Fixing Business Processes with Basel III and Other Emerging Risk Management Regulations

The most heavily regulated industry?

Because of the critical role it plays in the economy, the banking sector has always been the most regulated in any country. One need only look at any major economic crisis over the past century to see that the banking industry has always been at the heart of such events. In fact, non-banking laws governing the business community such as data protection and privacy are often first applied on the financial services industry before they are propagated to other sectors of the economy.

Banks are unlikely to escape the crosshairs any time soon if the emergence of new regulation such as Basel III and the Dodd Frank Act are anything to go by. And they have little choice but to comply with the new laws. As a business, to risk losing their banking license, incurring hefty penalties and having a dent on their reputation is not an option. There are also the business risks that go with non-compliance e.g. not having sufficient capital to withstand major shocks. Ultimately, the objective of risk management regulation such as Basel III is to protect both banks and their customers.

Killing Two Birds with a Single Stone

But compliance costs money. The expenses range from hiring external consultants that will guide the bank through the process, to purchasing new infrastructure and upgrading existing systems. The eventual cost and impact depends on the disparity between the new framework and its predecessor. Note however that just because such expense will go toward compliance with regulators does not mean it will circumvent the routine internal process for budget approval.

As such, senior executives, risk managers and line managers should ensure that the process of complying with the rigorous requirements outlined in Basel III is also used as an opportunity to address process inefficiencies, guard against major risks and raise the institution’s overall competitive advantage.

For instance, the Basel III standard primarily addresses liquidity risk, capital adequacy and stress testing. It requires banks to change how they compute liquidity and leverage ratios. The logical result of the new framework is that financial institutions have to integrate all relevant data sources and develop a new approach to data analysis and modeling. Basel III demands more transparency and greater documentation from banks than ever before.

Banks must thus create models that ensure compliance. The more savvy institutions however are slowly recognizing that the same processes, project teams, infrastructure investment and data warehouse models that would be necessary to prepare for Basel III compliance can be harnessed to simultaneously improve business processes and ensure the organization runs more effectively. In other words, compliance with Basel III is not mutually exclusive with developing the infrastructure necessary for the bank to sharpen its capacity to identify and respond to profit making opportunities.

In fact, in today’s heavily technology-dependent banking industry, both regulatory compliance and smarter, more efficient business decision making, are at their core, data-driven. Any bank that can correctly position its data warehouse and technology infrastructure stands to gain from both compliance and better business efficiency.

Pressure of Big Data and Real-time Reports

Just a decade or so ago, it was in order for banks in many of the world’s major economies to carry out liquidity-focused stress testing once a month. But as the 2008 financial crisis showed, market conditions that seemed stable can dramatically change in a matter of days. This means financial institutions have to be clear on what their liquidity position on a daily basis.

Not only does having such data daily leave the bank better prepared to plan for and weather a crisis, but it also gives it an edge over competitors in responding to market changing events such as a major stock market crash, political upheaval or devastating natural disasters such as tsunamis.

For the bigger banks, the realities of Basel III compliance imply enormous demands on data analysis. Financial institutions must capture, harmonize, analyze and report on more information than they have done before, often in chunks of a terabyte or more. Since this same data would be relayed over the same network that transmits information from day to day business processes, network infrastructure would be strained.

Ultimately, tweaking network architecture, data warehouse and other technology infrastructure on a regulation to regulation basis is neither cost effective nor does it allow for the efficient use of existing resources. Instead, the senior management of financial service providers must develop a detailed IT infrastructure and business process plan that envisages changes to future regulatory environment. The visibility, analytical and reporting capability must be geared toward meeting not just current regulation but also the demands from future changes to the business environment.

One of the ways banks can prime their systems and processes for future changes is through the aggressive consolidation of their data into data warehouses. Such consolidation invariably requires data conversion and format standardization. The advantage of consolidation is that it will continue to be useful in future compliance requirements. Data consolidation provides an opportunity for such institutions to correct systems and processes that are not working efficiently.

New international banking regulations such as Basel III require such transparency that the regulator will want to clearly see how data was captured, consolidated and what techniques were used to clean up and standardize it. Thus, banks must adopt a data warehouse model and an approach to acquiring new technologies that incorporate the following:

● A model whose logic built around deep data extraction.
● An end-user friendly model that can be rapidly deployed and put to use in the entire organization.
● A model that allows for fast execution of queries across all systems in the enterprise.
● Built in consistency and accuracy checks that ensure the quality if data. Consistency checks should include a reconciliation process where discrepancies can be identified and resolved.
● A data warehouse model that has the capacity to handle large data volume and consistently perform what-if scenario analysis and stress testing.

Smart compliance

All banks must comply with industry regulations. However, not all approaches to compliance are equally efficient. One of the biggest mistakes institutions make is hurriedly acquiring technology to address what may seem like an urgent compliance or business need. Over time, the bank may find itself with numerous disparate systems from multiple vendors, a situation that would pose a serious challenge to consolidation efforts. If the organization has 20 distinct systems each generating data in its own format, developing a single snapshot view of the state of the entire bank based on all enterprise data would be a laborious exercise.