By: Mike Horrocks This week, several key financial institutions will be submitting their “living wills” to Washington as part of the Dodd-Frank legislation.  I have some empathy for how those institutions will feel as they submit these living wills.  I don’t think that anyone would say writing a living will is fun.  I remember when my wife and I felt compelled to have one in place as we realized that we did not want to have any questions unanswered for our family. For those not familiar with the concept of the living will, I thought I would first look at the more widely known medical description.   The Mayo Clinic describes living wills as follows, “Living wills and other advance directives describe your preferences regarding treatment if you're faced with a serious accident or illness. These legal documents speak for you when you're not able to speak for yourself — for instance, if you're in a coma.”   Now imagine a bank in a coma. I appreciate the fact that these living wills are taking place, but pulling back my business law books, I seem to recall that one of the benefits of a corporation versus say a sole proprietorship is that the corporation can basically be immortal or even eternal.  In fact the Dictionary.com reference calls out that a corporation has “a continuous existence independent of the existences of its members”.  So now imagine a bank eternally in a coma. Now, I cannot avoid all of those unexpected risks that will come up in my personal life, like an act of God, that may put me into a coma and invoke my living will, but I can do things voluntarily to make sure that I don’t visit the Emergency Room any time soon.  I can exercise, eat right, control my stress and other healthy steps and in fact I meet with a health coach to monitor and track these things. Banks can take those same steps too.  They can stay operationally fit, lend right, and monitor the stress in their portfolios.   They can have their health plans in place and have a personal trainer to help them stay fit (and maybe even push them to levels of fitness they did not think they could reach).  Now imagine a fit, strong bank. So as printers churn, inboxes get filled, and regulators read through thousands of pages of bank living wills, let’s think of the gym coach, or personal trainer that pushed us to improve and think about how we can be healthy and fit and avoid the not so pleasant alternatives of addressing a financial coma.

By: Joel Pruis From a score perspective we have established the high level standards/reporting that will be needed to stay on top of the resulting decisions.  But there is a lot of further detail that should be considered and further segmentation that must be developed or maintained. Auto Decisioning A common misperception around auto-decisioning and the use of scorecards is that it is an all or nothing proposition.  More specifically, if you use scorecards, you have to make the decision entirely based upon the score.  That is simply not the case.  I have done consulting after a decisioning strategy based upon this misperception and the results are not pretty.  Overall, the highest percentage for auto-decisioning that I have witnessed has been in the 25 – 30% range.  The emphasis is on the “segment”.  The segments is typically the lower dollar requests, say $50,000 or less, and is not the percentage across the entire application population.  This leads into the discussion around the various segments and the decisioning strategy around each segment. One other comment around auto-decisioning.  The definition related to this blog is the systematic decision without human intervention.  I have heard comments such as “competitors are auto-decisioning up to $1,000,000”.  The reality around such comments is that the institution is granting loan authority to an individual to approve an application should it meet the particular financial ratios and other criteria.  The human intervention comes from verifying that the information has been captured correctly and that the financial ratios make sense related to the final result.  The last statement is the key to the disqualification of “auto-decisioning”.  The individual is given the responsibility to ensure data quality and to ensure nothing else is odd or might disqualify the application from approval or declination.  Once a human eye is looking at an application, judgment comes into the picture and we introduce the potential for inconsistencies and or extension of time to render the decision.  Auto-decisioning is just that “Automatic”.  It is a yes/no decision and is based upon objective factors that if met, allow the decision to be made.  Other factors, if not included in the decision strategy, are not included. So, my fellow credit professionals, should you hear someone say they are auto-decisioning a high percent of their applications or a high dollar amount for an application, challenge, question and dig deeper.  