Building a scorecard I would like to build a scorecard from scratch.

Currently I have been working on data cleaning, and I created a dataset for training and another for test.
What I have is a dataset of around 700 rows (maybe not so many as I could expect; it is for a computer science project), 20 columns. The target is if the borrower has defaulted or not (indicator). Most of the factors are categorical or Boolean.
I am assigning a score based on these parameters, specifically:

- if the account has defaulted in the last 90 dats, then assign 60;
- if the account has defaulted in the last 30 days, then assign 50;
- if the account has not defaulted in the last 90 days, then assign 80;
...


I would like to know if this is a good approach and/or if there is an algorithm to assign scores to the accounts or if I should do something different. 
I do not know very well the logic behind a scorecard, so any information would be extremely useful.

Thanks
What you describe it is NOT a typical approach for scorecard design.

Typically binary target - "0/1" is used for target variable and logistic regression is typical method to create a scorecard.

Typically to define "default trigger" one fixes two time periods:

1) fix say 1 year from credit start, that means defaults happening after 1 year are not considered as defaults

2) fix say 90 day period - means "default trigger" is 1 if client delays payment for more than 90 days from the payment date, that means you do not consider as "default" if delay is less than 90 days. 

There can be some other conditions - like threshold on amount of delinquency - if below - do not consider as "default". And may be some other.

You might consider the book:

Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring 

by Naeem Siddiqi