Artificial

1. NADAR SARASWATHI COLLEGE OF ARTS AND SCIENCE
ARTIFICAL INTELLIGENCE & MACHINE LEARNING
CREDIT CLASSIFICATION
presented by
k.diana devi
m.sc(cs)

3. In today’s world credit scores are essential to
determine are creditworthiness for lending
insititutions,and they impact everything from getting
a mortgage to rending an apartment. With the rise of
big data and machine learning, the credit scoring
process has been revolutionized, making it more
accurate and efficient. Machine learning algorithms
have the ability to analyze vast amount of data and
provide more accurate predictions than traditional
credit scoring models.

5. A Credit Score is a numerical
representation of a person’s credit worthiness
based on their credit history , income, and other
financial factors. It is a critical factor for lenders
and credit card companies when deciding whether
t approve a loan or extend credit. A credit score
range from 300 to 850 with higher scores
indicating better creditworthiness. A good credit
score is typically above 700 while a score below
600 is consider poor

8.  Machine learning algorithms can also take into account a
border range of data
including non-traditional data sources such as social media,
to make more accurate predictions.
One of the main benefits of machine learning in credit
scoring is its ability to reduce
bias.
 Traditional credit scoring models often have inherent
biases based on factors such
as race or gender.
Machine learning algorithms are designed to be unbiased
as they are trained on data
and not incorporate any preconceived biases.

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