Banking Institution Earns a Diverse Talent Pool with Scientific Candidate Screening Process

Company info

Banking & Financial services
40000+ Employees
$28 Billion US Annual turnover

Business Need

Banking Institution Earns a Diverse Talent Pool with Scientific Candidate Screening Process

This Fortune 1000 Banking organization has 2000 branches across the globe. The bank was planning to hire 1000+ employees within six months in different countries. They were seeking people who could help the bank build the digital ecosystem and leverage the power of data science. The bank was finding it challenging to find candidates with the required skills sets as they were not only competing with other banks but also organizations outside the banking square that were looking for candidates with the same skill sets.

Banking Institution Earns a Diverse Talent Pool with Scientific Candidate Screening Process

The KS team reviewed the hiring process of the bank. They met with the HR leadership team of the bank to understand the hiring criteria, current hiring process, medium of hiring, and skills required for the job role.

The KS team shadowed the bank’s hiring process right from sourcing the candidates, shortlisting the resumes, screening process, interview process, shortlisting, and then rolling the offers.

It was discovered during the analysis phase that the bank was urgently looking to hire talent with analytics and modeling, data science, business intelligence, programming, and machine learning skills.

As a result of this analysis, the following gaps were identified in the current hiring and screening process:

  1. The process was not automated, which allowed the biases to creep into the hiring decisions, resulting in acquiring the wrong talent.
  2. Job roles were not well defined, and the criteria were not mapped with the role-critical skills and competencies.
  3. Bank was unable to fulfil the Data Scientist requirement for the following functions:
    • SAS+ Banking
    • Retail Analytics
    • Loss Forecasting
    • Data Analytics – Banking
    • Manager Analytics – Credit Card Domain
    • Strategic Analytics

    However, the assessment criteria did not map with specific role-critical competencies, because of which the HR was unable to categorize candidates as per the functions’ requirements.

  4. As part of the initial profile screening, the candidates were supposed to appear for a manual technical test. If they obtained the threshold score, they were called for the subsequent rounds of interviews. As the process was manual, it was time-consuming, inefficient, and human error-ridden.
  5. The manual test covered the technical knowledge, albeit insufficiently. The test fell short of assessing or adequately predicting the candidate’s performance in the desired and job-critical competencies.

How did EasySIM help the bank screen better for a more fitting workforce?

KS recommended EasySIM—New Hire Screening, Onboarding, and Skill Management Platform, that has competency-mapped AI-enabled automated recruitment screening workflow that could help the bank to hire a diverse talent pool in a time-bound and scientific manner.

Bank’s existing competency rubrics, knowledge assets, job descriptions, and selection criteria were leveraged to build business specific scenarios and screening simulations for different strategic business units. The screening simulations mapped job critical competencies with various decision points. For example, an SBU screened the candidates on python programming skills, ML, and shell programming skills to assess the job readiness of the candidates, whereas another unit tested their shortlisted candidates on analytical, behavioural and leadership competencies.

EasySIM standardized the hiring process of the bank:

  • Candidates can now take the screening test from the comfort of their homes/campuses. AI-based candidate authentication ensured only shortlisted candidates took the test, and AI-based proctoring checked for unfair practices like cheating by capturing facial expressions and attention
  • Assigned evaluators were automatically notified by EasySIM of the candidates’ submissions. EasySIM’s Open Response template accepted responses in the form of software code, DFD, screen recordings, videos, audio, and text responses.
  • The EasySIM analytics engine provided hiring managers concrete data regarding the candidates’ aptitude, role-fitness, job readiness, and skill -level. The EasySIM screening process helped managers select candidates who, predictably, performed well on the jobs.
  • EasySIM’s new-hire screening model provided the bank with the much-needed objective, transparent, and bias-free hiring process.

Within the four months of using EasySIM, the bank was able to recruit more than 800 employees in eight countries with the right competencies. The candidate screening time was reduced from three weeks to about one week. The recruiting team’s productivity went up by 34%, and the overall hiring process satisfaction index rose by 63%.

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