From b6c64145ff60f62279d51a7c01619a46150e17cd Mon Sep 17 00:00:00 2001
From: Ornella Altunyan <44654695+ornellaalt@users.noreply.github.com>
Date: Tue, 1 Jun 2021 15:04:51 -0700
Subject: [PATCH] Add paragraph for credit card fraud detection

---
 Real-World/1-Applications/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Real-World/1-Applications/README.md b/Real-World/1-Applications/README.md
index 26e0aa19..58555b2c 100644
--- a/Real-World/1-Applications/README.md
+++ b/Real-World/1-Applications/README.md
@@ -13,7 +13,7 @@ One of the major consumers of classical machine learning models is the finance i
 
 We learned about [k-means clustering](Clustering/2-K-Means/README.md) earlier in the course, but how can it be used to solve problems related to credit card fraud?
 
-https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.680.1195&rep=rep1&type=pdf
+K-means clustering comes in handy during a credit card fraud detection technique called **outlier detection**. Outliers, or deviations in observations about a set of data, can tell us if a credit card is being used in a normal capacity, or if something funky is going on. As shown in [this paper](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.680.1195&rep=rep1&type=pdf), you can sort credit card data using a k-means clustering algorithm and assign each transaction to a cluster based on how much of an outlier it appears to be. Then, you can evaluate for riskiest cluster for fraudulent versus legitimate transactions.
 
 ### Wealth management