ACA 1095 PITFALL: BAD DATA
Bad data comes in many different varieties each with impacts ranging from moderate to severe. Now that “best efforts” is behind us there’s no time like the present to step up and get it right. Otherwise, the penalties for each infraction start at $250 and the penalties for offering coverage to less than 95% of the ACA population is $2,000 for each enrolled employee.
While some people simply choose to bury their head in the sand and ignore it others take Stephen Covey’s 7 Habits of Highly Effective People and are Proactive. Like many things in life, we have choices. The choice is to either 1) Accept bad data and pay penalties to the IRS (and probably lose your job), or 2) Be proactive, fix the data and NOT pay penalties to the IRS. In this blog we’ll review a few examples of bad data and discuss their potential impact.
Example 1 – Hours worked for part-time and variable hour employees. Hours worked data for non-fulltime employees is absolutely critical when it comes to determining eligibility throughout the Measurement Period lifecycle. Unfortunately, some employers only utilize annual hours worked data which invariably does not always include ACA eligible hours, such as FMLA, leave, vacation time, jury data and military leave.
When these hours are excluded, the employer is at risk for not offering ACA eligible employees benefits. Since the threshold is 95%, ACA eligibility accuracy is critical. In the event ACA eligible employees have not been offered coverage and the threshold is not met, the penalty is $2,000 which is applied to every enrolled employee. Hence, a plan with 1,000 employees could be penalized $2,000,000. For an employer operating on a 5% profit margin this translates to $40,000,000 in revenue equivalents – starting to get the picture!
Example 2 – Using unverified carrier-provided enrollment data – Like many things in life, carrier enrollment data is often inaccurate. If this is utilized instead of benefits data (maintained in a benefits system), this has RISK written all over it. Not only is there risk in the actual eligibility dates, but there is also data conversion risk. Since benefits and the ACA is so new to the IT community, HR and Benefits must be involved in testing and QA throughout the entire data conversion journey.
Example 3 – Incorrect social security numbers – An important part of the IRS process includes verifying employee name and SSN. Interestingly, the majority of 1094-C filings had multiple errors of this type. In the spirit of Stephen Covey, now is the time to be proactive. A best practice is to start verifying name and SSN information before forms are created. In the event an employee cannot provide its employer with their actual SSN, this can create problems in other areas which may also have financial implications. In the event your employer is resource-constrained, an outside firm such as a dependent eligibility auditing firm should be utilized to facilitate this process.
Example 4 – Missing address 2 (e.g., apartment number) – While “address 2” may not seem like a big “bad data” deal, it can be particularly if many of your employees live in urban areas. Why? Because most of the employees will be living in high rises, such as apartment buildings or condominiums. If the postal carrier doesn’t know which unit should get the form, the form will be undeliverable. If the employee doesn’t receive the form, the employee may not be able to do their taxes which could become a bigger employee relations issue down the road.
Much the same way an outside firm can collect and validate employee names and SSNs, an outside firm can be hired to verify names and addresses to ensure 1095-C forms are delivered in a timely manner.
Example 5 – Wrong union classification – Often is the case that misinterpretation can lead to bad data. In the case of not completely understanding union codes, this truly applies. A common data mistake for groups with union populations was “checking the union flag” box even though the health benefits were provided by the employer (not a multi-employer plan). While this appears to be an innocent mistake, this mistake can translate to $250 per infraction. Even though there were good intentions, these good intentions resulted in bad data.
In summary in order to mitigate future risks employers need to take bad data very seriously. If you have any doubts about your data, it’s time to bring in an outside auditor to either 1) perform a data diagnostic or 2) perform an independent/objective audit. As they say, “there’s no time like the present – do it now!”