writing ▁of ▁corpor ate ▁se cur ities ▁is ▁carried ▁out ▁through ▁separate ▁holding - company ▁affili ates , ▁called ▁se cur ities ▁affili ates ▁or ▁Section ▁ 2 0 ▁affili ates . ▁ ▁Of ▁late , ▁the ▁disc ourse ▁on ▁under writing ▁has ▁been ▁domin ated ▁by ▁the ▁advent ▁of ▁machine ▁learning ▁in ▁this ▁space . ▁These ▁prof ound ▁techn ological ▁innov ations ▁are ▁alter ing ▁the ▁way ▁traditional ▁under writing ▁score cards ▁have ▁been ▁built , ▁and ▁are ▁disp la cing ▁human ▁under writ ers ▁with ▁autom ation . ▁Natural ▁language ▁understanding ▁allows ▁the ▁consideration ▁of ▁more ▁sources ▁of ▁information ▁to ▁assess ▁risk ▁than ▁used ▁previously . ▁These ▁algorithms ▁typically ▁use ▁modern ▁data ▁sources ▁such ▁as ▁S MS ▁/ ▁Email ▁for ▁bank ing ▁information , ▁location ▁data ▁to ▁verify ▁addresses , ▁and ▁so ▁on . ▁Several ▁fir ms ▁are ▁trying ▁to ▁build ▁models ▁that ▁can ▁gauge ▁a ▁customer ' s ▁willing ness ▁to ▁pay ▁using ▁social ▁media ▁data ▁by ▁applying ▁natural ▁language ▁understanding ▁algorithms ▁which ▁essentially ▁try ▁to ▁analy se ▁and ▁quant ify ▁a ▁person ' s ▁popular ity ▁/ ▁lik ability ▁and ▁so ▁on , ▁with ▁the ▁prem ise ▁being ▁that ▁people ▁scoring ▁high ▁on ▁these ▁parameters ▁are ▁less ▁likely ▁to ▁default ▁on ▁a ▁loan . ▁However , ▁this ▁area ▁is ▁still ▁vast ly ▁subject ive . ▁ ▁In sur ance ▁under writing ▁In sur ance ▁under writ ers ▁evaluate ▁the ▁risk ▁and ▁expos ures ▁of ▁potential ▁clients . ▁They ▁decide ▁how ▁much ▁coverage ▁the ▁client ▁should ▁receive , ▁how ▁much