Want more Insights from SAS? Subscribe to our Insights newsletter. Or check back often to get more insights on the topics you care about, including analytics, big data, data management, marketing, risk and fraud.
Where to focus when lending is up (but so are delinquencies)
Credit risk management is the answer
By Roger Lang, Principal Product Marketing Consultant, SAS
Banks are refocusing on their core business of lending and loan volume is back up to pre-crisis levels. But delinquencies are higher as well, so improving credit risk management is critical.
With a resurgence in commercial bank lending since 2014, banks are back to quarterly lending of approximately $105 billion – levels not seen since 2008.
In the 2015 Bank of America Annual Report, Chairman and CEO Brian Moynihan described growth in several areas:
- Core loan balances up $75 billion.
- $10.7 billion in new credit to small business.
- Millions of new credit cards.
- $70 billion in residential home loans.
This back to the core strategy is echoed in Europe as well, however, increased lending has been accompanied by an increasing rate in the number of delinquent commercial and industrial loans. In his September 2016 Fed Dashboard & Fundamentals economics report, Brian Barnier from ValueBridge Advisors observed delinquencies in 2Q2016 were 1.6 percent, up from 0.6 percent in 1Q2015, while in Europe delinquencies of commercial and industrial loans are also at historical highs.
As noted by Gabriel David, Senior Director, BurntOak Capital Ltd. (an advisor to central banks) delinquencies are partially connected to the current increase in loan volume and may also be linked to external factors in the economy and financial markets such as historically impaired assets and systemic economic issues.
While higher delinquency rates are caused by factors banks either anticipated or could not have foreseen, it is clear that the rise in lending has been accompanied by increasing rates of delinquency.
Detailed data that resided within each region and line of business must be made available for analysis at the corporate level to achieve an enterprise view of risk and optimize the allocation of capital across the balance sheet.
Implications for credit portfolio risk management
With increased focus on the core business of lending and a continuing rise in delinquency, banks need better credit risk management processes and analytics. They need analytics for underwriting, decisioning and workflow to optimize and govern the origination and processing of loans. For loans that become delinquent, banks need better models to project potential losses and determine how best to allocate collection resources. Currently, a number of middle- and back-office processes associated with credit portfolio management– from data aggregation to the use of spreadsheets for analysis – are time consuming, costly and difficult to scale to meet increasing loan volume. In addition to these manual processes, there is significant additional expense to meet regulatory requirements. Therefore, the process of managing risk in credit loan portfolios must be streamlined and highly automated to remove as much cost as possible from each step.
Any solution to this challenge will have three major components:
- The ability to aggregate data and support decisioning across lines of business and geographies.
- A platform for credit model risk management.
- The capability to run multiple stress tests on demand.
The data challenge
Credit risk managers need to take an enterprise view of risk across their portfolios and they need information to support credit decisioning. To accomplish this, they must collect and analyze data across lines of business and geographies. Detailed data that resided within each region and line of business must be made available for analysis at the corporate level to achieve an enterprise view of risk and optimize the allocation of capital across the balance sheet. In an environment of increasing risk and pressure on margins, credit risk professionals must automate workflow and streamline the management of much larger volumes of data for analysis and reporting.
Model risk management
Credit risk modeling across the loan portfolio – from commercial to small business loans and mortgages – is starting to look more like the evolving techniques for retail credit scoring. This involves the use of more complex scenario-based models that analyze large sets of both traditional and unstructured data.
Credit risk modeling needs to be integrated within automated and streamlined workflows, from credit origination to analysis, reporting, servicing and collections. Automation of modeling also serves to reduce costly and manually intensive processes.
As models become more numerous, more complex and extend across the enterprise, governance of model inventory needs to be automated and centrally managed. Model governance is more than simply putting in lines of defense and oversight. It includes software assurance and tuning throughout the model lifecycle. Beyond addressing the critical business need for a well-orchestrated credit modeling process, model governance is also a regulatory requirement with specific guidance subject to audit (by the Federal Reserve under SR 11-7 and increasingly viewed as a best practice by banking regulators in other parts of the world including the EU).
An important business benefit of streamlined systems and the automation of models includes the ability to run more timely stress tests, enabling management to react to events in near-real time and address new risks as soon as possible once they are identified. In their 2015 JPMorgan Chase annual report, CEO Jamie Dimon described the ability to run thousands of stress tests as a key capability linked to improved returns on capital and immunization of the balance sheet.
A path forward
Credit portfolio managers generally agree that they are unable to implement the procedures, controls and credit models they need to manage risk cost effectively across disparate silos without the right tools. The amount of data to be analyzed has increased exponentially. Advances in software and technology, combined with enhanced credit risk analytics, offer the tools credit portfolio managers need to analyze large, detailed data sets from traditional and unstructured data sources. With this capability, they will be better positioned to address the increasing risk of default as banks refocus their strategy on lending and increase the size of their loan portfolios.
The path forward for credit portfolio managers is one of continuous improvement in streamlining costs, reducing risk and optimizing the allocation of capital. It’s the only pathway to maintain profitability in an increasingly regulated environment.
Free white paper
- The Changing Landscape for Credit Risk Management examines how new credit portfolio strategies are changing how banks develop and use credit risk models to incorporate enhanced data management and high-performance computing.