The month of May can be cruel – it’s the peak of summer in most parts of India, and being out for even a few minutes in the sun can be gruelling. But the weather isn’t the only thing you should watch out for in May. If you’re a recruiter, you should be paying extra-close attention to the ‘Experience’ section of resumes that candidates hand over to you.
It so happens that the maximum instances of people lying about their work experience to obtain a job, occur in May. This is why one needs to be extra vigilant at that time.
And what about October? Summer’s well over by then, and even the monsoon has faded – but even then, the heat is on. As the country goes into festival mode, and there is a flurry of shopping before the winter sets in, recruiters would be well advised to train their focus on the ‘Qualifications’ section of candidates’ resumes.
The maximum instances of people lying about their qualifications in order to get a job, take place in the month of October.
But the worst month is December: this is when, on average, one sees the maximum number of false claims about experience and qualifications combined. So if you’re a recruiter looking to hire a large number of people towards the end of the year, it would be a wise idea to subject applications to stricter scrutiny than usual.
But where do all these proclamations come from? Am I simply making stuff up, to sound like a sage on a hill, issuing warnings willy-nilly? Of course not! Everything I’ve said so far in this post can be backed up with tons of data that we’ve gathered over our five years’ experience in conducting people information searches and verifications. As a sample, take a look at the graph below, showing the percentage of ‘Red’, or clearly false, declarations about people’s experience and qualifications that we saw over a one-year period, from May 2015 to April 2016:
This graph, interesting as it is, however, is only the tip of the proverbial iceberg of data that we handle and analyse every single day. The really valuable insights come not only from understanding overall monthly distributions as in the graph above – though they are a good place to start – but by digging much deeper into the data, and deriving insights and understanding that one may not even have imagined from a surface examination of the data.
With the proper application of methods such as feature scaling and mean normalisation, we are able to apply multivariate linear regression to generate outcome predictions that take into account over a hundred and fifty data variables about each individual. Imagine what you could do with that!
The time and cost savings from such exercises can be phenomenal, and the overall benefit towards achieving an organisation’s H.R. objectives can be tremendous – but one needs to keep a close eye on the data, and apply the right analytical methods to draw useful learnings from them.
And oh – best of luck for December! 🙂