Manage Risk More Effectively

January 9th, 2017

risk-management-1-9-17.pngIn today’s connected, interrelated world of finance, it’s hard for bankers to see all the complex relationships between different groups of customers—perhaps some are subsidiaries of larger firms, guarantors of third-party loans, or investors in the same funds. Whatever their nature, these hidden links can multiply the risk of lending by exposing you to more risk than you’re prepared to assume.

One of the missing links that worsened the 2008 financial crisis was the inability of financial institutions to accurately connect exposures to the responsible entity. In some cases, the same entity was recorded twice in the system under different names. In some cases, the ownership or credit relationship among entities was not defined, and in others, manual data entry errors distorted the entity identity or its hierarchical relationships.

A uniquely defined entity aids in risk management by helping banks know who is carrying risk for them and allows organizations to capture operational efficiencies. In a sense, banks build their entire organizations around entities. For instance, a retail and commercial bank will have separate business divisions that look after different groups. Once these entities are known and structured within their hierarchies and groups, banking organizations apply risk calculations along these hierarchies to get an accurate view of the risk contribution of an entity. Good entity management confers a host of benefits on the bank:

Improved Operational Efficiency
Being able to construct a full view of the entity rather than seeing it from the perspective of a single account could deliver substantial cost reductions by helping banks avoid large scale duplication in the recording and maintaining of customer data. Creating an entity record in the system involves manually inputting entity details such as entity name, country of operations, tax ID number, chief executive and so on. Moreover, this information has to be updated on a regular basis, exposing the records to greater manual errors.

Accurate Risk Aggregation
When aggregating limits for risk appetite calculations, banks need to make sure that the appropriate entities’ data is included in the calculations to avoid misrepresentation, undercounting, or even double-counting. Entity-to-entity and facility-to-entity risk aggregation calculations used to allocate risk to the correct owner depend on this unique entity definition.

Counterparty Risk Management
Collateral and guarantees are risk mitigants that help reduce the credit risk of a particular borrowing transaction with an entity. This is achieved primarily by offering the bank an alternative or secondary source of repayment should the borrowing entity be unable to pay back a loan by itself. Looking at the entire deal structuring process, identifying who owns the collateral and who is providing the guarantee becomes critical for effective risk mitigation.

Entity Risk Grades
Typically, company financial statements are important inputs to the calculation of an entity risk grade, which in turn is used to calculate capital allocation against loans made to entities. Hence, it’s important to ensure that the correct financials for the entity are being used. In larger organizations, entities are linked together in a complex hierarchical relationship with intertwined risk. These situations may mean that the entire group shares common risk, resulting in the risk grade of one entity being distributed to other entities across the hierarchy.

Data Privacy and Security
From a regulatory perspective, banks have to demonstrate the integrity of their data, showing that no unauthorized person has access to the data or an opportunity to change it. In cases of sensitive, restricted deals, banks have to ensure that the access of any employee outside the deal team is prohibited. In other words, banks need a system where they can manage user access to entities along with the actions those users can take on those entities.

Demonstrating Regulatory Compliance
Know-your-customer regulations are in effect in all advanced economies and require that banks identify every customer to satisfy anti-money laundering rules, sanctions, fraud and other financial crime measures. The Basel Committee on Banking Supervision (BCBS) regulations also drive demand for identification. Leverage, liquidity and many other ratios calculated under different Basel regimes assume that the banks have properly identified entities.

Reporting on Transactions
Banks are required to prove that their records are accurate even when the actual borrower may be buried under a complex web of entity relationships and hierarchies. The principles for effective risk data aggregation and risk reporting are set out in BCBS 239, which requires accurate, true and clean data broken down along several dimensions. A unique entity identifier stored within the database makes it possible to query and report at the required level of granularity.

For more on this topic, see our white paper.


Jill Coppersmith is senior director at Moody’s Analytics.


Anju Govil is director at Moody’s Analytics.