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How trust & transparency lead the success of an Idiap student's Master's project in fraud detection
Raphaël Lüthi, Machine Learning and AI Lead at Groupe Mutuel
Wednesday, Oct. 11, 2023 · 2:22 p.m. · 03m 12s
Raphaël Lüthi leads machine learning and AI at Groupe Mutuel, one of the leading health insurance companies in Switzerland. With 6+ years of experience working on data and ML problems, Raphaël specialises in creating right conditions for data science projects (and teams) to thrive and reliably deliver value.
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who's not able to be here today and i'll try to do my best to
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present his project um and we talked about
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a lot you've talked about uh today about
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how to creating classes difficult and this is kind of the high level case study
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of how it's actually creating custom transparency as act absolutely necessary
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for the success of the projects uh uh especially this one
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so all's started by presenting the speaker who's not here
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gary see the has a double masters economics and management
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and computer science and you didn't master here yep
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and you was uh students well what's doing is masters
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with a circle mature and and i'm a machine learning a id that could lead to an
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never that background from it and and and that that's interesting and and and that s. b.
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uh so quickly the context and told me to have four of those of you who maybe don't know us this well
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uh where are um ensure us wasn't sure
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uh and we ensure people globally so of course healthcare probably know that
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but also twelve and a is small companies
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medium companies larger companies and self hatred printers
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the few numbers so we have about one point three million
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uh customers uh and it's twenty eight million a company clients and
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as you can imagine it it's a big operation so if we average
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over here we have about three thousand your clients coming in every year
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we get a round six thousands of the calls ten thousand emails let's
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uh what's important for this talk is seventy seven million uh thousands
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uh invoices that come in every day uh and we reimburse from all
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these invoices about two point through the twenty three point five million swiss francs
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so as you can imagine with that kind of of volume of process
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it's very difficult it's a two identified a
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fraud and identify the cases where some people
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are trying to gain the system to gain us and to get money that isn't do um
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the the context before we started this project was uh that
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we knew kind of from the literature from reading articles that
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in in general the company like us five to ten percent of claim are not necessarily fraud
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but could be suspicious and would raise an
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alarm and we have a a menu deterministic rules
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uh based on the legal framework based on our
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contracts to catch most of these cases but then of
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course there are some cases that pass these the deterministic rules which we don't really know how much they are
Conference Program
(Keynote Talk) Privacy-Preserving Machine Learning : theoretical and practical considerations
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5 minutes Q&A - Privacy-Preserving Machine Learning : theoretical and practical considerations
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Oct. 11, 2023 · 9:01 a.m.
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