<transcript><audio_44>like we're looking at utility payments rental payments cellphone payments you know you you can look at all kinds of things that are outside the traditional credit spectrum and get a good idea of</transcript><transcript><audio_45>risk so what we're doing a tool right now is building up the sufficient level of data to where we have an understanding of the Canadian market that when somebody who's never been approved for a credit card or a bank loan comes to us we have an</transcript><transcript><audio_46>understanding of what we can look at and accurately determine the risk of loaning to them from a technical perspective you know when when we're looking at you know</transcript><transcript><audio_47>something like a cago competition we're really simplifying the problem you have a credit file in the credit file has a lot of debt in it and you extract the data and you create a vector out of it so what we're trying to do is simply</transcript><transcript><audio_48>establish a correlation between the vector that's input and a dependent variable so the dependent variable is in machine learning terms classification this is a classification problem can we</transcript><transcript><audio_49>determine what loans are good or bad right but that doesn't help you with pricing but the probability that alone is good or bad tells you how to price it because a loan that has a 92%</transcript><transcript><audio_50>probability of being profitable is worth more than a loan that has an 82% probability of being profitable so in essence all the problems become classification problems and what you're really measuring is a binary outcome but</transcript><transcript><audio_51>what is the probability of the binary outcome another example that where we're using AI at ad 2 is using deep learning to</transcript><transcript><audio_52>validate ID so when we ask somebody for an ID we want them to take a picture of their driver's license and we want them to take a picture of themselves and then we'll use a deep learning algorithm to</transcript> like we're looking at utility payments rental payments cellphone payments you know you you can look at all kinds of things that are outside the traditional credit spectrum and get a good idea of risk so what we're doing a tool right now is building up the sufficient level of data to where we have an understanding of the Canadian market that when somebody who's never been approved for a credit card or a bank loan comes to us we have an understanding of what we can look at and accurately determine the risk of loaning to them from a technical perspective you know when when we're looking at you know something like a cago competition we're really simplifying the problem you have a credit file in the credit file has a lot of debt in it and you extract the data and you create a vector out of it so what we're trying to do is simply establish a correlation between the vector that's input and a dependent variable so the dependent variable is in machine learning terms classification this is a classification problem can we determine what loans are good or bad right but that doesn't help you with pricing but the probability that alone is good or bad tells you how to price it because a loan that has a 92% probability of being profitable is worth more than a loan that has an 82% probability of being profitable so in essence all the problems become classification problems and what you're really measuring is a binary outcome but what is the probability of the binary outcome another example that where we're using AI at ad 2 is using deep learning to validate ID so when we ask somebody for an ID we want them to take a picture of their driver's license and we want them to take a picture of themselves and then we'll use a deep learning algorithm to
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