bg-arrow-down icon-arrow-up icon-back-to-top icon-linkedin icon-menu icon-search icon-twitter logo-white slider-arrow-left-gray slider-arrow-left slider-arrow-right-gray slider-arrow-right

Improving Commercial Loan Pricing

Pressured for profitable growth, commercial lending units will need an improved analytic framework to support the field negotiations of relationship managers.

Commercial lending has been a mainstay for the banking industry following the Great Recession, providing badly needed growth and profits at a time when retail and residential lending stalled. As measured by the FDIC, commercial and industrial (C&I) loans grew by nearly $400 billion, or 34%, during the four years ended September 30, 2013, while consumer loans grew by only $3 billion, or 0.2%.

But commercial lending teams are not resting easy following this timely expansion. As household-related lending remains stuck in low gear, banks are trying to wring still more returns from the commercial customer base. But balance growth is being increasingly offset by falling margins, reflecting intense competition in an industry that remains overloaded with deposits and deprived of the broad-based loan expansion typically seen in prior eras of economic recovery.

The squeeze has raised pressing questions about commercial loan pricing and how to realize the most value from each new credit origination, whether directly or from relationship cross-sell. For all but the largest and smallest commercial loans, pricing is seen as much more of an art than a science, largely based on the individual negotiations of relationship managers with clients in the field.

Inconsistencies abound in the typical book of business, with considerable leakage, or lost revenue opportunity. There is a compelling need for an improved negotiating framework that: 1) identifies profitable tradeoffs in spreads, fees and terms; 2) protects risk-adjusted returns; and 3) captures upside potential in instances of strong demand.

To be sure, relationship managers will continue in their leadership role, negotiating credit facilities and managing the overall commercial relationship. But for consistently optimized pricing, the commercial bank will need to support RMs with an expert system based on a systematic evaluation of markets, customers and RM practices. Based on Novantas research, many commercial banks could reasonably aspire for an improvement of 10 to 15 basis points in operating margin, simply by establishing a more systematic and analytic-based context for RM negotiations in the field.

Holding the Line

While commercial banks long have contended with inconsistency as part of a negotiated pricing model, this issue is moving off the back burner in an intensifying competitive environment. Spreads have retreated by roughly 75 basis points in the “borrower’s market” of the last year or so, with loan covenants and conditions easing up as well.

Fig_1_Commercial_Loan_Pricing_drivers_watermarkEven more market pressure is in store, but holding the line on loan pricing is no easy task for commercial lending executives. To begin with, commercial loan pricing is largely decentralized (other than standardized loans for very small businesses), negotiated by various RMs and loan officers scattered across multiple territories and industries.

Second, pricing is opaque. There are no published “rack rates” for commercial loans. And except for large syndicated loans, lenders operate in relative darkness as to deal trends, as reflected in current competitive offers and recently completed transactions. In fact, borrowers are the ones that often gain superior knowledge about market rates as they shop the same deal with multiple providers, capturing negotiating leverage with individual lenders that operate at an informational disadvantage.

Commercial loans are also more complex than other types of credit products (and certainly relative to deposits), which makes pricing all the more involved. Along with intricate connections to other relationship business, commercial facilities are highly varied in structure, reflecting a diverse customer base, making loan pricing a multi-dimensional task.

These factors, combined with competitive pressures and the internal push for growth, foster an environment where myriad slivers of profit opportunity are sacrificed to get deals onto the books. Customers wanting the rock bottom deal may additionally be given a fixed rate, for example, when a variable rate loan would be more appropriate at that price point. Credit issued at an advantaged rate at the start of a relationship may be inappropriately renewed at continued “giveaway” pricing. Promised fee-based business may never materialize, yet the client continues to enjoy discount loan pricing.

What is more, the performance dispersion among individual commercial lending RMs can be significant, as with other areas of decentralized, negotiated sales. In one instance, detailed portfolio research showed that top quartile RMs on average were realizing more than 100 basis points of additional loan spread over LIBOR compared with the bottom quartile — even after adjusting for “deal fundamentals” including loan, credit and borrower characteristics. Such skews cannot be addressed by broad initiatives to “improve pricing.”

Learning Curve

In reviewing the pricing performance options available to management, it is understandable that many executives would think first of the underwriting and origination basics, including variations in borrower profiles, credit grade and loan characteristics (type, fees, new or renewal, etc.). But our research suggests otherwise.

In repeated Novantas analyses of LIBOR-equivalent spread variance among multiple commercial loan portfolios, the deal fundamentals explained only about 25% to 30% of the dispersion (Figure 1: Commercial Loan Pricing Drivers). Equally surprising, while credit grade was the most powerful factor followed by key loan characteristics, the explanatory power of the statistical regression was not significantly increased by considering borrower-related traits such as geographic region, industry sector and company size.

