That analysis can be filtered by a variety of factors:

• Loan Origination Date. If, for example, you feel that loans originated in the year 2005 and 2006 were underwritten more aggressively, you can filter the report to include only loans originated in 2005 and 2006 so you can better understand how the ratings of these loans have changed.
• Collateral Type. How have the ratings of loans collateralized by a particular collateral type changed over time? You can filter reports by one or more collateral types.
• Geographic Market or Region. Some geographic regions or markets may have experienced greater weakness than others so it will be important to understand how the ratings for loans from these locations have changed over time.
• Other Filter Categories. The Madison commercial loan software allows you to filter by many other loan characteristics so that you can best understand how ratings have migrated.
• Combine filters. Filters can be combined for greater depth of analysis. For example, you can evaluate loans in a particular market(s) that were originated in 2005 and 2006. Or loans collateralized by (a) office properties (b) in a particular market(s) (c) that were originated in a particular year(s).

 For presentation purposes, the Madison commercial loan software provides a summary matrix that displays percentages, a summary matrix that displays dollar amounts, and a detailed matrix that shows the performance of each loan.

To help manage this process the Madison commercial loan software provides a convenient checklist feature.

• First, you may specify an unlimited number to checklist types such as checklists for various loan types, or collateral types, or loan process steps.
• Second, for each checklist type you may specify the default list of tasks that users will see when they open the checklist.
• Third, you may allow (or not allow) users to add or delete checklist tasks for a particular loan so that the tasks are just what needs to be completed for that loan.
• Fourth, when tasks are completed, users can check them off as being completed, enter the completion date, and enter any comments that are appropriate.
• Fifth, to enable a tickler report, users can identify a future completion date and it will appear on a tickler report. The tickler report list tasks by date and highlights in red tasks that have passed their completion date and in yellow for tasks that are within 7 days of completion.

The check list feature can be shared by all users on a view only or edit basis, as appropriate for each user. You no longer need to call and play phone tag or send emails to find out the status of task for each loan. Instead, users can go to the checklist reports or checklist module and see the status of each loan.

1. 1. Comprehensive portfolio risk reporting. That is, an automated process for preparing standard risk management reports such as watch lists, delinquency reports, stress test reports, trend analysis, missing or out dated information, etc.
2. The ability to slice and dice the portfolio — the ability to quickly and easily prepare reports that evaluate the portfolio based on factors such as loan type, collateral type, loan officer, region or market area, and numerous other parameters.
3. Portfolio Stress testing — the ability to stress items such as collateral income, collateral value, borrower income and expenses, increases in interest rates, and other factors to determine whether loans are strong enough to withstand the stressed conditions.
4. Loan Rating Migration — how have loan grades changed over time. Is the distribution of ratings over time is an important measure of the credit quality of the portfolio as seem by bank management, and has important implications for the allowance for loan losses.
5. Automation of the Loan Review process. In the past Loan Review Departments captured the results of their analysis on paper forms that made it difficult to then prepare reports that summarize the results of their analysis of a portfolio. Hand entry of data is also inefficient since much of the information about the loans is already in the bank’s core system.

The Madison System includes comprehensive support for these and many other features requested by regulators. The trick for banks is to be able to create the necessary information, analysis and reports with a minimum of effort so. A fully automated system for doing so is often the best solution.

 In each case, the Loan Review Department may wish to have reports that document the review of each loan that contain information about the loan as it existed at the time the sample was selected rather than current information. Madison addresses this by creating a snapshot of data as of the date that the sample was selected. That way loan review reports can contain information about the loan as of the date the sample was selected, or as of the current date, or both.  This feature affords greater flexibility in documenting and saving the findings of each review.

First, if loans in the import file could not be imported, Madison provides a list of each loan, collateral or borrower that could not be imported along with the reason why it was not imported so that user can easily identify and correct import errors. When that step is done, totals in Madison should be the same as totals in the core.

Second, but that may not always be the case. The import file may not have included a loan or two that should have been in the file. Or some loans that should have been marked as paid off may not have been marked. Issues such as this give rise to reconciliation differences.

To assist in identifying the source of the difference it is useful to present lists and subtotals of loans in Madison that match similar lists and subtotals in the core. For example, most core systems have groupings of loan types that may be called class codes, minors, or other terms that signify a very granular grouping of loans. Madison often has these values in the loan category field so that reports can be grouped by loan category and then the subtotals compared to comparable subtotals in the core to discover the source of the difference. Since there is often a much small number of loans in these grouping it is much easier to identify the source of the difference.

Their first effort is to simply identify when loans will reprice or mature with indications of:

• The loan balance that will reprice,
• The current rate and spread,
• The index on which the loan reprices, and
• The current coverage ratios.

With this information in hand, clients are able to identify situations where the coverage is thin and will be significantly adversely affected by higher rates. They can also identify any concentration of repricing in future time periods  –  for example, do a lot of loans reprice in three years?

