Enhancing Credit Decisions with Risk Data

In striving for higher operational efficiencies and tighter alignment with regulatory compliance needs, lenders are seeking end-to-end origination solutions with a high degree of automation. As spreads remain tight for most lenders, ensuring a low cost of ownership and future longevity play critical roles in determining any investment in origination platforms. These platforms must support increasingly dispersed and complex business organizations.

The first step is to provide standard business functionality with configuration capabilities for different business lines, products, counterparty group structures, and legal entities. This helps to standardize data throughout the process and cuts costs by streamlining operations and eliminating redundant systems. Use of this approach alone, however, fails to consider the shift in focus of lending activities from purely credit decisions to a credit risk-based decision, and thus prevents credit managers from becoming risk intelligent.

As banks become more complex and diversified, aggregating information and total exposures for their borrowers also become increasingly complex. Pressed by the need to monitor and mitigate risks across boundaries – both for compliance and profitability targets, banks look to push through the risk strategy decisions made at head offices to their front lines, where loan applications and borrowers are routinely assessed. Positioning risk as the overseer and gatekeeper for every step of the origination process may afford credit and business teams with higher levels of efficiency, by ensuring that the right people make the right decisions at the right time.

In a 2013 CEB Tower Group poll of Commercial Banking, 79% of executives reported that it is critical or highly important to maximize their credit application, approval, and booking rates. A similar proportion reported that it is equally important to protect the credit portfolio from balance runoff and underutilization.¹

Commercial loan origination is comprised of several steps that require seamless transition and a built-in approval and monitoring mechanism. Segmenting the activities into three phases helps explain how incorporating additional risk data into each phase improves the transparency, accuracy, and timeliness of credit decisions. It also ensures that a loan portfolio is diversified in accordance with the level of risk undertaken.

Figure 1. Loan origination incorporating risk elements

Source: Moody's Analytics

Risk plays a critical component in the first phase of the origination process. A thorough examination of the existing and potential future borrower relationship, limits, and exposure is required not only from a sound business and economic perspective, but also to satisfy various regulatory guidelines (e.g., the Risk Data Aggregation principals as issued under BCBS 239). Introducing pre-qualification risk data such as pre-scoring, minimum acceptance criteria, and risk filters early in the process allows a bank to vet the borrower as meeting initial credit policy and regulatory acceptance criteria. Similarly, the incorporation of rating tools that require minimum spreading will further complete the borrower picture.

Enhancing risk helps to ensure compliance with the bank’s credit policy in terms of loan portfolio concentrations in, for example, particular industries, geographies, currencies, borrowers, or loan types.

As banks reinforce the organization’s risk policy on the front line, it is important they introduce risk data as early in the origination lifecycle as possible to confirm that each loan passing through each phase in the process is qualified. This will guarantee the accuracy of the sales pipeline, and reduce the cost and time of origination by ensuring that the maximum number of loans that start origination reach booking.

The origination, underwriting, and credit analysis steps provide ample opportunity for risk management to enhance the loan origination process. A traditional credit-based approach determines a borrower’s potential to default through financial analysis, and calculates risk metrics, such as debt service coverage. However, borrowers are interconnected through a maze of financial and legal relationships. A better approach would take into account the borrower hierarchy, relationships, exposure, covenants, financials, and risk metrics to aggregate total risk. Armed with this comprehensive view of the borrower risk profile, lending banks have the opportunity to streamline operations by routing different loans to different work queues (e.g., auto-decisions, level one decisions, credit committee decisions), while still understanding the risk implications of each new loan.

Banks further need to adopt pricing mechanisms that allow them to realize competitive advantages and avoid disadvantages based on the credit policy of the bank. Portfolio risk-based pricing enables banks to price their loans by taking into account the loan portfolio concentration and diversification strategy they would like to achieve. In the highly competitive loan market with tight margins, portfolio risk-based pricing would also help financial institutions acquire new customers from targeted sectors at competitive rates other banks could not afford. The banks’ risk-diversified portfolio composition would ensure that the new transactions meet minimum performance metrics like risk-adjusted return on capital (RAROC) and Sharpe Ratio at both transaction and customer levels.

In Phase 3, banks analyze and verify the credit and risk data gathered throughout the origination process. At this stage, banks must reinforce their understanding of the proposed risk in terms of subordination, covenants, collateral, and off-balance sheet liabilities. Collateral valuation and allocation must be monitored throughout the life of an exposure and any insufficiency immediately alerted. Similarly, ongoing exposure management and limit checking must ensure that the proposed structure is in line with the overall portfolio allowable limit structure and notify of potential breaches at any level. Automated tracking processes must be natively linked to financial statements to detect early warning signs about the borrower. Covenant conditions that have been defined during the origination process must be included in loan documentation and checked against new facilities. Credit decisions must be transparent and auditable at all stages.

Enabling risk alerts throughout the end-to-end origination process will allow rejection indicators early in the process, which saves banks time and money. Overlaying risk alerts also secures a higher conversion rate of successful opportunities progressing through the origination process.

This article discussed how incorporating risk data elements improves the origination workflow by providing a higher degree of accuracy in decisions, better assessment of total risk exposures, improved compliance, and greater control over pricing – both from a data consolidation and monitoring perspective.

As banks become more complex and global, aggregating global information and total exposures for their borrowers becomes increasingly challenging. Banks must monitor and mitigate risks across boundaries to meet compliance and profitability targets. They look to push risk strategy decisions made at their headquarters into locations where loan applications and borrowers are routinely assessed. A risk-based approach to credit decisions provides this capability.

“We’re seeing a shift from focusing on cost reduction to improving efficiencies and better portfolio and risk management,” confirms Joanne Pollitt, Practice Manager CEB TowerGroup Commercial Banking.²

Many banks rightly invest in origination system consolidation and upgrades in order to reap the cost and other benefits discussed in this article. By overlaying risk as the gatekeeper and overseer of the loan origination process, these banks can make the right decisions at the right time.