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As mass amounts of data meet ever-increasing regulation in the world of finance, sophisticated data management has never been more important. How a bank handles this complex problem will make or break its position as a global player.

Japanese banks face a pivotal moment in data management

With global regulatory bodies continually introducing new banking regulations, the burden to be fully compliant has significantly increased. Some regulations require banks to prepare new sets of data,1 while liquidity regulations, such as the Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR), compel banks to source data for their balance sheets. New regulatory concepts, such as the risk appetite frameworks and stress tests, will inevitably require banks to improve their data management systems even further.

In response, most large Japanese banks are currently building data management platforms – constructing large-scale data infrastructures that address every potential business and regulatory need. While a multi-purpose data platform is a step in the right direction, these ambitious projects sometimes fail at that very task due to their sheer scope and “do everything” strategy.

This complex problem has caused the industry to experience a paradigm shift from a "bigger is always better" mindset to a more functional and flexible data infrastructure design – an especially attractive approach for Japanese banks as they aim to both quickly respond to regulations and position regulatory compliance as a profit-making strategy.

The more effective use of risk capital

Improving low profitability has long been the biggest challenge to Japan’s slow-growth economic environment. After years of rebuilding following the global financial crisis, Japanese banks are finally redirecting their strategies. Large banks are pursuing new means of gaining revenue: expanding outside of Japan, such as making acquisitions in foreign countries, or merging with other banks in Japan. To increase their revenue, they are also taking more risks under a reasonable risk control regime, rather than simply containing the risks under a conservative limit framework.

Banks tend to regard regulatory compliance as an annoyance, as they believe it does not generate revenue in and of itself. New stress testing requirements have prompted many banks to ask, "Why do we have to use so many resources to analyze when and how we will die?" Beyond simply maintaining a banking license, though, regulatory compliance can help increase a bank’s profitability – if the data is effectively managed. Japan serves as an ideal case study to this point.

Banks are now asking themselves, “If we are already spending an enormous amount of time and money, why don't we make it more useful for senior management as well?” By developing effective management platforms, they can take risks in a more aggressive but reasonable manner, ultimately gaining the strength they need to compete with other global banks

Optimizing revenue, risk, and regulatory compliance: A complex puzzle

The risk appetite framework was introduced to Japan in 2011. Initially, many banks did not know how to reconcile their need to take more business risks with the framework’s ostensible purpose of reducing them.

After all, the major objective of Japanese banks was to achieve revenue targets, not just reduce risk. As banks realized that the quest for additional revenue sources would not be as simple as it was previously, they gradually “Japanized” their implementation of the risk appetite framework so that it fit their challenging environment. This shift became apparent in 2013-2014. As more complex and stricter banking regulations were released, however, they realized that optimizing revenue, risk, and regulatory compliance at the same time was a puzzle they needed to address.

Figure 1. Optimization of three factors: revenue, risk, and regulation
Figure 1. Optimization of three factors: revenue, risk, and regulation
Source: Moody's Analytics

Large banks now know that they have to find the best mix of solutions for these three factors. Making matters difficult is the fact that they tend to work against each other – trying to achieve higher revenue may cause higher risk and capital requirements. Moreover, new banking regulations may not be fully consistent with one another. For example, holding too many liquid assets to achieve the LCR could work against the leveraged ratio. With all of these contradictions, regulatory compliance can resemble a frustrating game of whack-a-mole.

In attempting to win this “game,” Japanese banks found that what had historically been their strength – their effective organization into necessary functions and departments – was actually their Achilles heel. If they worked separately on those three factors, it would be almost impossible to attain an optimal solution, especially if they stayed in departmental silos.

Banks discovered that risk appetite frameworks could, in fact, be a solution. They began implementing these frameworks as a platform for senior management to discuss how they could simultaneously optimize revenue, risk, and regulatory compliance.

Figure 2. Risk appetite frameworks and stress tests
Figure 2. Risk appetite frameworks and stress tests
Source: Moody's Analytics

At the same time, stress testing was also changing significantly. More banks began to use stress testing as a tool to verify their risk appetite and establish whether or not a proposed plan could withstand stress events and still achieve the three targets. This also helped senior management better understand their strategy’s weaknesses and adjust it if necessary.

