Building a Comprehensive Employee Verification System (EVS) with Advanced Features

The Challenge:

Employee credential verification has become very important as businesses continue to expand internationally. Companies are finding traditional methods like phone reference checks and background checks expensive, slow, and inaccurate. Businesses are also struggling to mitigate risks and validate information as there is a lack of AI-based insights.

To solve this exact problem TechQubix came to us for a solution. We discussed and asked what their requirements were and came up with a solution that they liked. Here we have discussed this particular system in this case study.

To solve the problems noted above, we first focused on the lack of a scalable and intelligent Employee Verification System.

• The current systems of manual examination were too slow and contained too many errors.
• Advanced integration into BI systems and AI tools was non-existent.
• There was no single solution available to handle employee verification and analytics.
• Integration of a chatbot to handle queries was missing, thus making the system less usable. 

We set out to build an advanced EVS that not only verifies employee data but also provides actionable insights using BI, AI, and data-intensive solutions.

The Solution:

We created the latest EVS system to have intelligent decision makers BI and analytics so as to better assist users. Enhanced communication was ensured through the usage of AIChatbots. Features that were tailored to HA also had to be added as well. Moreover, we introduced capabilities that allowed for the management of large amounts of information and portals for overseeing verification management. In building scalable and secure systems, we adopted industry standards developed in best practice Ivy projects in business intelligence, Artificial Intelligence, data science, and analytics.

Tech Stack:

Frontend:

• Angular: This framework was used to build a user web application that includes a wide range of features for ease of use, including simple navigation.

Backend:

• Node.js with Express.js: This combination served as a highly scalable and performant backend for the business logic of verification processes and data workflow.

Database:

• PostgreSQL: A highly efficient relational DB aimed to capture and preserve sensitive composite records of employees.
• ElasticSearch: For large-scale unstructured datasets and fast index-based searches.

BI and Analytics:

• Power BI: Added for monitoring real-time dashboards and detailed reports so that organizations can keep track of trends and verification metrics.

Artificial Intelligence:

Python-based AI models built using TensorFlow and scikit-learn for:

•  Fraud pattern prediction.
•  Resume verification automation and erroneous data flagging.
•  Employee risk assessment through intelligent data interpretation.

Chatbot:

• Dialog Flow: Integrated conversation-based bot designed to answer frequently asked questions, and provide status checkups, and onboarding procedures.

Deployment:

• AWS: Created a secure cloud infrastructure that easily scaled with the use of EC2, S3, and RDS.

Complexities and How We Overcame Them:

1. Data Integration:
   • The issue of collecting verification data from diverse sources presented a challenge. We created reliable ETL (Extract, Transform, Load) pipelines to automate the cleaning, normalization, and integration of data.
2. AI Model Accuracy:
   • Exceptional training datasets were needed to train AI models capable of detecting fraudulent activity. To ensure model accuracy and reliability, we implemented data augmentation as well as iterative testing.
3. Scalability:
   • To accommodate large datasets and numerous users concurrently, we employed a modular system design as well as optimized database indexing.
4. User Adoption:
   • Advanced features meant more complex user training and support. We met the challenge with the use of interactive tutorials and an easy-to-navigate UI.

Key Features:

1. Centralized Verification Management:
   • A single platform for managing employee qualifications, background checks, and reference verification.
2. BI and Analytics Integration:
   • Dashboards and reports for real-time monitoring of verification status, noted discrepancies, and reporting verification turnaround times.
   • Prediction of risks such as mismatched information or duplicate records.
3. AI-Powered Insights:
   • The recruitment process can now be fully automated and screened with either AI or CV robots, significantly decreasing manual work and improving effectiveness. 
   • Fraud detection models flagged employees’ background suspicious patterns. 
4. Chatbot for User Support:
   • An AI-powered SaaS chatbot is able to Sprint 24/7 to assist employees and HR teams on resources with verification questions and the automation of tasks.
   • Able to provide automated status updates and assist the user with verification status changes.
5. Portals for Data Management:
   • Role-based portals for HR teams, employees, and third-party verifiers.
   • Safe sharing and retrieval of confidential documents for employees. 
6. Comprehensive Data Handling:
   • Comprehensive Data Handling Designed for data-intensive operations with optimized performance for high volumes of verification data.
7. Successful Track Record:
   • Over 20 completed BI, AI, data research, and analytic projects strengthen the experience.
8. Compliance and Security:
   • We are compliant with GDPR and other relevant data protection regulations, thus there are no legal risks. 
   • Encryption and secure access protocols are used to protect sensitive information.

Outcomes:

1. Faster Verification:
   • Background check and validation credential verification is now being done 3x times quicker and takes less than 25 minutes to complete.
2. Improved Accuracy:
   • Never discrepancies were flagged, and verification was error-free marking the end of previously tedious checklists.
3. Data-Driven Decisions:
   • Integrating BI and analytics enabled HR departments to plan and make decisions with needed foresight regarding staffing in the future.
4. Enhanced User Experience:
   • Employees and HR personnel alike expressed joy at the ease of navigation that was facilitated by the chatbot and simple design.
5. Cost Efficiency:
   •  With the help of automation and integration, there was a needed decrease in the number of outside agencies, which allowed for lowered costs and heightened efficiency.
6. Scalability:
   • Due to the modular nature of the system, it was made simple for the organization to add new datasets and expand the number of users, making the transition for them easy and seamless.
7. Proven Expertise:
   • Made use of the know-how, lessons learned, and practices of the powerful BI, AI, and big data projects that were tailored and went through our hands, making adoption effective and working.

Conclusion:

Changes in how organizations verify employees were revamped from the old systems to the Employee Verification System (EVS) we developed. Fusing intuitive BI, AI, and chatbot graphics advanced how systems operated using data. An era of inefficient, outdated systems has been replaced, and how we verify employees with new methodologies sets a new standard. In addition to enhancing the way things are done, our recommendation systems helped HR departments to perform their activities efficiently. With this EVS, businesses can confidently hire, operate, and supervise their employees, reduce risks, and improve trust in the workplace.