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La Ceiba Statistical Model

La Ceiba Statistical Model. Analysis. Data for All Loans & All Clients was Assessed. Events assessed were the following:. Events assessed were the following:. Paid on Time. Events assessed were the following:. Paid 1-28 Days Late. Events assessed were the following:.

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La Ceiba Statistical Model

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  1. La Ceiba Statistical Model

  2. Analysis

  3. Data for All Loans & All Clients was Assessed

  4. Events assessed were the following:

  5. Events assessed were the following: Paid on Time

  6. Events assessed were the following: Paid 1-28 Days Late

  7. Events assessed were the following: Paid 29-56 Days Late

  8. Events assessed were the following: Paid > 56 Days Late

  9. Events assessed were the following: Outstanding

  10. What you see here is the Distribution for each level on the Loan Ladder, Starting with L500

  11. Using these distributions, I calculated the probability of each event, given a certain Level on the Loan Ladder.

  12. Since no function does what I need to do directly to create a statistical model, I listed each Event 100 times so that the same probabilities would be achieved.

  13. I then created a list to display up to 50 clients. The plan is to create this for each loan level, But for now I only have L500.

  14. The result of the complex formula is this: After specifying how many clients I want to model, It will generate outcomes of their loans based on the historical probabilities of each event.

  15. It is currently set up for 3 Clients. The Model generated One “Outstanding” Loan, and 2 Loans paid within 28 Days

  16. The investment required for 3 Loans, for 6 months at a 30% Annual Interest rate is $79.61 USD

  17. The Expected return from all 3 Loans, after interest, is $91.55 USD.

  18. With just one client “outstanding,” the actual return is $61.03 USD, a loss of $18.58 or 23% of the principal.

  19. I Decided to test whether or not this was the exception To the rule by performing multiple trials. Each Trial is a Statistically accurate test, using historical data and newly Generated clients and outcomes.

  20. The result was that given the current trends and statistics, we can only expect 17% (5/30) of sets of 3 clients to payback their loans at a profit to La Ceiba.

  21. Our Conclusion is that we need to change the structure of the loan ladder to Allow an evaluation period, but still gain a profit that we can return to more clients.

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