Data management in Japan: A changing mentality

Japanese banks have begun to view regulatory compliance and data management as an opportunity to enhance their business and increase revenue, rather than as a mere cost of maintaining their licenses. Along with regulatory challenges, data management has emerged as a critical issue for Japanese banks.

Banks typically handle data management by using a management information system (MIS). An MIS is a series of IT platforms that are used for all stages of the process, from aggregating data to reporting to senior management. All banks must build this as a foundation for a risk appetite framework.

Japanese banks typically share three common data management challenges:

  1. Aggregating all the data at a group level: As some large banks expand their businesses globally, it has become more challenging to gather risk data in the same formats from all of their global entities and business units.
  2. Identifying new risks throughout the group: It is important to identify and quantify a global organization’s hidden risks and incorporate them into its existing risk management framework. Japanese regulators expect these emerging risks to be used for stress testing scenarios.
  3. Reporting those risks to senior management: Senior management cannot effectively use risk appetite indices or stress test results for improving the management of their bank unless they are reported accurately and promptly. An MIS should incorporate a highly automated “dashboard” system to quickly share all the risk appetite indices.

By overcoming these three challenges, Japanese banks can build a comprehensive data platform that lets them gather all the data, identify emerging risks at early stages (with stress testing results), and swiftly report Key Risk Indictors (KRIs) to senior management. Banks are now asking themselves, “If we are already spending an enormous amount of time and money, why don't we make it more useful for senior management as well?” By developing effective management platforms, they can take risks in a more aggressive but reasonable manner, ultimately gaining the strength they need to compete with other global banks.

The strengths and weaknesses of centralized IT organizations

While a strong data platform is the foundation for achieving risk management objectives, Japanese banks tend to deal with all the data issues at the same time by planning one huge IT project – making the work much trickier and the risk of delays or failure much higher. Moreover, by the time such an ambitious project is completed, there may well be new banking regulations with different data requirements to contend with.

This method has resulted in part from the typically centralized organizational structure of the IT group at Japanese financial institutions, which controls the IT work for every business line. A centralized structure enables banks to build a consistent and comprehensive IT infrastructure, but it lacks speed and flexibility in implementation.

This structure is often contrasted with the “federal” organizational style, which global banks outside Japan frequently use when they have multiple legal entities or business units in a group. In a federal style, each entity or unit has some level of independence in designing and introducing IT platforms. To maintain order throughout an organization, a senior IT officer at a holding company level controls all those activities. (With regards to data management specifically, some large US banks have assigned a Chief Data Officer, or CDO, although it is still rare for Japanese banks to have a CDO.) A federal style’s strengths and weaknesses are the opposite of those of a centralized style: what it offers in speed and flexibility, it lacks in consistency and comprehensiveness.

Banks do not need to expand the project’s scope to encompass a rebuild of the entire database – a potentially endless project – but can instead simply channel the existing data into a relay station. A data relay station is more cost efficient, too, as it is often completed within a much shorter timeframe than a large IT project.

There is a general distaste in Japan for IT environments that involve many different systems commingling like “spaghetti” or multiple overlapping data warehousing systems. This is probably due to Japan’s bitter experiences in the 1990s, when it struggled to integrate different IT systems after mergers. Therefore, data integration projects in Japan are commonly comprehensive in scope and enormous in scale, usually requiring many years to complete. But with the extensive, complicated, and ever-changing requirements of the current regulatory regime, this centralized IT model falls short.

Why is data management so challenging?

The Basel Committee on Banking Supervision issued a report in January 2015 called Progress in adopting the principles for effective risk data aggregation and risk reporting, which contains an interesting lesson about Globally Systemically Important Banks (G-SIBs). Surprisingly, 14 out of 30 G-SIBs revealed that they will not be fully compliant with at least one of the Basel Committee’s regulatory principles by the deadline in 2016. Some banks noted in the report that this is partly due to delays in initiating or implementing large-scale IT projects and the complexity of those projects.

