huge. By the time they reach the checkout lane, their cart is overflowing. The husband hands over his credit card to pay and what happens? The cashier tells them your card declined the transaction. Why? Because our software that the Credit Card company had in place, identified that this credit card was never used before for baby products and that too in a large amount. While the newly expecting parents may not really be fraudsters here, this example shows that in a different situation with different parameters, fraud of such nature can be prevented.
The following query checks incoming transaction quantities against a dynamic threshold (99th percentile –> average + 3 standard deviations) that reflects current trends, seasonal changes, etc., since the threshold itself gets updated for every incoming transaction.
from TxnStream[itemNo==a.itemNo and qty > (a.avg + 3*a.stdev) in AvgTbl as a]
select *
insert into FraudStream;
With Rubikon Labs Solution in place, businesses can thus evaluate how frequently similar transactions are done in an item and set a function to filter out large deviations. The Rubikon Labs Analytics platform can be used to create an efficient and effective fraud detection solution that encompasses real-time batch, stream and predictive analytics via ML to support the multiple demands of IoT Solutions as well as mobile and web apps.