That leaves about 70% to 75% of the spread variation subject to three broad influences not connected with the basics. These include competitive dynamics (the loan compression that RMs complain about); relationship concerns (spread adjustments for other customer business); and process issues (basically, pricing discipline). Again surprisingly, adding a spread compression variable to the regressions (using Federal Reserve data) still left more than two-thirds of the dispersion unexplained.

Over time, banks may be able to acquire additional market information that sheds greater light on competitive dynamics. But while the sources and uses of that information remain more limited, there are tangible near-term opportunities to improve relationship and process factors, which are key determinants in commercial loan pricing.

The complexity of commercial relationships makes it difficult for lenders to measure specific influences on pricing spread dispersion. Looking across the book of business, is it clear where spread has been sacrificed to win incremental non-credit business from the borrower? To what extent are results influenced by price elasticity of demand? How disciplined are RMs in ensuring incremental profit improvement for each piece of business, and tracking and landing expected incremental business used to justify spread reductions?

These questions point to the need for a more analytically driven management system that can track salient information and specifically link pricing variations with actions that can be influenced by management. This is a clear step beyond risk-adjusted return on capital (RAROC), the prevalent metric used in commercial loan pricing.

Though RAROC is an advance in pricing discipline, it has its drawbacks as well, both in concept and in implementation. To begin with, RAROC is an internal cost metric that narrowly considers the bank’s profit requirements — omitting the strength of market demand and the individual borrower’s sensitivity to price. In the field, RMs have tended to lead with offers based on fully-loaded profitability targets, but then retreat in negotiation to pricing levels that may only generate incremental profits, sometimes with little or no contribution to overhead.

In the emerging scheme, by contrast, RAROC models set only the floor for pricing. From this lower boundary, the negotiating range then extends to an upper boundary set by price elasticity of demand.

Upside Potential

In terms of next steps, the area of process discipline provides ample room for near-term revenue improvement. The leakage is widespread at the typical commercial bank, and a systematic effort is needed to identify and address the main issues (Figure 2: Evolution of Commercial Loan Pricing).

Fig_2_Evolution_of_Commercial_Loan_Pricing_watermarkOur research demonstrates there is much value to be gained from applying more rigorous analytic insight in negotiating and pricing the deal during the initial prescreen/preview process of origination. This also is the stage where RMs need more context for client negotiations, including easy access to successful deal structures and pricing; best in bank negotiating tactics; and competitor information.

As the commercial bank works to provide more guidance and support for relationship managers in negotiating credit deals, a variety of specific performance benefits can be captured. In particular, the improvements we have outlined can help:

  • Narrow the spread “guardrails” for a particular loan (given deal fundamentals);
  • Improve discipline in making exceptions to deal standards;
  • Ensure the consistent imposition and collection of loan fees;
  • Understand and replicate “best-in-bank” negotiating behaviors of RMs;
  • Adjust loan terms in ways that uphold profitability; and
  • Improve loan renewal spreads.

Along with upholding relationship profitability, an improved negotiating process should ultimately help the bank to capture more cross-sell opportunities, based on a better understanding of where pricing can be effective in winning profitable incremental business. Internally, new information will help in deciding how to deploy lenders to better performing industry sectors and regions.

Because the RM continues to be the front line for loan negotiation, success in leveraging improved analytics and process standards will depend on how well the insights can be translated to RM behavior. “Easy” works best from the RM perspective, and to that end, the progressive bank will want to consider how to package and deliver analytic insights to the RM desktop/tablet. This would include a simple, real-time display of current market pricing for similar loans; suggested pricing; alternative offers; and possibilities to revise terms while upholding deal economics.

Overall, our research indicates that the combination of deeper analytics and effective process changes can, depending on the lending unit’s goals, either lead to a 10 to 15 basis point improvement in yields on the same size book, or spark increased loan growth while upholding current spreads.

Opportunities to apply more analytic science in commercial loan pricing will help commercial lenders to defend spreads at this tough point in the lending cycle, and build competitive pricing advantage. By no means will this supplant the critical role of the relationship manager in negotiating rates and deepening relationships. Rather, superior analytics will better support RMs in the quest for consistent high performance.

Michael Rice is a Managing Director in the Chicago office and Lee Kyriacou is a Managing Director in the New York office of Novantas Inc., a management consultancy. They can be reached respectively at mrice@novantas.com and lkyriacou@novantas.com

For more information, contact Novantas Marketing

+1 (212) 953-4444