Paul Whitmore, global head of counterparty risk solutions at Fitch Solutions, explains how qualitative data can add colour and insight to quantitative metrics for assessing the creditworthiness of counterparty banks
Banks can access data from a growing wealth of sources, but it is important to place that data in context when assessing the creditworthiness of counterparty banks. Current methods focus on quantitative metrics, but qualitative information is key to shedding light on organisational nuances. However, it is more difficult to identify and assess this type of information in a consistent and robust way.
What credit analysis methods do organisations typically use – and are they adequate?
A rating system used in some form or another by the majority of credit analysts is Camels, which comprises analysis of capital adequacy, asset quality, management quality, earnings, liquidity and sensitivity to market conditions. However, very little qualitative input – other than management quality – is considered under this approach, which prevents analysts from gaining a complete picture of an organisation. For example, a large bank could present positively in terms of quantitative metrics, but as balance sheets are backward-looking, some issues might only be detected through qualitative analysis.
Is there enough emphasis placed on qualitative data by credit analysts in general? Probably not, but sometimes this information is incredibly difficult to understand and interpret with regard to credit risk – as is understanding the influence it may have on the underlying credit risk of the institution being assessed.
Why is it important to incorporate more qualitative data – particularly in relation to assessing banks’ credit risk?
Quantitative models work very well for high-default asset classes where default data is plentiful, and this is an approach banks have been using for a long time. However, low-default markets such as the banking sector typically have much less default data available to prove out robust quantitative models.
You only have to look at the methodologies of rating agencies to see how much detail qualitative data can add to the credit rating assessment process. With Fitch Ratings, for example, the financial risk profile of the bank is just one of four pillars – or ‘risk dimensions’ – considered by the analysts. Within each of the ‘heads of analysis’, subcomponents are considered and scored by the ratings analyst, and all of these elements contribute to the overall viability rating.
Are banks finding it difficult to incorporate qualitative data into the credit risk analysis process at the moment?
Yes, they are. They understand the qualitative factors that should be considered, but the difficulty often lies in recognising the influence this has on overall creditworthiness. You can only truly determine this if a consistent approach or methodology has been used for a period of time, so long-term observations can be recorded. With rating agency methodologies, clear guidance and explanatory notes are published with regard to both the quantitative and qualitative aspects of their analysis. This is one of the reasons banks choose to align themselves with the methodologies used by rating agencies – they provide consistent, established approaches based on historical observations.
Why should a bank consider adjusting the emphasis placed on qualitative versus quantitative factors in the credit risk analysis process now?
There has always been an emphasis on due diligence, and this really goes above and beyond quantitative models. However, with the final reforms of Basel III coming into effect from January 1, 2022 – with particular emphasis on the treatment of unrated banks when it comes to to risk-weighted capital – banks’ due diligence of their counterparties is going to be firmly in the spotlight.