How AI can help you with background screening
Uber, a well-known multinational app-based cab booking service, had to settle a $10 million lawsuit in 2016 due to its failure to conduct a background check on the drivers. However, it was only one instance where Uber failed to conduct background checks on drivers, which led the company into legal hassles. Another report states that Uber paid an $8.9 million fine in Colorado (USA) for recruiting drivers with past criminal records and motor vehicle offenses.
Background screening for customers and employees is challenging for banking, finance, and many other sectors. Adopting the latest technologies in background screening is the only viable solution for these companies. Fortunately, artificial intelligence has devised a promising mechanism that assures error-free and quick background screening.
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Limitations of Traditional Background Check
The traditional background screening process has two prominent limitations. Firstly, the process needs to assure 100% accuracy, and many well-known companies have paid hefty fines due to failures in background screening. Secondly, the conventional background screening is time-consuming, which makes the process unproductive and costly.
In 2017, JP Morgan Chase, a leading American investment bank and financing company, was slapped with a $1.25 million fine by the American Financial Industry Regulatory Authority (FINRA).
According to FINRA’s report, the company failed to conduct a background screening on more than 8,500 employees.
William Hill, a reputed British bookmaker, was also fined 6.2 million pounds in 2018 for accepting money from people with criminal backgrounds. The company should have checked these people’s backgrounds before accepting funds from them.
Why are changes in approach required?
The examples above suggest the limitations of the traditional background check. The examples also stress adapting to the modern AI-integrated mechanism to bring accuracy and swiftness to background screening. Artificial intelligence can process a large amount of data in a short time. On the other hand, it prioritizes the relevant information and omits unnecessary data from the verification process. Finally, a data-driven decision ensures error-free background screening.
Keeping sensitive financial information safe is a massive challenge for the banking sector. Many banks fail to maintain security, which compromises sensitive customer information. Such banking and financial institutions lose the trust of their buyers. AI-integrated background screening also helps overcome security concerns for banking institutions. Besides processing and analyzing data quickly, AI tools ensure that the standard measures to maintain data security are followed.
A More Detailed and Accurate Analysis
Background screening is complex for small to large enterprises due to the absence of a mechanism to perform detailed analysis. The companies that deploy manual verification need to conduct in-depth data analysis. As a result, mistakes appear in background screening.
A few mistakes may appear small or ignorable initially, but such mistakes can harm a company’s reputation in the long term. Moreover, the states change policies after certain intervals. Maintaining compliance in background screening becomes complex when new policies emerge.
Adopting artificial intelligence for background screening is the best way of overcoming such concerns. The AI-integrated background screening system can run in-depth analyses and determine the factors that may lead to noncompliance with the state’s policies. Moreover, a detailed analysis is essential to maintaining accuracy in background screening.
A detailed report always helps business managers make the right decisions on hiring employees for their organizations. Similarly, the banking sector offers certain products to customers with exceptional financial records. Detailed background screening is crucial before offering such products to customers.
A Better Understanding of Multiple Risks
Manual background screening overlooks the risks that are minor or negligible. But small mistakes often become the system’s loopholes, which miscreants may exploit to commit fraudulent activities. Companies like Uber, JP Morgan Chase, and others have suffered immensely from faulty manual verification. Such companies paid hefty fines and suffered massive financial losses due to the mistakes in manual background screening.
Artificial intelligence deals with an extensive data set and identify all potential risks precisely.
An automated system does not overlook small or negligible risks. As a result, it can create a detailed risk assessment report for the users. As a result, companies recruit people with the cleanest background records. Moreover, the banking sector can identify all potential risks before offering users credit or other financial products.
More Attention Given to the Right Data Points
AI-integrated systems have to follow certain algorithms. Therefore, background screening becomes a systematic process with AI-based automation. A systematic process gives more importance to relevant data. The system follows its algorithm to prioritize data analysis to quickly and efficiently identify potential risks.
Manual background screening often fails to prioritize the right data in a risk assessment process. Therefore, much time is well spent dealing with data that holds little relevance. Artificial intelligence technology makes risk assessment more systematic by making it easier to identify relevant data and prioritize analyzing it in an organized manner.
Giving more attention to the relevant data makes the overall risk assessment in background screening more accurate. Nevertheless, it also improves the overall background screening speed.
Data from Various Sources for Background Analysis
Background screening involves collecting data from different sources and accumulating it on one platform to create an accurate background report. A manual process may need to be revised to acquire data from different sources for various technical reasons. Nevertheless, humans tend to skip processes that are prolonged and tiring.
But, an AI-integrated system does not leave a stone unturned in analyzing the data and creating background screening reports. The system can quickly establish communication with different sources and collect data systematically. It runs a relevancy check once data from different sources has been collected. Finally, it prepares a detailed background report after omitting the irrelevant data from the system.
Automation Speeds up the Process
Traditional background screening could be faster and more convenient. Many companies adopt aggressive business expansion policies, making it difficult to get accustomed to the slow conventional background screening process. Many companies invite errors to make the existing process faster, eventually leading to hefty financial penalties and reputation damage.
Thanks to automation in background screening, the process has become faster. Since the process does not depend on human intervention, it runs systematically to access online reports, patterns and records to create background screening reports. The overall process happens quickly, and the system can run background analyses of multiple individuals simultaneously.
So, these are the changes that AI-integrated automated systems have brought to background screening services. Both small and large companies have benefitted immensely due to more accuracy and lesser time consumption in background checks. Moreover, traditional background screening is costly, while advanced AI-integrated systems do not require human intervention. As a result, background screening becomes fast, effective, and cost-effective.
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