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Credit Risk Monitoring: Building Next Generation Early Warning Systems

When it comes to credit risk monitoring, many banks find themselves falling behind the regulatory expectations. Regulators are expecting banks to take a more proactive approach, using forward-looking data and deploying a broader set of indicators in their early warning systems (EWS).

The European Banking Authority (EBA) guidelines on loan origination and monitoring* calls for regular evaluation of relevant quantitative and qualitative early warning indicators, supported by an appropriate information technology and data infrastructure. The UK Prudential Regulation Authority’s (PRA) 2023 supervisory priorities**, gives clear indication of the need to move to a forward-looking EWS framework at banks “given many credit risk metrics are backward looking”.

To date, banks have relied on limited – often backward-looking and infrequently updated – datapoints such as quarterly earnings data and credit agency ratings. However, the emergence of big data and expansion of computing power is creating fertile ground for the next generation of EWS. The movement is also receiving impetus due to mounting prospects of a global recession, a shift in the credit cycle with rising interest rates and quantitative tightening.  

Moving from a traditional, passive EWS system to next generation, dynamic credit risk monitoring system requires a shift towards a systematic data driven approach with additional data including forward-looking data, a reduction in false positives through more evolved model-based triggers and continuous learning, and empathetic and contemporary UI/UX integrated with credit risk workflow.

Below is a summary of the attributes of modern EWS design versus a traditional system:

Five Benefits of Next Generation EWS

1. A systematic data-driven approach with the inclusion of forward-looking data

One of the key attributes of a modern EWS is that instead of using lagging, backward-looking data feeds only, it incorporates more dynamic, frequently updated variables that serve as leading indicators.

It also looks beyond traditional financial and industry data, and leverages a broader range of – both qualitative and quantitative - data sources including news  sentiment, ESG related metrics, alternative data sources such as website traffic and Google Trends data, and enhanced market metrics that can provide a more immediate and comprehensive picture. Such data points, when combined with internal bank data such as utilisation of credit lines, days past due, and margin call delays, enhance the efficacy of EWS, especially for small and medium counterparties.

2. Enhanced monitoring of private companies

A further constraint of traditional EWS is its limited efficacy in monitoring private companies. Since traditional systems rely mostly on share prices and ratings, this means a notable lack of sufficient triggers when it comes to private entities. Next generation EWS, based on a wider range of data, can cross this hurdle by establishing linkages between listed and private entities.

3. Continuous model management and governance

In terms of models, the modern EWS can leverage the above expanded set of variables to identify red flags, both through predictive models and enhanced rules-based approaches reflecting deep understanding of industry drivers  and KPIs. Further, EWS systems might tend to lean on the side of caution initially and generate a high number of false positives — that is, more red flags than necessary. A modern EWS tackles this challenge by leveraging data and using back-tested predictive models based on contemporary algorithms. Further, continuous model management and governance through a supervisory learning process run by credit specialists can help fine-tune models.

4. Better UI/UX & self-serve monitoring

Next generation EWS necessitates a move away from legacy applications and monitoring through offline means such as Excel. Instead, it needs to be powered by an intuitive user interface/user experience (UI/UX), which would serve as a front-end platform to consume analytics, insights and charts using the data and models. The interface would allow credit officers to monitor portfolios in real-time and take immediate action within the application itself.

5. Next generation EWS can provide a competitive edge

Regulatory requirements aside, banks with good credit-monitoring practices are proven to have higher risk-bearing capacity, offer better pricing, and generate higher returns on equity compared with peers.

A McKinsey & Co study said improving the effectiveness of monitoring reduces loan-loss provisions by 10-20% and  risk-weighted assets and regulatory capital by up to 10%. Such banks reduce unsecured exposures for customers on the watch list by about 60% within nine months, whereas average banks achieve only around 20% reductions.

* https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring

** https://www.bankofengland.co.uk/-/media/boe/files/prudential-regulation/letter/2023/uk-deposit-takers-2023-priorities.pdf

 

 

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This post is from a series of posts in the group:

Banking Strategy, Digital and Transformation

Latest thinking in respect to Banking Strategy, Digital and Transformation. Harnessing our collective wisdom to make banking better. Ambrish Parmar


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