Effective credit risk management depends on data aggregate across multiple silos and the ability to produce accurate, consistent and forward-looking measures of credit exposure at the single obligor level. These data silos are often inevitable, especially
when multiple front office trading systems, ETRMs, etc. are present, and yet must be overcome by middle and back office functions. Some take the approach of mirroring the upstream balkanisation into their own data and processes, supported with spreadsheets,
tactical applications and an ecosystem of manual processes (hopefully with one wary eye on operational risk!).
Credit Risk managers within small, simple firms often take such an approach, establishing a fragmented set of limits, and relying on manual processes and spreadsheets to create a rudimentary single obligor view.
However, for more complex firms trading in multiple markets and asset classes, this type of approach is value destroying at best and potentially dangerous at worst. Consequences include but are not limited to:
- Lost revenues: as the firm grows and adds new business lines, the finite credit risk appetite is increasingly salami sliced, leading to a large number of limits, many of which will be poorly utilised. Reallocating this stranded capacity to more active business
lines can be difficult (e.g. limit hoarding behaviour arises) and administratively expensive, with the result that profitable businesses are starved of revenue opportunities.
- Limit inflation: the reaction to limit hoarding is to over allocate limits beyond the normal risk appetite level. This protects revenues but now the firm has the potential to acquire unacceptable levels of credit risk.
- Exposure overestimation: measuring exposure in silos means it is not possible to capture even simple portfolio effects such as long-short offsetting positions, netting and time dimension on a walk-forward basis. Incorporating correlations and performing
true portfolio calculations are impossible. This exacerbates the limit inflation problem.
- Difficult to identify outsized exposure concentrations.
- Missing exposures as a result of failed manual reporting processes.
- Inability to run coherent stress tests.
- Failure to identify secondary exposure concentrations through risk shifting effects of guarantees, letters of credit and credit insurance.
- No capability to identify contingent market and liquidity risks caused triggered by counterparty default. This includes clustering of toxic contract terms such as downgrade or other termination event.
- Difficult to see the holistic picture of the overall credit relationship and any concerning patterns of behaviour; late payments, missed collateral calls, failed security settlements, etc.
- Inefficient allocation of capital.
- High administrative costs.
Approaches to estimating the cost of these negative consequences is beyond the scope of this article and best practice firms employ a spectrum of methodologies to build a business case for investing in an enterprise wide credit risk solution. To be effective,
such a solution needs to be truly multi-asset class, and capable of interfacing to a wide variety of trading and other source systems to gather critical data and create a firm-wide risk data store. With this critical step accomplished, a mature credit risk
management system must include functionality for:
- Managing static and client reference data
- Limit and exposure management
- Financial spreading and scoring
- Sophisticated exposure measurement including PFE and stress testing
- Collateral management
- Early warning, monitoring and controls
- Reporting, visualisation and dashboards
- Workflows tools for modelling and automating business processes
As a footnote, the challenge for small, simple firms is to recognise when they have reached the inflexion point of complexity. This is similar to the “boiling frog” fable, which illustrates the challenge of identifying threats that arise gradually. Unfortunately,
some firms don’t detect the rising water temperature soon enough…