Case Study: Artificial Intelligence in the Financial Sector

The Application of Artificial Intelligence (AI) in the Credit Screening

of Clients in a Financial Institution.

The Application of Artificial Intelligence (AI) in the Credit Screening of Clients in a Financial Institution
The need for Credit Screening may occur in several circumstances in a Financial Institution (banking, insurance, investment banking, etc.). For example when a private person wants to borrow money, when a business wants extra credit, as part of a recruitment process, etc. Credit Screening means that the financial institution performs a background check on the applicant in order to decide whether to approve or reject e.g. the credit request. Such credit screening involves the collection of a number of attributes which are relevant for making such a decision. Depending on the value of these attributes the financial institution can decide whether to approve or reject the application. Such attributes are e.g. the annual income of the applicant, owned cash and properties, existing loans, former applications history, etc.
This case study will show you how to use an AI model to make credit screening decisions fast and accurately for credit card applications.

The dataset used in this case study contains data collected in a Japanese bank for 653 credit card applications [1]. Each record in the dataset corresponds to an APPROVE or REJECT credit card applicant. A part of the dataset can be seen in the image here under.

Credit Screening dataset for DecisionAI, Artificial Intelligence in Finance

Please note that the names and some of the values of the attributes are changed to symbols in order to protect the confidentiality of the bank.

The type and values of the different attributes:
  • A1 - Text data type with values: A, B.
  • A2 - Number data type with values in the range of: 13.75 – 76.75
  • A3 - Number data type with values in the range of: 0 - 28
  • A4 - Text data type with values: U, Y, L, T.
  • A5 - Text data type with values: G, P, GG.
  • A6 - Text data type with values: C, D, CC, I, J, K, M, R, Q, W, X, E, AA, FF.
  • A7 - Text data type with values: V, H, BB, J, N, Z, DD, FF, O.
  • A8 - Number data type with values in the range of: 0 - 28.5
  • A9 - Text data type with values: T, F.
  • A10 - Text data type with values: T, F.
  • A11 - Number data type with values in the range of: 0 - 67
  • A12 - Text data type with values: T, F.
  • A13 - Text data type with values: G, P, S.
  • A14 - Number data type with values in the range of: 0 - 2000
  • A15 - Number data type with values in the range of: 0 - 100000
  • A16 - This is the decision variable or class (Text data type) with values: APPROVE, REJECT
You can download the dataset here: Japanese Credit Screening dataset.

After collecting the data the training of the AI model is a very simple process. By feeding this data to the AI the Japanese bank could train an AI model which could be used for making the credit card application approval very fast, reliable and simple.

There are of course many other applications where an AI model can also be used in the Financial sector, as for example in decision making processes similar to the credit card application process, or in other types of risk analyses, in making buy/sell decisions on the financial market, etc.

References
1. Japanese Credit Screening dataset, Chiharu Sano


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