Treat it like the fishing story “I caught a fish THIS BIG”. No financials segment The highest volume of applications and the lowest total dollar production area of any business banking/small business product set.  We had discussed the use of financials in the prior blog around application requirements so I will not repeat that discussion here.  Our focus will be on the  decisioning of these applications.  Using score and application characteristics as the primary data source, this segment is the optimal segment for auto-decisioning.  Speeds the  decision process and provides the greatest amount of consistency in the decisions rendered.  Two key areas for this segment are risk premiums and scorecard validations. The risk premium is important as you are going to accept a higher level of losses for the sake of efficiencies in the underwriting/processing of the application.  The end result is lower operational costs, relatively higher credit losses but the end yield on this segment meets the required, yet practical, thresholds for return. The one thing that I will repeat from a prior blog is that you may request financials after the initial review but the frequency should be low and should also be monitored.  The request of financials should not be the “belt and suspenders” approach.  If you know what the financials are likely to show, then don’t request them.  They are unnecessary.  You are probably right and the collection of the financials will only serve to elongate the response time, frustrate everyone involved in the process and not change the expected results. Financials segment The relatively lower unit volume but the higher dollar volume segment.  Likely this segment will have no auto-decisioning as the review of financials typically will mandate the judgmental review.  From an operational perspective, these are high dollar and thus the manual review does not push this segment into a losing proposition.  From a potential operational lift perspective, the ability to drive a higher volume of applications into auto-decisioning is simply not available as we are talking probably less than 40% (if not fewer) of all applications in this segment. In this segment, the consistency becomes more difficult as the underwriter tends to want to put his/her own approach on the deal.  Standardization of the analysis approach (at least initially) is critical for this segment.  Consistency in the underwriting and the various criteria allows for greater analysis to determine where issues are developing or where we are realizing the greatest success.  My recommended approach is to standardize (via automation in the origination platform) the various calculations in a manner that will generate the most conservative approach.  Bluntly put, my approach was to attempt to make the deal as ugly as possible and if it still passed the various criteria, no additional work was needed nor was there any need for detailed explanation around how I justified the deal/request.  Only if it did not meet the criteria using the most conservative approach would I need to do any work and only if it was truly going to make a difference. Basic characteristics in this segment include – business cash flow, personal debt to income, global cash flow and leverage.  Others may be added but on a case by case basis. What about the score?  If I am doing so much judgmental underwriting, why calculate the score in this segment?  In a nutshell, to act as the risk rating methodology for the portfolio approach. Even with the judgmental approach, we do not want to fall into the trap thinking we are going to be able to adequately monitor this segment in a proactive fashion to justify the risk rating at any point in time after the loan is booked.  We have been focusing on the origination process in this blog series but I need to point out that since we are not going to be doing a significant amount of financial statement monitoring in the small business segment, we need to begin to move away from the 1 – 8 (or 9 or 10 or whatever) risk rating method for the small business segment.  We cannot be granular enough with this rating system nor can we constantly stay on top of what may be changing risk levels related to the individual clients.  But I am going to save the portfolio management area for a future blog. Regardless of the segment, please keep in mind that we need to be able to access the full detail of the information that is being captured during the origination process along with the subsequent payment performance.  As you are capturing the data, keep in mind, the abilities to Access this data for purposes of analysis Connect the data from origination to the payment performance data to effectively validate the scorecard and my underwriting/decisioning strategies Dive into the details to find the root cause of the performance problem or success The topic of decisioning strategies is broad so please let me know if you have any specific topics that you would like addressed or questions that we might be able to post for responses from the industry.

