Kannan Subramanian R - Event- and Data-Centric Enterprise Risk-Adjusted Return Management: A Banking Practitioner’s Handbook

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Banks are financial intermediaries. The four key intermediation functions, as stated by the Bank for International Settlements, are credit, maturity, liquidity, and collateral transformations. In playing the role of a financial intermediary, banks are exposed to market, credit, liquidity, and operational risks. Risk taking is an inseparable part of banking, and the impact of a risk can be more than the banks risk appetite for it, or in stressed situations, more than its risk capacity. Risk events can result in a direct loss or constrain the banks ability to seize business opportunities, or could lead to the collapse of the institution in a crisis situation.

The definition of risk has changed over the last several decades. From being defined in terms of the probability of a loss occurring, and managing the impact of uncertainty in business, it has evolved into a forward-looking, enterprise risk-adjusted return management capability that helps a bank manage adverse internal and external events. Banks are finding it difficult to manage their businesses efficiently because they are constrained by their fragmented business and technical architectures. The fragmentation has created a complex banking operating environment that hinders business growth.

Even as a new breed of tech-savvy, non-banking companies have emerged as competitors, large banks face the challenge of working with a knackered infrastructure that has burst at its seams. The new entrants are demonstrating their capability in leveraging technology and improving the customer experience.

Siloed risk management refers to the risk-management approach in which the perceived dominating risk type of a financial contract drives the risk-mitigation process. For instance, risk management in treasury contracts is primarily focused on market risk, as the characteristics of the contract are sensitive to market rates and prices. However, while several contract types have a credit risk exposure as well, market and credit risks are not managed simultaneously, even if the risks are known. Some of the risks are managed in a downstream application at a different point in time. In the business of lending, credit risk is the dominant factor. For instance, some banks treat floating-rate loans as being without market risk. However, the probability of default could increase with a rise in interest rates, as the borrowers obligation increases. Hence, the actual probability of default is underestimated in some situations. In most banks, market and credit risks are managed without considering their impact on liquidity. Commercial banks rely on retail deposits to support their asset growth. Increased competition and unpredictable customer behavior have increased the rate sensitivity of retail deposits. Because of this, banks use alternative sources of funding, such as from the wholesale and brokered markets. However, these exposures carry more rate and liquidity sensitivity risks than do traditional retail deposits. Further, an increasing number of banks offer asset and liability products with embedded optionality, on both sides of the balance sheet. This has made cash-flow management more challenging, and the liquidity risk-management mechanism has not kept pace with these changes and their associated complexities.

By taking an incremental approach to automating their growing business requirements, many banks are increasing the complexity of their operations. They are not resolving the known risks and limitations in their current operating environment. The weakness is predominantly in their enterprise architecture and data governance. Basels recent recommendations on the Fundamental Review of the Trading Book, Interest Rate Risk in the Banking Book, Intraday Liquidity Management, Liquidity Coverage Ratio, and Net Stable Funding Ratio have a significant impact on data and enterprise architecture. These requirements make it imperative for a bank to take a holistic, enterprise view of their risk-adjusted returns. It is an opportune moment for banks to transform their operations into agile enterprise models that will allow them to stay relevant and competitive.

Enterprise data ontology is an approach that provides the banking industry and relevant stakeholders with a common understanding of the business terms. It provides a framework for conceptual enterprise risk-return modeling (ERRM) that leverages advanced analytics and knowledge management. The four important dimensions of ontology-based ERRM systems are metamodels, procedural knowledge, temporal relations, and knowledge acquisition. These components are consistent with the inherent nature of the quantitative modeling of risk-adjusted returns in commercial banks. The purpose of semantic technology is to uncover meaning within data, which is a pre-requisite for unlocking its value. Initiatives such as the Financial Industry Business Ontology (FIBO) of the EDM Council Inc. are paving the way for the implementation of ontology-driven systems.

In most banks, data has been a by-product of their product-oriented approach toward computerization in the Electronic Data Processing (EDP) era. This book puts forth the reasons why enterprise architecture and enterprise data governance should be considered as pre-requisite capabilities for enterprise risk-adjusted return management. Banks are making a paradigm shift in their approach to managing their enterprise architecture so as to improve their return on investment in technology. This is driven by changes in the economy, business models, customer expectations, business delivery channels, and risk-management requirements.