11 January 2019
Most banks lack a data model that exposes collateral data in a way that reveals its every nuance and connection. Trouble is, anything less inevitably results in a superficial understanding of the broader loan book – and of the true extent of credit risk at play.
Poetic license aside, any bank worth its salt will know the details of the collateral held against its loans, right? Well, that’s three shades of wrong right there.
Collateral blindness affects most banks, often without them realizing it. It impacts them in at least one of three common ways:
– Insufficient credit detail – Collateral data and the workflow tasks associated with putting a loan in place are not actively managed; assured completion of the loan process is impossible. Loan perfection is at risk.
These banks are in danger of incurring avoidable losses.
– Lack of visibility on what’s not there – Gaps, missing links, undocumented loan increases and out-of-date valuations go unreported or, worse, unnoticed.
These banks struggle with data integrity, under-valued LGDs and significant costs associated with manually finding and fixing gaps and correcting data.
– All is not as it seems – a piece of collateral may secure more than one loan – or a guarantor could cover multiple exposures. This is usually not systematically flagged.
These banks have a false sense of security; they could be exposed to unrecognized and unquantified risk, or possibly fraud.
It makes simple business sense to get a grip on all of the complex data held in the banking book and to manage it dynamically and with reference to the bank’s broader credit landscape.
By implementing an aggregation layer for banking book collateral, the appropriate, correct and complete information can be put into the right hands. All stakeholders can be satisfied – from operations to business development, to product portfolio management, to those responsible for compliance, capital adequacy and treasury.
Putting a lens on collateral data reveals data quality and completeness in granular detail, resolving information gaps and more comprehensively uncovering credit risk.
A data model that treats collateral in a holistic way and places it at the center of the credit ecosystem is the perfect lens to use for correct perception of collateral and of the broader credit landscape (see below).