The past decade has seen explosive growth in the number of requestsfor firearms-related criminal background checks, leaving the National Instant Criminal Background Check System (NICS) scrambling to accurately process an unprecedented amount of data. In fact, from 2004-2014, NICS saw a whopping 241 percent increase in applications for firearms licenses and, in 2015, it processed a record-breaking number of background checks (23,141,970).
NICS denied 700,000 of the 100 million background checks it processed over the last 10 years . This may seem like a low number, but consider how new technology could significantly increase the accuracy of personally identifiable information and decrease the number of applicants who are falsely categorized, one way or the other.
If NICS records are any indicator of how rapidly it must evolve its firearm screening methods, we should consider just how significantly advanced analytics could improve the data quality issues bogging down these processes.
When it comes to maximizing information gleaned from big data, advanced analytics heightens the speed and improves the quality of data interpretation by cherry-picking anomalies and correlations, thus allowing analysts to focus on the most relevant pieces of information to assign risk scores and reduce margins of error.
There are a number of federal considerations that might result in the denial of a firearm license, including past felonies, extended periods of incarceration, illegal residency, outstanding warrants, drug use or mental health issues.
Further complicating the background check process are widely varying state-level regulations. These are all, effectively, “rules” that can be rapidly manipulated by algorithms on a broad scale to spotlight the most significant data points (even across disparate, publically available data sets) for a human investigator.
Advanced analytics also points the investigator to anomalies within data sets–for instance, when an applicant provides a nonexistent or abandoned address, or if their social security number is inconsistent with other identifying information. This reduces the burden of the investigator, who no longer has to dig through large swaths of mostly insignificant information and enables them to focus on categorically suspicious data, such as falsified identities that may have been submitted in an attempt to bypass the background check process, entirely.
The implementation of analytics-based solutions would further allow federal background investigators to assign applicants an aggregate risk profile that scores each applicant based on potential risk. For example, “low risk” applicants that are denied permits could have their appeals reviewed more quickly.
Such structural improvements would bring efficiency to the appeals process, resulting in fewer false positives and shorter wait times for law-abiding citizens, and not as many guns in the hands of known criminals.
Of course, many of the government’s other “background checkers”–-such as the Transportation Security Administration, Department of Homeland Security and the new National Investigative Service Agency-–could similarly improve their processes by more effectively leveraging advanced analytics.
Not only would a faster, more accurate process save the government an enormous amount of money, it would curb many of the inaccuracies (and headaches) associated with the current government review process.
But, for now, you’ll still need to remove your shoes while moving through airport security.
Lori Davis is the manager of the National Security Group with SAS, where she leads the group’s efforts to support the business and mission goals of the Intelligence Community (IC). Specific areas of focus include using analytics to detect and prevent insider threats and cyberattacks, as well as using analytics to improve the performance of the IC.