1) simpler, yet more accurate,
2) thorough, yet concise,
3) becomes more, not less, predictive over time, and
4) is statistical in nature, yet is transparent and passes various tests of reason by simple inspection.

ease of understanding
stresses loan affordability
adaptibility and ease of updating
ability to be effectively monitored
is based on proven lending principles
provides adequate controls to limit risk
affords early warning in advance of performance declines

A) the lowest monthly payment for a loan amount sufficiently large to put the borrower at considerable risk if everything doesn't go according to plan, or

B) the highest return for the lender associated with somewhat less suitable loan products for the borrower's core need.

Sadly, we are now witnessing the fallout from unchecked greed, namely:

i) borrower's homes in foreclosure
ii) lenders having higher loan loss reserves and owning more property in their OREO portfolio
iii) investors holding ill-liquid investments with accompanying negative returns.

"Risks in the financial services industry are constantly changing and evolving. Risk systems must change and evolve as well. Faithful adherence to what has worked in the past is a blueprint for extinction in the future."

FICO is a one-size fits all technology that is also a black box, i.e. consumers are not told what characteristics are included in their score and how many points they were assigned for each of those factors. Consumers do not even know how specifically to improve their FICO Credit Score! While it is true that credit scoring is more consistent that a purely judgmental system of credit granting, it is rooted in models that attempt to identify statistical correlations between “substitute factors” and poor loan performance. Many of those substitute factors have no direct logical connection to loan default or are viewed out of context, in stark contrast with such factors as capital and capacity. Consequently, when we experience significant change, such as the current economic downturn, FICO scores become irrelevant (i.e. what used to be predictive no longer is).

1. Current risk assessment models are inaccurate

a. Failure to consider all relevant factors simultaneously
b. Over-reliance on past performance

2. Modeling approach is unrealistic

a. Purely data driven and overly relies on quantitative methods
b. A black box and hard to interpret
c. One size fits all
d. Risk rating is performed prior to segmentation

3. Models are static with limited life

a. They become less accurate over time
b. Model re-development and model validation processes are very tme-consuming

1. A far broader range of outcomes being considered
2. A transparent process that captures the business reality for borrowers, lenders, and investors
3. An adaptive system that actually becomes more predictive over time

1. The notion that the FICO score captures 40% of the information value for a credit applicant in determining the likelihood that they will pay as agreed on their loan. As a consequence, a lot of weight may be put on the FICO score for risk-based pricing whereby a customer who normally pays cash for his/her car and who does not maintain a credit balance on thier credit card may be charged more than someone who uses credit to the extreme and lives on thre edge--but has not failed to make a payment yet.

2. The notion that a ten percent increase in the payment-to-income ratio above some pre-determine threshold will make an applicant twice as likely to default on their car loan. Would you think a higher payment-to-income ratio would affect a borrower who lives paycheck-to-paycheck with little savings more than a borrower who has little other debt and a high savings rate?

Our comprehensive credit assessment framework (CCAF) perfectly fulfills this need in three key areas, namely:

1. The new law seeks to ensure that consumers have, and understand, information that will help them make their financial decisions.

--> CCAF’s transparency, and its ability to portray all primary factor ratings for consumers with a single, interpretable number, is precisely what’s needed to satisfy this requirement.

2. Further, there is some additional emphasis on ensuring fair lending laws are doing the job they were intended to do, especially relative to the means by which consumers are classified and qualified for a loan.

--> Proper classification lies at the heart of that issue of fairly qualifying consumers for loans prior to determining the riskiness of the transaction. Our new lending framework would automatically identify similarly situated borrowers across protected classes, e.g. specific minority groups, women, and so on, and would facilitate comparisons relative to loan denials, above threshold pricing, market penetration, and other measures that characterize lending practices.

3. Finally, providing the underserved consumer segment greater access to financial services and products is another end goal of the legislation.

--> CCAF can help on that front as well, since it leverages alternative data to qualify borrowers who fall outside of the mainstream and it avoids the “one size fits all” aspect of credit scoring by conditionally assessing each borrower and deciding what factors are most relevant in a particular situation and what, if any, secondary factors need to be considered in order to render a final decision and, if approved, appropriately price the loan.