Once the repricing schedule is available, analytics can be applied to it to automate measuring the impact on future coverage ratios.

• The current balance can be amortized down to the repricing date to better estimate the impact of higher rates on coverage.
• Potential exposure from utilization of available lines can be measure.
• Interest rate projections can be established, future loan payments can be recalculated, and stressed coverage ratios can be presented.
• Projected coverage ratios that are below a user specified threshold can be identified for further analysis.

While all the information necessary to perform this analysis is resident in most core systems, the ability to create reports that present the information and perform the analytics is more difficult to achieve. Systems that easily provide this information to clients allow them to better measure this component of credit risk.

With commercial lending, many hands will touch a loan from origination through closing and on to portfolio administration and risk management. In many cases, each set of hands looks at a loan from its own perspective associated with its business function. The information that each set of users wants to see about a loan will be different and the work or analysis that they perform will differ.

Consequently it is not uncommon for each set of hands to rekey information about the loan into a Word document or Excel spreadsheet or Access database. Each time the same information is rekeyed, efficiency is lost.

And when one set of users wants to see information that has been assembled by another user or group of users, the information is requested often by email or a voice mail, with hours or days lost as one person finds time to pass on information to another person. Time lost further reduces efficiency.

The ability to provide effective reporting is diminished when little information resides in a central database so the time is devoted to pulling together information from many sources to support reporting requirements and portfolio risk management analysis.

Operating with information in a central, shared database can have a significant positive impact on productivity. Information can be accessed more quickly, reports prepared more efficiently, and risk management analysis performed that could not have been performed if data were scattered throughout the organization. Productivity can be further improved by metrics that display the time required to complete each step in the lending process and productivity measures such volume per person.

 Probably the single largest risk category is Credit Risk, which is addressed by the Madison System. But, while credit risk may be the largest single category of risk, effective ERM needs to take an institution wide perspective to achieve the best balance between risk and opportunity. There are trade-offs between risk and profit. In the case of loans, higher risk can garner higher margins but expose the lender to higher levels of risk. And Interest Rate Risk strategies may point you toward certain lending strategies so the credit risk implications need to be evaluated along with any operational risk implications.

 A well rounded ERM system will rely on systems such as the Madison System, and the others such as those mentioned above, to assist in the process of measuring the risk and the profit, as well as assisting in managing risk on a going forward basis.

Stress testing the commercial loan portfolio has been an important function for many years. Increasingly, we find clients are stress testing more granular subsets of the total portfolio. Here are some of the criteria we find clients using. See if any would make sense for you. The easier it is to select the desired sub group the more likely you are to conduct more granular analysis, and the less staff time is required to get the desired result.

 Department and Broad Portfolio Types. These are very broad categories such as commercial real estate (CRE) loans, C&I loans, construction loans, asset based lending, etc. In many cases there is a lot of overlap between departments and portfolio types. Not only would the appropriate stress values differ, but the particular ratios that you many want to test will differ. For Example, you may want to stress LTV and DSCR for CRE loans but DSCR and loan interest rates for C&I.

 Geographic Criteria. Different markets and geographic areas may have substantially different economic conditions, some may be strong while others quite weak. Geographic criteria would include region, state, market or city.

 Collateral or Loan Type/Loan Category. Needless to say, different collateral and loan types exhibit quite different risk characteristics.

 Loan Programs.  Loan programs may be directed at different customer groups and/or have different terms and structures that merit separate analysis.

 Origination Period. Loans originated in periods when underwriting criteria were especially aggressive may exhibit heightened risk.

 Participation Loans. Purchased participations were originated by another lender, so even though your organization also reviewed the underwriting of the loans, servicing will be by another lender and they will have more direct contact with borrowers and performance information. And Participations sold, while underwritten and serviced by your organization, may be larger implying worse consequences if they go bad but they may also have been underwritten against more rigorous criteria. Either way, the ability to look at participation loans separately from the rest of the portfolio is valuable.

 Rate Reset Dates and Indexes. The particular characteristics of floating rate loans give rise to risks associated with higher loan payments if interest rates increase. The ability to easily identify situations where variable rate loans may reprice upward and evaluate the resulting potential DSCR is also valuable.

Overview: There are many ways to measure the productivity of the loan origination process. One dimension is the time that is required to complete each step in the process. This information can assist in identifying bottlenecks so situations can be resolved before they become problems.

For example, the report can group loans by loan origination program so you can quickly and easily identify programs that take longer to complete than they should, or steps in origination process for those programs that do not meet your timing standards.

Or information can be grouped by loan officer to see if some are able to close loans faster than others. Maybe the “experts” can give some guidance to those that take longer to close their loans.

The report allows a senior lending officer to easily stay on to of the effeciency of the entire loan origination process so that management iniatives can be implemented where needed. Since time is money, this report provides valuable insight into the origination process.