The Benefits of Modernizing the Commercial Credit Decisioning Process

Commercial bankers understand that granting a loan is an iterative and dynamic process, not a distinct event with a simple “yes” or “no” outcome. It involves many data inputs and outputs, as well as examination of risk and revenue tradeoffs. A facility often evolves substantially before finalization.

Traditionally, a “loan file” was essentially closed once a bank finalized the facility, made the credit decision, and released the funds. Bank credit policies typically required an annual review, at which time the bank would update the borrower rating and close the loan file for another year. Periodically – and often haphazardly – the bank’s staff checked the compliance status of the loan covenants (or, being overwhelmed, ignored them). Banks rarely placed enough clean, consistent, and quality data in a searchable system to determine covenant compliance without having to manually reopen a credit file.

Today, the data from the loan decisioning process for complex commercial credit facilities is still rarely aggregated in a searchable, reportable, and auditable system – even at sophisticated banks. Instead, this data is manually loaded into Excel or Word documents from various source systems and left in flat files where it can’t be re-used for more critical processes, such as stress testing, covenant monitoring, or model validation.

The data on a typical commercial loan decision document comes from many areas, including customer relationship management (CRM), core systems, deposit and exposure systems, financial statement spreading systems, and scoring systems. This aggregated information is frequently used only to facilitate credit committee decisions and is not conveniently stored in one system. Banks thus lose invaluable opportunities to repurpose a rich dataset for meaningful activities that could ultimately increase revenues and greatly lower compliance and audit costs.

The recent financial crisis revealed that some banks did not electronically store data from the credit decisioning process and lacked systems to track covenant compliance. In addition, regulatory expectations for data retention, storage, and reporting have grown considerably – indeed, both regulators and auditors are increasingly requiring that banks capture and store all key data points and collateral information associated with making a commercial loan decision. Antiquated and standalone systems no longer meet these demands, much less optimize revenues.

The questions regulators ask banks might seem easy, but experienced bankers know that they can be difficult to answer, owing to the limitations of their systems. Among some of the simple but challenging regulatory and audit questions are the following:

• How many loans are guaranteed by the same guarantor, for example, by a high net-worth individual or real estate developer?
• How many loans comply with covenants?
• How many do not?
• What credit decisioning data does the bank have for model validation?
• What is the bank’s direct and indirect exposure to a given customer or financial institution?
• Can the bank stress the inputs to its rating models for enterprise stress testing?
• Can the bank recreate its rationale for a commercial loan decision?
• How many loans are related to a specific customer?
• Why does the bank have multiple spreads or credit files for a given customer? Which one is correct?

Answering these questions is challenging, but improving the credit decisioning process will make it easier and will also provide banks a number of benefits, among them:

• Faster loan approvals, which will increase the bank’s loan closure rate and throughput
• Automated covenant monitoring and reporting
• Lower regulatory compliance costs
• Ability to re-use origination data for stress testing and model validation
• More consistent underwriting and better return on risk

We will examine each of these benefits in detail, as well as some of the challenges that banks may encounter when switching their systems.

Loan approvals often face bottlenecks, whether from multiple approval requirements because deals exceed credit authority or a key credit officer is on vacation. Modern credit decisioning systems can assign approvals to the appropriate credit officer and reroute requests when resources are out of the office.

Bankers can use online technology to improve the speed and accuracy of their loan decision making process, earning them more business. Credit teams can add new approvers on the fly (or the system can do this automatically) and establish a “service level,” or a schedule that outlines when the tasks required in the credit process have to be completed. For example, if the service level for financial statement spreading and analysis is four hours, the deal team can expect turnaround within that time span. Service levels can be tracked to identify bottlenecks and improve productivity.

These and other process efficiencies can shorten the cycle for credit decisioning. If a complex commercial credit decisioning process can be cut from 22 to 12 days, productivity and profitability will increase dramatically, which could boost loan throughput significantly.

Figure 1. Decision cycle length for large commericial loans: before and after modernization

Source: Moody's Analytics

Regulators and auditors have expanded their scrutiny of covenant monitoring and reporting. For example, regulators are increasingly issuing Matters Requiring Attention (MRAs) to commercial banks to improve systematic monitoring and compliance reporting for both financial and non-financial covenants.

