It’s been one of the safest bets on Wall Street: Study business at Wharton, Harvard, or the University of Chicago and apply for a job in investment banking. Play squash or lacrosse? That helps. So can the right fraternity or club.
When recruiting talent, the overwhelmingly white, male, and elite-educated cohort who run America’s top financial firms reflexively seek out younger versions of themselves. Psychologists call this tendency the “like-me” bias. Aspirants with a preppy pedigree have the inside track on coveted jobs in mergers and acquisitions, capital markets, corporate finance, and restructuring. Everyone else, including women and minorities, starts at a disadvantage.
But finance needs to change in this era of diversity and inclusion, and firms have turned to artificial intelligence for hiring help. A growing number, including Houlihan Lokey, Lazard, Moelis, and PJT Partners are using predictive algorithms to sift through applications and find those candidates most likely to become top performers—looking past the kinds of superficial signals that might sway a campus recruiter. Think of it as Match.com for finance.
They’re all working with Suited Inc., a Los Angeles startup that’s developed a recruiting tool expressly for investment banks. As in online dating, each applicant fills out a profile. This one covers everything from college education to personality traits and takes about 30 minutes to complete. A computer program runs those responses through a model, scoring them against an ideal set of attributes for each firm. “When you talk about it in the abstract, that’s one thing,” says Steven Murray, PJT Partners Inc.’s head of human resources. “But when you turn up with a pile of résumés and our bankers can see the data, suddenly they’re asking, ‘Oh, how did this person score?’ ”
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