Overview

The client, a prominent rural housing finance firm, sought our help to innovate their attrition risk prediction and reporting processes. They faced challenges in predicting attrition at recruitment, during employment, and lacked a system for regular, relevant reports.

With our ML model, we achieved 89% prediction accuracy, automated recruitment, and offered a comprehensive employee data repository.

Story of the Customer

The client, a leading rural housing finance company, primarily operating within the NBFCs sector, with a heavy focus on customer relationship management. Their business was challenged by frequent employee turnover, negatively impacting customer loyalty and satisfaction.

They sought an innovative solution for predicting employee attrition risk and generating relevant monthly reports for strategic intervention and resource management.

The Challenge

  • One of the challenges was establishing a unified repository of data related to all employees and potential hires.
  • The other challenge comprised automating the tedious and time-consuming aspects of the recruitment process.
  • Ensuring a balance between freeing up human resources from repetitive tasks and maintaining a high level of customer interaction and satisfaction.

The Solution

  • Utilized machine learning models to assess attrition risk using both structured and unstructured data.
  • Automated recruitment process and created a unified repository for all employee data.
  • Designed an interface to display KPIs, aid HR, and measure cultural fit for potential hires.

The Result

  • The ML models identified high-risk employees with 89% accuracy, improving attrition risk mitigation.
  • An intuitive interface displayed employee KPIs, empowering HR for timely decision-making.
  • The system provided a 12-month cultural fit score for potential hires, enhancing recruitment by predicting attrition risk.