The comment by @user2974951 assumes that the two variables are independent. To understand how [s]he arrived at this result, have a look of the two dimensional random variable $(X,Y)$ and integrate over the region where $X>Y$: 
If the two variables are independent with densities $f(x)$ and $g(y)$, respectively, the probability density of $(X,Y)$ is the product of both desitiesdensities $f(x)\cdot g(y)$.
The comment by @user2974951 assumes that the two variables are independent. To understand how [s]he arrived at this result, have a look of the two dimensional random variable $(X,Y)$ and integrate over the region where $X>Y$:
If the two variables are independent with densities $f(x)$ and $g(y)$, respectively, the probability density of $(X,Y)$ is the product of both desities $f(x)\cdot g(y)$.
The comment by @user2974951 assumes that the two variables are independent. To understand how [s]he arrived at this result, have a look of the two dimensional random variable $(X,Y)$ and integrate over the region where $X>Y$:
If the two variables are independent with densities $f(x)$ and $g(y)$, respectively, the probability density of $(X,Y)$ is the product of both densities $f(x)\cdot g(y)$.
The comment by @user2974951 assumes that the two variables are independent. To understand how [s]he arrived at this result, have a look of the two dimensional random variable $(X,Y)$ and integrate over the region where $X>Y$:
If the two variables are independent with densities $f(x)$ and $g(y)$, respectively, the probability density of $(X,Y)$ is the product of both desities $f(x)\cdot g(y)$.
