Accenture ACN 2012 Risk Analytics Study Insights for the Banking Industry

Accenture Risk Management:2012 Risk Analytics Study

Insights for the Banking Industry

Executive Summary

Our 2012 Risk Analytics Study—conducted by Accenture Risk Management—surveyed more than 450 risk professionals (see sidebar, “About the Research”) across several industry sectors to assess the support for, and maturity of, risk analytics technologies, tools, processes and talent. Support is strong for analytics as a means to mitigate risks more effectively, though the patterns of responses from those surveyed show that the risk analytics field is, in many respects, still in its infancy in terms of its practical implementations across these industries.

Companies are investing in risk analytics and intend to increase those investments, yet the potential return is often stifled by inconsistent or incomplete data. This prevents organizations from generating the insights needed to support a more predictive approach to risk management.

Steve CulpManaging Director

Accenture Risk Management: 2012 Risk Analytics Study

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Sixty-five percent of respondents say that management’s use and acceptance of risk analytics within their organization is either excellent or above average. Risk analytics leaders are especially successful in this area: 62 percent rank themselves as excellent when it comes to management use and acceptance of risk analytics, compared with only 21 percent of laggards.

Investments are increasingIn the past year, 87 percent of organizations increased their investments in analytics technologies for managing risk; 58 percent of those increased their spending more than 10 percent, and 14 percent increased investments more than 30 percent. Over the next two years, the vast majority of organizations anticipate that their investments in risk analytics will continue to increase. Investments are expected to focus mainly on data quality and sourcing, systems integration and modeling. These findings are generally consistent across the industries studied.

Risk analytics leaders are more likely to have made significant investments. Among leaders, 28 percent have made investments of 30 percent or more, while only 12 percent of laggards have invested at this level.

Slightly fewer than one in five (16 percent) of the surveyed companies ranked their risk analytics capabilities as industry leading. Although this is a survey-based assessment, further analysis by Accenture comparing the data from this group (“Leaders”) with that of the other 84 percent of the survey group (“Laggards”) has generated important insights—both in the results achieved and in the distinctive capabilities of an advanced risk analytics practice.

For example, 83 percent of leaders, but only 54 percent of laggards, indicate that the use of risk analytics has significantly improved the quality of decision making. Conversely, one in five laggards says the use of risk analytics has not improved decision making, whereas only one in fourteen leaders has the same difficulty.

One reason for these findings appears to be that, among the leaders, specific analytics tools are better integrated into decision-making processes. For example, 58 percent of leaders say stress testing is integrated with strategic decision making for large projects, while only 34 percent of laggards say this is so. Risk reporting is also more mature among leaders: 71 percent note that risk reports are generated and used by operations and senior management, compared with only 50 percent of laggards.

The Accenture 2012 Risk Analytics Study has found that many challenges lie ahead for organizations looking to achieve distinctive capabilities in risk analytics. What is consistent across the surveyed groups, however, is that all see risk analytics as an area that can deliver competitive differentiation.

Summary of Cross-Industry FindingsThe industries studied vary widely in their business challenges and strategic goals, so risk analytics takes different forms across the different companies. However, based on analysis of the data, Accenture has identified five common trends across the industries studied.

OneInvestments in risk analytics are increasing and executives expect ongoing developments in this area.

Executives are supportiveAbout 95 percent of surveyed companies are currently using risk analytics. About half (49 percent) are using these techniques in a coordinated way across the company while the other half (47 percent) are implementing solutions in pockets, in particular geographies or business units. The primary applications are for risk based capital, managing credit, and business strategy.

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TwoThe maturity of risk analytics is uneven across essential capabilities and functions, so the value being achieved is not yet robust.

Few companies assess their analytics capabilities as industry leading.Although about half (46 percent) of the organizations surveyed rate their risk analytics capabilities as being above average, only 16 percent, as mentioned earlier, rate themselves as best in their industry. About one–fourth of companies across the industries studied are not even using risk analytics in their organizations at this time.

More than half of organizations (57 percent) say that risk analytics significantly improves decision making. However, in terms of specific analytics tools, 62 percent of respondents say that scenario modeling and stress testing tools are either not being used or are only made available to executives, rather than being integrated into strategic decision making. As noted earlier, risk analytics leaders are distinguished from their peers in their ability to drive better decision making from their analytics capabilities.

