Finding risk equalized S/L account holders

Machine learning algorithms gather knowledge at an oversize scale, consolidate this knowledge and establish patterns during this knowledge from its own disposition history. Based on these patterns it judges whether a borrower is credit worthy or not and regulate the process of loan decisioning engine. So AI may help you make credit decisions at scale but it is Machine Learning that helps you improve your algorithms and ensure that you are one step ahead of the industry in understanding credit and market patterns. There are few platforms which offer help in credit analysis.

And how does Smart Card help? The risk equalization is much more accurate with obey and protect digital contracts.  Prequals are a ying and yang effort, both parties searching the appropriate S/L count. We end up with something like the search engine, the stored history becomes a personal grammar between the engine and human.



Once the risk segment is chosen, the S/L account can be auto priced, so we can see from this simple model that banks make buck via prequals, risk equalization. Trying to take excess profits from interest flow between deposits and loans is a distortion, the bank better off charging account entry and exit fees plus expanded account selections and ads.

The lowest level of risk is positive in smart card, just holding a smart card and using it to click on free articles has positive market information. So there is always a $20 dollar risk available somewhere if it is used for clicking on articles. The productivity gain is reduced ad congestion.

For advertisers the gain is much more specific knowledge about interest of the customer as the customer clicks through the articles with pennies. That personal contract promises the delivery of enough autonomous information. The advertisers can response with much more informative articles, likely to be clicked by selected viewers. Two way gain, we will have improved the 'trade press and order books' quality for the economy.