Banks must act on early warning systems or risk ROE downturn

Banks have a limited window of opportunity to adapt and respond to the emerging scenarios, says a latest report ‘Banks must act on their early warning systems or risk return on equity (ROE) downturn, jointly authored by Galytix and PwC


Sandhya D'Mello

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Published: Sat 11 Jun 2022, 11:43 PM

Last updated: Sat 11 Jun 2022, 11:47 PM

Are early warning systems (EWS) to assess credit risks critical to maximise corporate bank returns and shareholder value? Certainly yes! but how many follow them?

Evolving external forces are creating many risks for corporate banks globally. Corporate banks are now witnessing the end of an era. Following the 2008 financial crisis, banks rebuilt capital and invested in technology to strengthen their relationships with clients through improved efficiencies.

Macro factors like inflationary pressures, pandemics, frequent climate change-related events, and heightened geopolitical risk have the potential to disrupt supply chains across industries and impact businesses in unexpected ways.

Industry faces new era

The banking industry now faces a new era that will challenge banks’ ability to future-proof their risks and exposures without having any historical evidence or data on how to predict or tackle the risk. Banks can no longer depend on their current EWS using backward-looking indicators with high false positives, to protect them from future risks. Previous methods and data sources for assessing credit risk or identifying distress signals are quickly becoming out of date.

Banks have a limited window of opportunity to adapt and respond to the emerging scenarios, says a latest report ‘Banks must act on their early warning systems or risk return on equity (ROE) downturn, jointly authored by Raj Abrol, CEO, Galytix; Rupak Ghose, head of EW, Galytix; Symon Dawson, partner, PwC; Matt Moran, Partner, PwC and Natasha Rakova, director, PwC; and Roxane Haas, partner, PwC.

The report shares insights that although large systematic shocks are unpredictable, it is clear that the speed at which banks are able to react to these events can have a huge effect on the ability to actively manage the lending book and reduce any negative impact.

The same can be said about the ability to have early warnings of more common “everyday” market movements and events too. Banks are struggling to generate returns in their corporate lending books. Galytix estimates that ROEs on a standalone basis without the support of investment and transaction banking business are between 3-6 per cent.

Traditional EWS falters

The experience of both Galytix and PwC is that currently most early warning indicators that banks produce could be meaningless. Given the surge in false positives, several manual data checks are implemented by banks to ensure consistency, effectiveness, and accuracy of signals – which ultimately increases the cost and time of managing the early signal detection process.

The ECB has also been critical of the use of adhoc manual triggers being implemented by banks and the need for a more systematic approach to alert monitoring. For many banks, existing EWS frameworks are based on more easily available and traditionally used data sources including client financials and market data which is readily available. However, such indicators are usually backward-looking and fail to predict corporate defaults well enough.

“An effective EWS should identify borrowers at risk of non-performance (High Hit Ratio), distress or default sometime before an actual event (Time before Default). It must enable efficient and reliable assignment of borrowers to different watch-list categories and trigger other actions and escalation scenarios depending on the nature and severity of risk. The system must use indicators that are derived by combining both traditional and non-traditional data sources (internal and external) using a multivariate or decision tree modelling approach”, says the report.

Financial statements alone are no longer enough for risk managers to assess credit worthiness. Banks that fail to improve their EWS will also face significant regulatory pressures. The European Central Bank (ECB) has highlighted the huge variation in the quality of early warning systems and how credit assessment at a micro as well as macro-level is core to risk management and provisioning. Furthermore, banks are also losing their competitive positioning to bonds and non-bank lenders, in many countries.

Galytix estimates that in the US more than three-quarters of corporate financing comes from these sources. This trend is less pronounced in Continental European countries, where bank lending continues to dominate.

Upgrading EWS are crucial for corporate banks to drive their competitive advantage and improve returns. This needs to be embedded across the credit chain from loan origination to fulfilment to risk monitoring.

The strategic case for developing and implementing an EWS is clear: effective risk monitoring will lower both credit losses and capital requirements – directly improving a bank’s ROE by over 20 per cent. Experience shows that an effective EWS could help reduce loan loss provisions by 10-20  per cent and the required regulatory capital by up to 10  per cent. Moreover, an effective EWS will also maximise shareholder value by materially reducing the volatility of corporate bank earnings. This will underpin higher stock market valuation multiples.

Road ahead

A LEGO (Leverage, External Indicators, Governance, and Ontology) framework implemented in an AI-driven pipeline architecture-based solution can accelerate EWS quality and effectiveness.

This framework involves streaming real-time data and analytics, making adjustments to current indicators, adding a few high-impact ones, and providing a systematic and automated capability to manage the EWS without creating unnecessary burdens. These processes allow credit risk managers to quickly assess the exposures of counterparties.

Both PwC and Galytix believe that expanding the list of early warning indicators must be focused on the highest impact external data sources around equity market signals, governance, fraud, aggressive accounting, or cyber risks. There is a lot of interest in sentiment analysis amongst both financial investors and banks. —

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