The main goal of this book to uncover the hidden rules behind our navigation between subjectivity and objectivity. We cannot deny (and I don’t have any intention to do), that algorithms are based on human’s assumptions. After the assumptions are made, the evaluation is the outcome of an automatic procedure). To set a credit score algorithms the first question to decide is the input data to be taken into account. FICO uses five factors: (i) the history how did you pay your bills; (ii) how much money you owe on credit cars. mortgages, loans etc.; (iii) the length of your credit history (the longer the better); (iv) mix of credit (the more divers the better); (v) new credit applications (don’t open too many new accounts too fast). The next natural questions is whether of not is reasonable assume that all the five factors have the same importance? Assuming the answer is yes, to each input variable we should assign 20\% weight. It is more plausible to assume that there are more and less important factors, and FICO uses the following weights:
- Payment history: 35%
- Amounts owed: 30%
- Length of credit history: 15%
- Credit mix in use: 10%
- New credit: 10%
We already know what are the factors the calculations take into account, and it is very crucial to know factors which don’t count. The Equal Credit Opportunity Act (ECA), doesn’t allow creditors in the United States to discriminate based on race, color, religion, national origin, sex, marital status, age.
There are variations how to calculate the credit score. Somewhat more technically speaking, the credit score (i.e a single number is the output of the algorithm), and the simplest way to get is by summing the weighted inputs. FICO uses a scale which runs from 300 to 850. As a blogger writes: “FICO should disclose what goes into its all important algorithms. They say they don’t want people to game them, but considering their importance in buying a house or a car, it can’t be a black box, that only FICO knows”. Figuere shows the national distribution of the FICO scores.
Stay tuned! next post: Struggle for fairness
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