How Big Data Prevents Fraud Across Industries
No matter the industry, fraud is a constant threat. Some industries are more closely associated with fraud — like insurance — but all industries can benefit from new technology and practices that make fraud harder for bad actors and easier for businesses to catch.
Advances in computer technology make analyzing huge amounts of data easier than ever. One new technology that could is already being used to prevent fraud is big data analysis — computer programs that help data scientists analyze massive sets of data and build predictive models.
Big data can be used to create predictive models from just about any kind of large, standardized data set. That makes big data an effective tool in detecting fraud. As a result, multiple industries are using big data to tackle the problem of fraud in all its possible forms.
Big Data in the Insurance Industry
Insurance companies have traditionally relied on computer technology to process and analyze claims. But while many of these processes were digital, they often relied on legacy technology — SQL queries, non-centralized data, and siloed processes — that limited the speed at which claims could be processed.
In cases like these, big data analysis is typically easy to implement — and some insurance companies are already making the switch.
With these legacies systems, essential claim data was already being converted to a digital format and stored. While the data collected was too much for a human analyst to process without the help of a computer, it was still being stored in a way that’s easy for AI and big data techniques to analyze.
Big data analysis can be trained on the digital archive of insurance claims. to build a predictive model that knows which elements — like claim type or amount claimed — are most common in denied or fraudulent claims. This predictive model can then be used going forward, flagging claims that look like they could be fraudulent. With this model, human analysts can spend extra time reviewing the claims that are the most likely statistically to be fraudulent.
The technology could also save non-fraudulent claims from extended analysis, allowing those filers to have their claim processed sooner than it could have been before.
Big Data and Healthcare Fraud and Abuse
The National Healthcare Anti-Fraud Association has reported that fraud could cost the healthcare industry up to $80 billion every year. Healthcare professionals need a way to fight back against healthcare fraud and abuse. As a result, big data is being used in the healthcare industry to help prevent fraud and abuse of medical systems.
Common types of healthcare fraud include billing for services not provided and performing — and charging for — unnecessary procedures. At their worst, these cases can be hugely expensive — in one example, 412 individuals working in the medical sector falsely billed more than $1.3 billion by ordering unnecessary procedures and medications.
Big data analysis, when provided with the right amount and kind of data, can help uncover patterns of abuse. The technology can be used to aggregate individual claims of abuse and strengthen them by bringing together patterns of fraudulent behavior.
Big could also help detect some of the most common types of pharmaceutical fraud, like fraudulent clinic trials and average pricing information. With enough data to create a predictive model, a big data-powered algorithm could analyze clinical trials and drug pricing to detect broader patterns of fraudulent behavior from pharmaceutical companies.
Big Data and Cyber Fraud
One of the biggest challenges faced by cybersecurity experts is phishing schemes. These are emails that are designed to look like legitimate correspondence from employers, online retailers, and other trusted sources. In reality, however, they’re sent by hackers with the intention to lure individuals into downloading malicious files or divulging valuable information — like personal details, passwords, and network access.
Phishing schemes are hard to defend against because they use social engineering to attack the weakest portion of a business’s cyber defense — employees without security training. Firewalls and anti-virus software can automatically defend against malicious downloads but there’s no automated solution to security training.
In some of the biggest data breaches of the past decade, phishing schemes were how hackers gained access to secure networks and confidential customer data.
And in the wake of big breaches, hackers can use personal data to make their phishing schemes seem even more trustworthy. This makes targeting phishing schemes a top priority for cybersecurity experts.
Big data analysis can be used to create what is effectively a highly advanced spam filter. Big data can be used to build a model that detects common elements of fraudulent emails — like misleading links, spoofed email addresses and requests for personal information. The big data-powered filter can catch these emails and either flags them as potential phishing schemes or ensure they never get to the user.
If effective, these new filters could significantly reduce the avenues of attack the hackers could use when trying to gain access to confidential data and secure networks.
How Big Data Can Prevent Fraud
In any case, where there is a large amount of data that can be analyzed, big data can be used to tease out subtle patterns. This ability makes it a great fit for industries wanting to beat fraud — no matter whether the fraudsters are pharmaceutical companies, cybercriminals or individuals filing fraudulent claims.
Given the increasing number of claims and incidences of fraud that many industries are facing, it’s likely that big data will continue to become a more and more popular tool in fighting fraud.
