Better Financial Risk Management With Machine Learning

How does one go about preventing credit frauds, ensuring the reliability of someone that one proposes to give a loan to, or, more generally, taking safer decisions when it comes to something as tricky as financial risk management?

This isn’t a new question, of course – it’s been around as long as people have borrowed and bartered, and over the ages the finance industry has come up with several methods of trying to address this concern. The latest has been to take the approach of statistical modelling, and one has to admit, this has helped matters tremendously. Banks are able to paint a more detailed picture of what their overall risk profile is, and avoid seriously risky situations.

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Informatics, the future of HR

I remember one of my customer meetings last month quite vividly. It was with the HR General Manager of a multi-national manufacturing company. Perhaps one of the most affable people I have met, he spoke at length about his experience managing HR in different companies and interspersed his speech with lively anecdotes and his opinion about the changing role of HR.

“HR owes its origin to Industrial Relations” he began. The nature of the workforce has, obviously, changed a lot since the Industrial Revolution, but it is not difficult to imagine that about a hundred and fifty years back in Victorian England, there might have been a clique of managers specialising in dealing with workers in coalmines or shipyards. Around the turn of the century, with communism in the air, it couldn’t have been too easy for those managers. A seismic change might have begun.

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There is more to Financial Risk than just checking credit ratings!

Financial fraud is all around us. People routinely con banks or simply default on large loans or credit card bills. There are others who take large insurance policies and submit wrong claims. I have had the good fortune of meeting many risk officers and underwriters, and have heard sordid tales of financial crime. The ingenuity of fraudsters never ceases to amaze me. Risk officers and underwriters are forever playing catch-up. Every time they adjust their systems and processes, a new type of fraud emerges!

I have realised that relying on a simple credit score isn’t enough. Neither is just checking one’s address. We have to go back to basics – establish some fundamental facts about the person who is about to take a loan, a credit card, or an insurance policy. It is important to know who the person is and uncover relevant information about their background.

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Deciphering the Exit Interview

In 1939, a team of British mathematicians set about breaking the Enigma code, a system used by the Axis forces to send and receive secret messages. The Enigma machine could be coded in 10 19 ways. Armed with statistical weapons, the team built a system that could significantly reduce the set of possibilities required to decode the Enigma machine. The Enigma code was broken every day, saving millions of lives and swinging the outcome of the war towards the Allies.

It is commonly held that people lie a lot during Exit Interviews. Some people therefore consider exit interviews an enigma! If this were true, it would indeed be a tragedy, for Exit Interviews are an excellent way to determine what is wrong with teams and H.R. policies.

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Executive Search and the bewitching spell of stories

Stories are powerful. Nothing illustrates that more than the story of M.K. Gandhi himself. His attire, the way he cultivated his image, the spectacle of the Dandi march, in fact everything he did, captured the imagination of the world. It was a powerful narrative, that of a man who dealt in absolutes – he had a grand dream but wouldn’t comprise on principles to achieve it. That is a powerful story.

Most C.E.O.s will testify to the power of stories. It what they use to rally their teams, sell their product, or convince investors. Unfortunately, it is also what they use to get a new job. Therefore, when searching for executives to hire, one has to be wary of stories.

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Would I Lie to You?

There are two ways of approaching your recruitment requirements:

  • Passive: Find candidates, ask them for their personal information, conduct a background verification, and decide whether or not to hire them depending on what results you get from their checks.
  • Active: Instead of just waiting around for the results, you use data and analytics to derive insights about the people you want to recruit, and take pre-emptive decisions to weed out those candidates who will cause your organisation harm.

So which one is better?

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