Recently we released a white paper that emphasizes the need for better, more granular indicators of local home-market conditions and borrower home equity, with a very interesting new finding on leading indicators in local-area credit statistics.  Click here to download the white paper Home-equity indicators with new credit data methods for improved mortgage risk analytics Experian white paper, April 2012 In the run-up to the U.S. housing downturn and financial crisis, perhaps the greatest single risk-management shortfall was poorly predicted home prices and borrower home equity. This paper describes new improvements in housing market indicators derived from local-area credit and real-estate information. True housing markets are very local, and until recently, local real-estate data have not been systematically available and interpreted for broad use in modeling and analytics. Local-area credit data, similarly, is relatively new, and its potential for new indicators of housing market conditions is studied here in Experian’s Premier Aggregated Credit Statistics.SM Several examples provide insights into home-equity indicators for improved mortgage models, predictions, strategies, and combined LTV measurement. The paper finds that for existing mortgages evaluated with current combined LTV and borrower credit score, local-area credit statistics are an even stronger add-on default predictor than borrower credit attributes. Click here to download the white paper Authors: John Straka and Chuck Robida, Experian Michael Sklarz, Collateral Analytics  

By: Mike Horrocks This week, several key financial institutions will be submitting their “living wills” to Washington as part of the Dodd-Frank legislation.  I have some empathy for how those institutions will feel as they submit these living wills.  I don’t think that anyone would say writing a living will is fun.  I remember when my wife and I felt compelled to have one in place as we realized that we did not want to have any questions unanswered for our family. For those not familiar with the concept of the living will, I thought I would first look at the more widely known medical description.   The Mayo Clinic describes living wills as follows, “Living wills and other advance directives describe your preferences regarding treatment if you're faced with a serious accident or illness. These legal documents speak for you when you're not able to speak for yourself — for instance, if you're in a coma.”   Now imagine a bank in a coma. I appreciate the fact that these living wills are taking place, but pulling back my business law books, I seem to recall that one of the benefits of a corporation versus say a sole proprietorship is that the corporation can basically be immortal or even eternal.  In fact the Dictionary.com reference calls out that a corporation has “a continuous existence independent of the existences of its members”.  So now imagine a bank eternally in a coma. Now, I cannot avoid all of those unexpected risks that will come up in my personal life, like an act of God, that may put me into a coma and invoke my living will, but I can do things voluntarily to make sure that I don’t visit the Emergency Room any time soon.  I can exercise, eat right, control my stress and other healthy steps and in fact I meet with a health coach to monitor and track these things. Banks can take those same steps too.  They can stay operationally fit, lend right, and monitor the stress in their portfolios.   They can have their health plans in place and have a personal trainer to help them stay fit (and maybe even push them to levels of fitness they did not think they could reach).  Now imagine a fit, strong bank. So as printers churn, inboxes get filled, and regulators read through thousands of pages of bank living wills, let’s think of the gym coach, or personal trainer that pushed us to improve and think about how we can be healthy and fit and avoid the not so pleasant alternatives of addressing a financial coma.

By: Joel Pruis From a score perspective we have established the high level standards/reporting that will be needed to stay on top of the resulting decisions.  But there is a lot of further detail that should be considered and further segmentation that must be developed or maintained. Auto Decisioning A common misperception around auto-decisioning and the use of scorecards is that it is an all or nothing proposition.  More specifically, if you use scorecards, you have to make the decision entirely based upon the score.  That is simply not the case.  I have done consulting after a decisioning strategy based upon this misperception and the results are not pretty.  Overall, the highest percentage for auto-decisioning that I have witnessed has been in the 25 – 30% range.  The emphasis is on the “segment”.  The segments is typically the lower dollar requests, say $50,000 or less, and is not the percentage across the entire application population.  This leads into the discussion around the various segments and the decisioning strategy around each segment. One other comment around auto-decisioning.  The definition related to this blog is the systematic decision without human intervention.  I have heard comments such as “competitors are auto-decisioning up to $1,000,000”.  The reality around such comments is that the institution is granting loan authority to an individual to approve an application should it meet the particular financial ratios and other criteria.  The human intervention comes from verifying that the information has been captured correctly and that the financial ratios make sense related to the final result.  The last statement is the key to the disqualification of “auto-decisioning”.  The individual is given the responsibility to ensure data quality and to ensure nothing else is odd or might disqualify the application from approval or declination.  Once a human eye is looking at an application, judgment comes into the picture and we introduce the potential for inconsistencies and or extension of time to render the decision.  Auto-decisioning is just that “Automatic”.  It is a yes/no decision and is based upon objective factors that if met, allow the decision to be made.  Other factors, if not included in the decision strategy, are not included. So, my fellow credit professionals, should you hear someone say they are auto-decisioning a high percent of their applications or a high dollar amount for an application, challenge, question and dig deeper.  