How can data quality and governance assist with counterparty exposure?

Liberty data breach – setting a precedent for GDPR?
Liberty data breach – setting a precedent for GDPR?
Gary Allemann, MD at Master Data Management.
Gary Allemann, MD at Master Data Management.

Financial institutions have become so complex and the products they sell have developed at such an accelerated pace that their internal risk infrastructure has been unable to keep up. In such tumultuous economic conditions, counter party risk management (where the customer does not live up to their contractual obligations such as payments by the due date) and exposure has become an incredible challenge for financial institutions.

It is here that having a product-focused, rather than customer-focused approach to data management fails to provide an understanding of the total exposure to an individual client, industry or region. Data that is high-quality and readily-available is necessary to help financial services companies effectively extend credit within their own reserve limits in order to minimise the risk of catastrophic bad debt, but this is only possible with clearly-defined data quality and data governance principles and procedures in place.

Hard lessons

The 2007 Global Financial Crisis highlighted the shortfalls of risk assessment and management systems used by financial institutions, a lesson driven home by the devastating failure of the Lehman Brothers financial services firm that lead to the largest bankruptcy filing due to bad debt seen in the U.S to date, with Lehman holding over $600 billion in assets. Closer to home and notorious for its aggressive strategy in the unsecured loans space, the collapse of African Bank was caused in part by its inability to predict the impact of the global financial crisis on the mining sector due to a lack of clearly-defined information policies within a data-driven credit risk management framework. It is believed that there were possibly several data-related warnings that were missed and had this not been the case, such warnings could have prevented the downfall of the bank if caught in time. So, the question remains – what went wrong? Inadequate industry diversification within the bank’s portfolio was exacerbated by a lack of clearly-defined information policies, which was impacted by insufficient reporting and stress testing and then topped off with an inadequate credit policy to culminate in the inability of customers to repay loans, which ultimately lead to the bank’s downfall.

While banking regulations require all financial institutions to maintain a minimum capital reserve, bad debt and over-exposure to a particular client or industry are risks for every business. Such risks can only only be measured through accurate client data analysis. This is problematic if a product-focused approach is taken when capturing customer information (and debt) across numerous product systems and business areas. Inconsistent data is a result of inconsistent processes across business units and the risks of over-exposure. Inefficient cash utilisation and under-provision experienced by financial institutions are a result of an inability to accurately calculate risk exposure for each customer. South African companies are faced with an additional risk, that of non-compliance with the National Credit Act, which is intended to penalise companies that provide additional debt to consumers that are already overburdened. The establishment of clearing houses, credit insurance and credit vetting bureaus, while intended to deal with these risks have had little success. This is because these measures fail to address the heart of the challenge: the single customer view.

Data quality and governance

Without an accurate single view of the customer, companies struggle to calculate their total credit exposure to each individual. Poor quality of data relating to customers is attributable to the data originating from multiple sources, as well as inconsistent availability due to incongruent systems, and the difficulty experienced in collecting and aggregating data as a result. It is only when there is a clear agreed-upon policy for defining and capturing customer data that it becomes possible to conceptualise the single customer view, as customer data can be easily aggregated and consolidated to provide a centralised understanding of total credit risk. Not only highlighting risk, having a single customer view can also highlight opportunities to increase share of wallet, where appropriate.

With clear policies in place, data quality can then help to link related parties (be it the same individual, household or company) even where data is inconsistent and ensures uniformity in how information is aggregated and reported. With a unified master data management platform, it becomes possible to deliver data that is readily available and of high quality to help financial companies to extend their credit realistically within their limits while at the same time diminishing the risk of bad debt.


By Gary Allemann, MD at Master Data Management