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Financial institutions have to deal more and more with increasing regulations and obligations. One of the new accounting rule-sets that are coming their way is called "Basel II". One interesting part of "Basel II" says that banks need to keep a certain standard reserve for bad loans. However, if the bank in question can prove that they have a better then average loans portofolio, they can lower the percentage of money that they have to keep in reserve. This is called the Internal Rating Based (IRB) calculation. So, instead of letting money sit idle in the reserve, this money can be used to generate new income from other banking operations. However, how do yo prove that your customers are better than average? Well, you can use a data warehouse to do this. The data warehouse needs to acquire information from different parts of the lending process, from the request for a loan, over the acceptance to the processing of the monthly payments. A lot of information is also uploaded in the warehouse about the customers info and the different kind of products he has. For example if a customer has a lot of savings, this would lower the risk of a bad loan. A data warehouse can then excell in the creation of reports that combine all this info, providing a relative easy solution to this complex problem. The use of a data warehouse is also encouraged in this case because the IRB system has to have historical data for the past 6 years in order to be valid.
Direct Marketing
This is one area where a business intelligence system can really make a difference. That is because a direct marketing manager can make a lot of use of a data warehouse to report on his customers. For example if he has a budget to send a mailing to his 1000 best customers, he needs to select these 1000 customers. To determine what his best customers are, the manager wants to use Recency, Frequency and Monetary value (RFM) as parameters. These parameters are determined in the data warehouse by first gathering all the sales data in a sales fact table. (sales per customer per product). Every month, the data warehouse looks at all the customers and looks in the sales fact table and determines
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