Treat it like the fishing story “I caught a fish THIS BIG”. No financials segment The highest volume of applications and the lowest total dollar production area of any business banking/small business product set.  We had discussed the use of financials in the prior blog around application requirements so I will not repeat that discussion here.  Our focus will be on the  decisioning of these applications.  Using score and application characteristics as the primary data source, this segment is the optimal segment for auto-decisioning.  Speeds the  decision process and provides the greatest amount of consistency in the decisions rendered.  Two key areas for this segment are risk premiums and scorecard validations. The risk premium is important as you are going to accept a higher level of losses for the sake of efficiencies in the underwriting/processing of the application.  The end result is lower operational costs, relatively higher credit losses but the end yield on this segment meets the required, yet practical, thresholds for return. The one thing that I will repeat from a prior blog is that you may request financials after the initial review but the frequency should be low and should also be monitored.  The request of financials should not be the “belt and suspenders” approach.  If you know what the financials are likely to show, then don’t request them.  They are unnecessary.  You are probably right and the collection of the financials will only serve to elongate the response time, frustrate everyone involved in the process and not change the expected results. Financials segment The relatively lower unit volume but the higher dollar volume segment.  Likely this segment will have no auto-decisioning as the review of financials typically will mandate the judgmental review.  From an operational perspective, these are high dollar and thus the manual review does not push this segment into a losing proposition.  From a potential operational lift perspective, the ability to drive a higher volume of applications into auto-decisioning is simply not available as we are talking probably less than 40% (if not fewer) of all applications in this segment. In this segment, the consistency becomes more difficult as the underwriter tends to want to put his/her own approach on the deal.  Standardization of the analysis approach (at least initially) is critical for this segment.  Consistency in the underwriting and the various criteria allows for greater analysis to determine where issues are developing or where we are realizing the greatest success.  My recommended approach is to standardize (via automation in the origination platform) the various calculations in a manner that will generate the most conservative approach.  Bluntly put, my approach was to attempt to make the deal as ugly as possible and if it still passed the various criteria, no additional work was needed nor was there any need for detailed explanation around how I justified the deal/request.  Only if it did not meet the criteria using the most conservative approach would I need to do any work and only if it was truly going to make a difference. Basic characteristics in this segment include – business cash flow, personal debt to income, global cash flow and leverage.  Others may be added but on a case by case basis. What about the score?  If I am doing so much judgmental underwriting, why calculate the score in this segment?  In a nutshell, to act as the risk rating methodology for the portfolio approach. Even with the judgmental approach, we do not want to fall into the trap thinking we are going to be able to adequately monitor this segment in a proactive fashion to justify the risk rating at any point in time after the loan is booked.  We have been focusing on the origination process in this blog series but I need to point out that since we are not going to be doing a significant amount of financial statement monitoring in the small business segment, we need to begin to move away from the 1 – 8 (or 9 or 10 or whatever) risk rating method for the small business segment.  We cannot be granular enough with this rating system nor can we constantly stay on top of what may be changing risk levels related to the individual clients.  But I am going to save the portfolio management area for a future blog. Regardless of the segment, please keep in mind that we need to be able to access the full detail of the information that is being captured during the origination process along with the subsequent payment performance.  As you are capturing the data, keep in mind, the abilities to Access this data for purposes of analysis Connect the data from origination to the payment performance data to effectively validate the scorecard and my underwriting/decisioning strategies Dive into the details to find the root cause of the performance problem or success The topic of decisioning strategies is broad so please let me know if you have any specific topics that you would like addressed or questions that we might be able to post for responses from the industry.

Recently we released a white paper that emphasizes the need for better, more granular indicators of local home-market conditions and borrower home equity, with a very interesting new finding on leading indicators in local-area credit statistics.  Click here to download the white paper Home-equity indicators with new credit data methods for improved mortgage risk analytics Experian white paper, April 2012 In the run-up to the U.S. housing downturn and financial crisis, perhaps the greatest single risk-management shortfall was poorly predicted home prices and borrower home equity. This paper describes new improvements in housing market indicators derived from local-area credit and real-estate information. True housing markets are very local, and until recently, local real-estate data have not been systematically available and interpreted for broad use in modeling and analytics. Local-area credit data, similarly, is relatively new, and its potential for new indicators of housing market conditions is studied here in Experian’s Premier Aggregated Credit Statistics.SM Several examples provide insights into home-equity indicators for improved mortgage models, predictions, strategies, and combined LTV measurement. The paper finds that for existing mortgages evaluated with current combined LTV and borrower credit score, local-area credit statistics are an even stronger add-on default predictor than borrower credit attributes. Click here to download the white paper Authors: John Straka and Chuck Robida, Experian Michael Sklarz, Collateral Analytics