Examples from Lending Industry
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In the lending industry, a simple decision matrix is often involved with a few parameters to arrive at an in-principle approval.
An example decision matrix is as follows:
There are also scenarios that involve a complex decision tree. In the below example, the applicant's age sits at the top of the decision tree. Based on the applicant's age, additional conditions are involved, resulting in a GO or a NOGO decision.
When the applicant's age is >=35, either of (applicant ownership or business ownership) must be Owned.
When the applicant's age is <35, both (applicant ownership and business ownership) must be Owned.
The above are some examples of how specific facts about a loan applicant are used in a series of rules to decide whether to proceed with the loan application.
In the next section, let us look at an introduction of Python library.
Between 650 and 800
Married or Unspeficied
Owned by Self or Owned by Family
GO
Less than 650
NOGO
greater than 800
GO
>=35
in [Owned by Self, Owned by Family]
in [Owned by Self, Owned by Family]
GO
>=35
in [Owned by Self, Owned by Family]
in [Rented]
GO
>=35
in [Rented]
in [Owned by Self, Owned by Family]
GO
>=35
in [Rented]
in [Rented]
NOGO
<35
in [Rented]
in [Rented]
NOGO
<35
in [Owned by Self, Owned by Family]
in [Rented]
NOGO
<35
in [Rented]
in [Owned by Self, Owned by Family]
NOGO
<35
in [Owned by Self, Owned by Family]
in [Owned by Self, Owned by Family]
GO