Ma­chine Learn­ing, Blockchain an­swer to risks & frauds

Banking Frontiers - - Research Notes - Insurance Tech -

Due to var­i­ous frauds, in­sur­ance com­pa­nies in In­dia faced a loss of $50 bil­lion in 2011. If cal­cu­lated with pre­ci­sion, this loss amounted to al­most 9% of the in­sur­ance in­dus­try. If an­a­lyt­ics is not im­ple­mented prop­erly, in­sur­ance in­dus­try might face a greater loss.

The ris­ing trend of process au­toma­tion and lean com­pli­ance in the in­sur­ance sec­tor will cat­alyze the emer­gence of ma­chine learn­ing across ge­ogra­phies in the near fu­ture. AI-based con­sumer needs anal­y­sis can help im­prove the prob­a­bil­ity of lead-to-quote con­ver­sion by in­sur­ers, thus re­duc­ing turn­around times. Us­ing ma­chine learn­ing could also elim­i­nate sub­jec­tiv­ity in re­sponse and would lead to sav­ings in over­head costs to a great ex­tent.

The claims man­age­ment process has mul­ti­ple stages which can be au­to­mated by ma­chine learn­ing, us­ing a com­bi­na­tion of mod­el­ling, rules, text-min­ing and database searches. By ap­ply­ing ma­chine learn­ing tech­niques to claim au­dits, the ma­chines can help en­hance the abil­ity to learn from those through­out the claims life cy­cle. Ma­chine learn­ing can help iden­tify fraudulent claims and give the in­sur­ers a more ac­cu­rate idea of a per­son’s life­style choices, thus, en­sur­ing bet­ter com­pli­ance and pre­vent­ing mis-sell­ing of prod­ucts.

The claims process in the in­sur­ance in­dus­try is risk-prone and the in­dus­try spends over­all around $2 bil­lion to iden­tify fraud and com­pli­ance is­sues in the process. Blockchain can bring the cus­tomer closer to the in­sur­ance provider. Cus­tomers can ap­proach other cus­tomers to cre­ate an in­sur­ance iden­tity to in­sure them­selves as a group. Blockchain is the an­swer to risks and frauds as­so­ci­ated with the in­sur­ance process.

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