Risk Modeling/ Credit Scoring
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This technology is often used in fast lending for small amounts, when registering consumer express loans in real stores by credit companies, in the business of mobile operators or insurance companies.
Scoring is the assignment of points by filling out a certain questionnaire developed by credit risk assessors. Based on the results of the points gained, the system automatically decides to approve or refuse to issue a loan.
Risk Modeling is a high priority for the banking industry. It helps them to formulate new strategies for assessing their performance.
With Risk Modeling, banks are able to analyze the default rate and develop strategies to reinforce their lending schemes
Benefits for the company
Risk Modeling a high priority for the banking industry. It helps them to formulate new strategies for assessing their performance. Credit Risk Modeling is one of its most important aspects. Credit Risk Modeling allows banks to analyze how their loan will be repaid.
Unlike the traditional methods which are usually limited to essential information such as credit score, ML can analyze significant volumes of personal information to reduce their risk.
Feasability
High
Type of expertise/ AI domain
Machine Learning and Statistics
Internal data required
Credit Score, personal information, digital footprint
External data possible
FICO, Experian, TransUnion/Equifax
With the help of Big Data and Data Science, banking industries are able to analyze and classify defaulters before sanctioning loan in a high-risk scenario.