May 30, 2024

How Technology is Combating Workers’ Compensation Fraud

0
Spread the love

Workers’ compensation fraud costs businesses billions each year. Some of the most common types of fraud in workers ‘ comp are employees who fabricate workplace accidents, care providers who overcode or bill for procedures not performed, and employers who misclassify employees to lower premiums.

Medical provider fraud compromises the integrity of the workers’ comp system and damages trust. Fortunately, insurers and regulatory authorities are working to identify fraudulent practices and shut down fraud rings.

Surveillance

Workers’ compensation fraud imposes significant financial burdens on insurance providers and employers and distorts the availability of legitimate benefits for injured workers. In addition, those caught engaging in fraudulent activities can face criminal charges, restitution orders, and severe penalties.

The actual workers’ comp fraud rate is difficult to determine because most claims are legitimate. However, some claimants commit fraud by fabricating or exaggerating injuries or symptoms to receive increased benefits or extended time off from work.

Surveillance technology can help detect workers’ compensation fraud by capturing video of a worker engaging in suspicious activities. Insurers can then use this evidence to deny a workers’ compensation claim or recoup payment on a valid one.

However, this type of surveillance is costly and can have inequitable consequences for low-wage workers who may be targeted by insurers looking to uncover fraud. Further, surveillance can also create a sense of paranoia and suspicion among injured workers. This can exacerbate the pain, humiliation, and anxiety associated with a genuine workplace injury.

Social Media

Insurance companies investigate whether the claim is legitimate when people file workers’ compensation claims. They hire investigators to do surveillance and search public records for clues of fraud. The emergence of social media has altered the methods used in investigating claims, which has led to debates about appropriate investigations and privacy rights.

Using the latest technology to combat insurance fraud, many insurers have expanded their special investigation units (SIU) to include social media analysts. These professionals can search public records, websites, and social media platforms to uncover information contradicting a worker’s claim of a work-related injury.

For example, if someone claims an injury that prevents them from participating in physical activities, but their social media accounts reveal them playing contact sports on a semi-professional team, this could be grounds for denying the claim. In the past, investigators might have had to park outside the injured person’s home and take photos, but now they can log onto their Facebook page or Twitter account. Depending on the user’s privacy settings, some insurers can access private information even if it is encrypted.

Predictive Analytics

Predictive analytics models use data analysis to spot patterns that indicate future trends. They are used to streamline operations, boost revenue, and mitigate risk for businesses in virtually every industry. The key to using predictive analytics is to have a large enough pool of reliable, clean data.

The broader use of big data systems and advanced machine learning algorithms has made it possible to gather predictive data more easily. However, developing functional models to support business decisions requires machine learning and predictive analytics expertise. This type of talent is in short supply.

Insurance companies and other businesses have adopted predictive analytics to create a strong deterrent against workers’ compensation fraud, protect the system’s integrity, and ensure benefits reach those who need them. In addition, they have established policies and practices to encourage employee whistleblowing, provide channels for reporting suspected fraud, and implement preventive measures.

Wearable Devices

Wearables are digital devices that track people’s movements and communicate the data to third parties, such as insurance companies. The technology is used for various purposes, from monitoring heart rate and blood pressure to helping people with chronic diseases like diabetes or heart disease.

Some examples of wearables include smart watches, virtual reality headsets, and even fitness trackers like Fitbit. These devices can record movement to the last step and are useful for investigating fraud. For example, a claimant may report walking only 3 minutes straight before needing to stop, but the wearable shows 30-minute straight movement.

Some wearables are implanted under the skin, such as the pacemaker and insulin pump, that help patients manage their conditions. They also can track a patient’s mental health by tracking their emotions and stress levels or detecting if they’re experiencing a panic attack. The technology can alert their care team and family members or notify the local police. This is a powerful tool in the fight against workers’ compensation fraud.

Electronic Records

Most individuals within the workers’ compensation system act in good faith and are genuinely injured. However, fraudulent activities remain a significant issue that drains insurance companies’ resources and penalizes society. Employers should proactively implement preventive measures to protect the system’s integrity and ensure benefits reach those who require them.

Developing strong partnerships with insurance carriers and authorities, creating clear channels for reporting, and providing whistleblower protection will help deter fraudulent activity. In addition, conducting background checks and criminal records searches before hiring employees will help identify those with a history of fraud or dishonesty.

Employers should also consider implementing surveillance technologies and predictive analytics to catch fraudulent claims as early as possible. This will save money, reduce costs, and expedite the investigation process. For example, predictive modeling can identify claimants with multiple Social Security numbers, multiple home addresses, or multiple dates of birth — all red flags for suspicious behavior. These models can then automatically refer the case to a claims examiner for further review. Additionally, deploying predictive analytics technology will allow investigators to examine the claimant’s medical information and detect abnormalities quickly.

Leave a Reply

Your email address will not be published. Required fields are marked *