The Model Muddle

Many financial institutions (FIs) find themselves wondering how they became mired in a credit model muddle that is dragging down their profitability. They want to know how they can get out of this swamp.

Traditionally, FIs heavily relied on economic capital models to support business decision-making. Then, with the financial crisis precipitating a slew of new regulations, FIs were forced to lean away from economic capital models to right-size the capital requirements imposed upon them. Today, the divide between economic and regulatory capital is larger than it has ever been.

In response to regulatory capital requirements, FIs now seek to invest in businesses with lower regulatory capital commitments, clustering their investments into similar businesses that promise to deliver a higher ROE. When many FIs abandon their core strengths to focus on such investments, systemic risk builds, profit margins narrow, and fresh concerns arise as FIs enter somewhat unfamiliar territory.

Consequently, maximising shareholder value has become a direct function of efficiency improvements and optimal resource management. Assessing and perfecting the risk model execution framework is a key part of that objective.

Most large FIs use the internal ratings based (IRB) approach in an effort to optimise capital. However, developing custom-built IRB credit risk models is complex. Hence, many FIs have been forced to invest heavily in the services of third-party consultancy firms that essentially monopolise a niche – developing and maintaining these models.

FIs pay a price. Initial and recurring investments costs are high. But because the model execution process is effectively a black box, they also suffer negative impacts on decision-making due to lack of transparency.

Thus, the model muddle in which many FIs find themselves!

To escape the mire, FIs today can consider a fresh, unconventional approach: building a credit risk model framework using open-source language and integrating it seamlessly with regulatory reporting requirements. This approach provides the FI with in-house control, reduces cost commitments and gives them much needed visibility into the process.

With model ownership transferred to an in-house team, FIs can easily scrutinise techniques and approaches in finer detail, thereby improving governance. With this level of transparency, comes the ability to develop more bespoke, complex, and refined models for myriad, ever changing capital reporting requirements. Building with open-source language means that third-party consultancies become redundant. No longer is there the need to sustain year-on-year licensing costs for their platform, upgrades, or patches.

Credit risk models form the foundation of risk reporting and are the very core of regulatory requirements such as counterparty credit risk (CCR) and IFRS 9. By adopting this innovative, transparent approach, FIs:

  • Reduce total cost of ownership (TCO)
  • Maximise shareholder value
  • Strengthen traceability and governance processes
  • Satisfy regulatory scrutiny through enhanced controls
  • Improve time to market

Further, improved data lineage not only fosters development of valuable, actionable in-house knowledge but also underpins BCBS 239 compliance.

IRB and credit risk models built using open-source language and managed internally provide the missing link that makes it possible to seamlessly create an end-to-end risk reporting process. By adopting this unconventional approach, FIs can leave the model muddle behind.

This article is originally published on RegTech Insight.