An efficient credit decisioning process will automatically capture covenants at the point of credit underwriting and monitor them throughout the life of the loan. Banks can select from a library of standard covenants or customize them for a customer’s specific risk attributes. Integration with the spreading process can automatically test financial covenants when a borrower’s monthly, quarterly, or annual financial statements are analyzed. Portfolio- or business line-level reports can automatically identify customers who do not meet covenant requirements. Banks can track the entire covenant resolution process – granting customers a grace period, giving them the opportunity to cure the covenant, and monitoring the cure periods – so that they can determine how to improve or expedite the process.

Moreover, having this data and history at hand provides banks another powerful benefit: They can more quickly adjust their credit policies to eliminate covenants that do not result in a meaningful reduction of risk. Imagine a credit policy with fewer but more effective loan covenants!

Historically, most banks stored credit decision data and documents in an unstructured or even imaged process. Given that regulators now require more information about the rationale and all of the data involved in a commercial lending decision, however, these systems are inadequate and unable to deliver the information in the format required.

Most underwriting and decisioning systems were designed for a single purpose – credit decisioning – and not for submitting data in bulk to the regulator. This has led to banks’ hiring dozens of people to cut, paste, and audit underwriting data for consistency and de-duplication as they prepare information for submission to regulators.

We know of one leading commercial bank that employs 20 full time workers to aggregate and clean data in preparation for the FR Y-14Q quarterly commercial data submission. What if the bank had a clean, aggregated, and reliable source of data? Both regulatory compliance costs and data error rates would decline, and the bank would be able to more meaningfully deploy resources to more profitable tasks.

One essential element required for both stress testing and model validation is good, clean, standardized data. A modern credit decisioning system can deliver this data in droves to stress testing and quantitative teams, which can then build and update more powerful and relevant models.

Banks can use data validation rules in the origination process so underwriters don’t mistakenly enter incorrect data. The data from credit decisioning can be used to build bottom-up or top-down stress testing models. Historical data on a borrower’s financial status, loan performance, and other key data elements can be quickly exported to model development environments to build more relevant models. This lowers the costs of ad hoc data requests from quantitative teams because the system provides the crucial data to the modeling teams on the back end.

A modern credit decisioning system can help a bank identify trends, giving it a competitive edge and boosting its commercial portfolio profits. Readily accessible portfolio-level reporting can help banks spot performance trends in borrowers’ financials and also regional or industry trends.

With reliable underwriting and loan performance data and an online decisioning system, a bank can segment its portfolio by region or industry and quickly analyze data and identify bright spots in the market. For example, during the financial crisis, many banks abandoned commercial real estate owing to the housing market crash; medical office loans, however, performed well – throughout the crisis. Banks can adjust their industry and regional portfolio composition to beat the competition in promising market segments.

Many banks assume that an enterprise data warehouse will help with the creation of an online decisioning system. Despite heavy investments in data warehouse solutions over the last 10-15 years, however, these projects have achieved only a checkered success rate. Many were too ambitious in scope, attempting to place virtually all the bank customers’ data into a warehouse. Although these projects may have succeeded in their main objective of consolidating data, they did not always succeed in delivering reportable aggregated information to commercial business leaders and risk managers. One of our customers calls its bank data warehouse the “data landfill”; still others think of their data warehouses as graveyards for data that is doomed never to be seen again.

Fortunately, new reporting tools and more focused “datamarts” have evolved to fill the voids left by the large data warehouses. Datamarts and data warehouses differ in a few key attributes. Data warehouses are quite expansive, containing data from dozens to sometimes hundreds of systems, while datamarts are highly specific means of aggregating and validating data for a specific purpose.

Datamarts also typically incorporate processes to clean and validate data. Rules can be built to automatically kick out data that doesn’t meet specified criteria. For example, an exposure aggregation process may have to be put into common currency through an automated data aggregation and validation process before it enters the datamart.

The other major difference between a datamart and data warehouse is the reporting capability. Because datamarts are purpose-built, standardized reports and user-friendly reporting tools can be leveraged to deliver the meaningful information users demand from the system.

Banks should utilize a data warehouse as a source of data feeding a purpose-built datamart. This will enable more informative reporting supporting business decisions.

A modern, more powerful on-line credit decisioning process can help improve a bank’s commercial portfolio performance, no matter the economic condition, through a combination of increased revenue, process efficiency, and lower regulatory compliance costs.