Banks meet roadblocks when they try to accomplish multiple objectives when they have only one core challenge. A data infrastructure usually has more than one purpose, such as integrating all data locations, cleansing/reconciling data from different sources, constructing data flows to calculation engines, and adding calculation results to an MIS reporting flow to senior management/regulators. Even a simple project like constructing data flows between several IT systems could become delayed if managers decide to expand its scope. Maintaining focus on the most important project task is therefore paramount.

In addition to regulations requiring banks to source new data directly, the current focus of data management for Japanese banks lies in the following two areas, which may involve the use of data beyond regulatory compliance:

  • Calculation of risk appetite indicators and reporting by an MIS
  • Automation of a stress testing calculation

Determining risk appetite requires banks to collect data throughout a business unit to calculate multiple risk indicators, such as KPIs, and to report them to senior management/regulators through an MIS. Several steps of data processing are required, including aggregation, calculation, and reporting. Stress testing regimes also require banks to automatically and promptly conduct multiple calculations.

Recent trends show Japanese banks create multiple scenarios and conduct sensitivity analyses on a single indicator. For example, they simulate many patterns of LCRs based on several different data inputs, which are typically provided in a matrix. This helps senior management understand the nature of the LCR more effectively – how it behaves under stressful conditions and how it affects the firm’s liquidity. The results should be shown in a more intuitive way if stress testing results are to be used to improve the bank’s management.

Meeting the IT challenge: Focus, flexibility, and speed

There are two specific data challenges to which Japanese banks should pay the most attention. First, the data warehouse system has to be flexible, as the requirements for data management often change. Second, a data management platform needs to be implemented quickly.

These two challenges are exactly where Japanese banks’ method is relatively weak. They should instead seek a “lighter” IT system and focus on one task at a time by dividing the entire project into multiple task periods. By reasonably limiting a project’s scope, it is much more likely to succeed.

Focusing on “data flow” rather than “data storage” is one way to implement an effective, efficient data platform. Banks can maintain existing data sources and create a data flow, which gathers the necessary data from those sources and sends them to a new “data relay station.” Banks do not need to expand the project’s scope to encompass a rebuild of the entire database – a potentially endless project – but can instead simply channel the existing data into a relay station. A data relay station is more cost efficient, too, as it is often completed within a much shorter timeframe than a large IT project.

One reason a data project encounters trouble is that all these functions are built into a single data platform. A data relay station can be used for multiple functions, including data aggregation, cleansing, processing, and reporting, which work separately from a bank’s existing data platforms. Having a functionally separated data flow is less risky from an operational risk perspective, too.

Figure 3. Functional database
Figure 3. Functional database
Source: Moody's Analytics

Under this data management structure, existing data sources and a new data relay station are linked together. Data requirements based on regulations or management needs could be reflected in the data relay station, not at an existing data source level, which means banks can only work on the data relay station in a comprehensive way. Such a data relay station can be highly functional without necessarily covering all the banking activities that require data management.

Banks could also introduce this type of functional database into limited areas of business, such as stress testing. To do so, they would gather all the essential information, including balance sheet items and risk parameters, from all the existing databases. The functional database would then perform data processing to create consistent assumptions and send them to relevant calculation engines. The results would then be collected at the functional database again and reported to senior management/regulators through an MIS.

Sophistication is the future of data infrastructure

Sophisticated data management is the foundation for both improving the management of a bank and increasing revenue in global markets. For many Japanese banks, imitating the best practices of foreign banks is not necessarily the best solution. While they struggle to find the appropriate direction, they have gradually succeeded in "Japanizing" some regulatory concepts while expanding their own concept of effective IT infrastructure beyond the cumbersome “centralized” approach.

Japan’s banks are currently being challenged to significantly improve their data management systems, specifically for achieving flexibility and speed to continue growing revenues while meeting regulatory requirements. Accomplishing this will allow a bank to become a stronger, more competitive player on the global stage.


1. Basel Committee on Banking Supervision, Principles for effective risk data aggregation and risk reporting, 2013

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