Redefining Background Checks with AI Solutions

In the ever-evolving landscape of background check services, affordability, efficiency, and accuracy are paramount. To meet these demands, Turn has harnessed the power of Natural Language Processing (NLP) Generative AI combined with rule-based systems, drastically cutting the manual data quality review burden by over 85%. This article explores the transformative impact of this technology on our costs, quality control, and service delivery, while also emphasizing our commitment to algorithmic justice.

The challenge

Traditional background check (BGC) processes often rely on archaic technology and lack comprehensive report quality assurance (QA) processes. This results in significant challenges, including the existence of variable and non standardized data sources. Additionally, the use of algorithms in decision-making processes raises concerns about profiling and discrimination, necessitating solutions that enhance efficiency and precision while upholding ethical standards.

Background check providers vary significantly in their approach to data review and validation:

  • Sub-Par BGC Providers: These providers have zero review gates, blindly trusting unverified source data and displaying all data received. This lack of oversight results in inaccurate and unreliable reports, which often leads to downstream legal consequences.

  • Legacy BGC Providers (typically the largest industry incumbents): They may implement conservative flags on certain data facets in public records but ultimately lack efficient systems to match data and apply Fair Credit Reporting Act (FCRA) and reportability measures in an efficient manner.  This leads to longer turnaround times and inconsistent expectations.

  • Modern BGC Providers: These providers incorporate flags, systems for data matching, and reportability filters, ensuring a higher level of accuracy and compliance.

  • Elite BGC Providers: They innovate advanced technologies to minimize manual review efforts while leveraging more downstream data sources, employing solutions like large language models (LLMs) to enhance efficiency and accuracy.

The Solution: NLP Generative AI and Rule-Based Systems

Turn has implemented an advanced system that blends NLP Generative AI with rule-based algorithms designed to automate data dispositioning and scoring. This combined approach leverages sophisticated algorithms to analyze, interpret, and classify vast amounts of data with unprecedented accuracy.

Key Features

  1. Enhanced Accuracy: Deep learning models trained on diverse datasets minimize the risk of errors.
  2. Programmatic Data Dispositioning: Automated workflows ensure systematic data review and dispositioning according to predefined rules.
  3. Scoring and Evaluation: AI assigns scores based on data relevance and reliability, guided by rule-based criteria for consistent evaluation.

Impact on Operations

Decrease in Dispute Rates

Our advanced models significantly decrease dispute rates, showcasing the effectiveness of Turn’s innovative approach. While the industry standard ranges between 5-7%, Turn has achieved an impressively low dispute rate of just 0.8%. This represents nearly a 10x improvement over the industry baseline, underscoring our commitment to delivering unparalleled accuracy and reliability in our services.

Reduced Manual Review Efforts

By automating the bulk of the data quality review process, Turn has achieved an 85% reduction in manual review efforts. This allows our team to focus on more complex and value-added activities, enhancing overall productivity.


Improved Turnaround Time and Cost Efficiency

With AI-driven automation, background check completion times have drastically reduced, enabling quicker decision-making and improved hiring processes. The reduction in manual labor and associated costs translates to more affordable services for our clients, giving us a competitive advantage in the market.


Superior Quality

The precision of our combined NLP Generative AI and rule-based system ensures that our background checks are faster, more affordable, and of superior quality. The consistency and reliability of this AI-driven process enhance our reputation for excellence in the industry.

Commitment to Algorithmic Justice

Algorithmic justice refers to the ethical and equitable use of algorithms in decision-making processes, aiming to eliminate bias and ensure fair treatment of all candidates. According to a 2021 report by the Brookings Institution, biased algorithms can perpetuate inequalities and discriminatory practices if not properly regulated (West et al., 2021).

At Turn, we are acutely aware of these concerns. Our rule-based algorithm, combined with artificial intelligence, is implemented in a carefully controlled manner to ensure fairness and transparency. We actively work to mitigate potential biases and maintain the highest ethical standards in our operations by:

  • Regular Audits and Updates: Continuously auditing our algorithms to identify and rectify biases.

  • Environmental Controls: We don’t let AI operate autonomously. While it helps, we are still in control of all decision frameworks.

  • Diverse Data Sets: By fine tuning logic inputs to guardrail results, we ensure broad representation and accuracy.

  • Transparent Processes: Providing clear explanations of our AI processes to stakeholders, ensuring transparency and trust.

Conclusion

Turn’s integration of NLP Generative AI and rule-based systems has revolutionized our background check processes. This innovation reduces manual data review by over 85%, benefiting clients with faster, more accurate, and cost-effective background checks.

Clients can make quicker hiring decisions with our reduced turnaround times. Enhanced accuracy ensures reliable and compliant reports, minimizing hiring risks. Our efficient processes also translate to more affordable services, offering exceptional value.

Schedule some time with our experts for a free consultation to review your current screening process.

Disclaimer:

Turn’s Blog does not provide legal advice, guidance, or counsel. Companies should consult their own legal counsel to address their compliance responsibilities under the FCRA and applicable state and local laws. Turn explicitly disclaims any warranties or assumes responsibility for damages associated with or arising out of the provided information.

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