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Loan Decision for Application #889219

We are sorry to inform you that your loan application was not successful.

Decided on: Dec. 13, 2024, 11:03 p.m.
Using: Decision Pipeline #1

Provenance-based Explanations for Data Subjects

We recorded the provenance of the above decision, from which explanations about the decision can be generated. If you have queries about the above decision, some explanations can be found below by clicking on the corresponding questions below.

Q: Has the loan decision been reached solely via automated means?

Whether a decision made solely by automated means without any meaningful human involvement.

The automated recommendation was reviewed by a credit officer (staff/112) whose decision was based on it application (applications/23), the automated recommendation (recommendation/23) itself, a credit reference (credit_history/23) and a fico score (fico_score/23).

  • This explanation helps determine whether GDPR Article 22 is applicable and thereby the prohibition applies:
    “The data subject shall have the right not to be subject to a decision based solely on automated processing…”
    It is therefore relevant for demonstrating compliance with Article 5(1)(a) (fairness principle) and Article 5(2) (principle of accountability).
  • This explanation should also help understand when best practice as unfolded in Recital 71 is met, e.g. to determine whether either child data or solely automated means have been used.
  • This explanation could also help determine whether the information provided to the data subject as per Article 13, 14 and 15 is adequate.

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Q: What types of data were used to assess my loan application?

A loan application assessment may consider several types of data about the applicant, such as credit scores, or other publicly available information.

The data Company Co considered for the borrower's loan application is the state, the self-reported annual income, type of application, employment length in years, the self-reported job title, the home ownership status, the listed amount of the loan, the purpose of the loan, the number of payments on the loan, the loan title, the address, the month the borrower's earliest reported credit line was opened, the number of mortgage accounts, the number of open credit lines, the number of derogatory public records, the number of public record bankruptcies, total credit revolving balance, revolving line utilization rate, the borrower's higher FICO score and the borrower's lower FICO score.

  • This explanation would help determine whether the data is ultimately relevant to the processing purposes as per Article 5(1)(c) (although this would only be the first step) and inform requests for access (Article 15), rectification (Article 16) and portability (Article 20). Ultimately its implementation would be useful for accountability purposes.

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Q: Which data was excluded from the decision process?

Some information you provided may not be used, either because it is not legal to do so orthe organisation deemed it is not relevant to the decision of approving your loan.

The data that Company Co excluded for the processing of the borrower's loan application are the self-reported job title, the address, the number of mortgage accounts, the loan title provides by the borrower, the home ownership status provides by the borrower during registration, type of application, the number of public record bankruptcies and the number of derogatory public records.

  • This explanation would help determine whether the data is ultimately relevant to the processing purposes as per Article 5(1)(c) (although this would only be the first step). Ultimately its implementation would be useful for accountability purposes.

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Q: Where did you get those data about me?

Data considered by a credit institution may come from a variety of sources.

The data sources were the borrower's loan application (applications/23) provides by the loan applicant (applicants/23), the borrower's credit reference (credit_history/23) provides by credit referencing agency (credit_agency) at 2024-12-10T05:12:12.382989, the borrower's FICO score (fico_score/23) provides by credit referencing agency (fico) at 2024-12-13T14:54:32.924062 and the borrower's loan application (applications/23) provides by the loan applicant (applicants/23).

This explanation would help assess the lawfulness, fairness and transparency of the processing (Art. 5(1)(a)), the accuracy of the data (Article 5(1)(d)) and inform requests for access (Article 15) and rectification (16). Ultimately its implementation would be useful for accountability purposes.

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Q: How timely relevant is the data used for assessing my loan?

Data used in loan decision making may be collected a long time ago and no longer relevant.

The external data sources were the borrower's credit reference (credit_history/23) provides by credit referencing agency (credit_agency) at 2024-12-10T05:12:12.382989, the borrower's FICO score (fico_score/23) provides by credit referencing agency (fico) at 2024-12-13T14:54:32.924062, the borrower's credit reference (credit_history/23) provides by credit referencing agency (credit_agency) at 2024-12-10T05:12:12.382989 and the borrower's FICO score (fico_score/23) provides by credit referencing agency (fico) at 2024-12-13T14:54:32.924062.

  • This explanation would help determine whether the data is ultimately relevant to the processing purposes over time as per Article 5(1)(e) and inform requests for access (Article 15), rectification (Article 16) and erasure (Article 17). Ultimately its implementation would be useful for accountability purposes.

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Q: Are the data used for assessing my loan application correct?

Data correctness may not be guaranteed: the applicant may have made a typo in their application or the data provided by a third-party may be inaccurate.

You can check the data supplied in your original application for any inaccuracy.

In addition, we obtained the following data from third-party providers:

  • Accounts: 7.0
    The number of open credit lines in the borrower's credit file
  • Mortgages: 0.0
    Number of mortgage accounts
  • Revolving Balance: 8983.0
    Total credit revolving balance
  • Credit Utilization Rate: 34.0
    Revolving line utilization rate, or the amount of credit the borrower is using
  • Earliest Credit Line: May-2006
    The month the borrower's earliest reported credit line was opened
  • Public Records: 0.0
    Number of derogatory public records
  • Bankruptcies: 0.0
    Number of public record bankruptcies

We also calculated the following data from the above and your supplied application data:

  • Interest Rate: 12.62
    Interest rate on the loan
  • Installment: 268.09
    The monthly payment owed by the borrower when the loan starts
  • Debt to Income: 5.9
    The ratio of debt over income
  • Grade: C
    Loan grade
  • Subgrade: C1
    Loan subgrade

  • This explanation would help determine whether the data is ultimately relevant to the processing purposes over time as per Article 5(1)(e) and inform requests for access (Article 15), rectification (Article 16) and erasure (Article 17). Ultimately its implementation would be useful for accountability purposes.

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Q: Is there bias introduced in the decision by my home ownership status?

An automated decision may be sensitive to a particular demographic such as whether the loan applicant owns a home or not, for instance. In this demonstrator, you can test such sensitivity over home ownership by running a counter-factual test over this characteristic.

The decision pipeline will be run on your application again but with different value on the home_ownership field (compared to what you provided in the application form). The possible values for this field are: OWN, MORTGAGE, RENT, and OTHER.

Check counter-factual fairness on home ownership

The provenance of simulated counter-factual decisions are recorded and used to generate the above explanation.

  • This explanation would help determine whether the data is processed lawfully and fairly (Article 5(1)(a)) (although this would not lead to a complete fairness assessment). This explanation would also help determine whether special categories of data as defined in Article 9 have been processed. Ultimately its implementation would be useful for accountability purposes.
  • The UK Equality Act prohibits differential treatments based on protected characteristics (i.e. age, disability, gender reassignment, marriage and civil partnership, race, religion or belief, sex, sexual orientation), unless exceptions apply.

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The explanations were generated with profile Third Person. Choose a different profile below.

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