Some components of the technical environment are still immatureAsked to rate the maturity of different risk analytics capabilities, the lowest scores (poor and fair) were in software (13 percent), systems integration (12 percent), and data quality and sourcing (12 percent). These areas will have the greatest impact on risk analytics capabilities, processes and solutions.

Risk analytics leaders exceed their peers in the maturity of almost all of these technical components. With systems integration, for example, 40 percent of leaders describe their capabilities as excellent, compared with only 16 percent of laggards. The following are other percentages comparing excellence between leaders and laggards:

• Business rules development: Leaders, 51 percent; laggards, 19 percent.

• Modeling: Leaders, 52 percent; laggards, 18 percent.

• Software: Leaders, 44 percent; laggards, 15 percent.

• Reporting and dashboard development: Leaders, 44 percent; laggards, 17 percent.

ThreeData consistency is a significant challenge.

In general, data availability is not a major issue: Only 7 percent of respondents cited a lack of data as one of their challenges. The problem, instead, is often one of data consistency, rooted in the inability to integrate analytics and insights across siloed divisions and functions, severely compromising the effectiveness of the overall risk management capability.

Of all respondents, 40 percent determine risk analytics data requirements by collecting data in pockets internally within the firm. Only 27 percent of those surveyed have a fully aggregated view of risk across their organizations.

Leaders show significantly more advanced capabilities in data quality. Fifty-four percent of leaders describe their capabilities in data quality and sourcing as “excellent,” compared with only 19 percent of laggards.

More than 54 percent of leaders note that they are able to take a fully integrated view of risk aggregated across models, while only 22 percent of laggards claim this capability.

Laggards are more likely to have trouble with siloed data. Forty-four percent say that data about risk events is collected in pockets internally within the firm, while only 23 percent of leaders say this is so.

FourRisk analytics is currently more preventive and reactive than predictive.

Only about one-third of companies studied (36 percent) say their risk management resources are proactive and strategic; 46 percent say their approach is primarily preventive; and almost one in five (18 percent) say their risk management capabilities support merely reactive responses to events. Spending also reflects this insight: The allocation of risk resources, across all industries, is primarily for preventive activities.

Far greater percentages of leaders are apt to say they use analytics in fraud prevention—82 percent, compared with only 52 percent of laggards. Leaders also link analytics to business strategy more effectively: 79 percent say they use analytics in setting business strategy, while only 60 percent of laggards do so.

FiveLack of expertise in risk analytics looms as an important challenge.

Most organizations (71 percent) build their current risk analytics capabilities in house with support from outside vendors and/or consultants. Twenty-nine percent do not rely on any external specialists but use only internal staff. However, only 19 percent of companies surveyed rank their staffing capabilities as “excellent.”


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These findings underscore the fact that analytics is a relatively new field, and that optimal talent sourcing and development is not yet in place. Organizations need to consider how best to meet that challenge, whether it is accelerated internal development, better hiring, or more comprehensive external sourcing and collaboration, even on a managed service basis.

Risk analytics leaders tend to be challenged less by staffing and capability issues. About one-fourth of risk analytics laggards (24 percent) are challenged by a lack of skills to develop risk models, compared with only 11 percent of analytics leaders.

Recent research from the Accenture Institute for High Performance (“Counting on Analytical Talent,” Accenture 2010)1 has found that analytics talent at many organizations is not developed and nurtured effectively. Many companies do not manage analytical talent as a distinct and valuable workforce. Analysts are often scattered throughout departments; many companies do not have a clear picture of who their analysts are or where they reside organizationally.

Companies also struggle with how to structure an analytics team, in terms of whether it should be centralized or decentralized. The research revealed that companies that want to build a strong analytical workforce are best served by greater centralization and coordination of their analytical talent. Doing so ensures that analysts are working “close to the business” on the most important initiatives and also “close to one another” to coordinate their efforts and to promote mutual learning and support. It also ensures that analysts have the kind of meaningful work and career opportunities that are critical to their engagement and retention.

Making the Right InvestmentsThe Accenture 2012 Risk Analytics Study has found strong support for analytics across several important industry sectors, but also reveals that many components of the analytics field are still growing in maturity. In general, companies should be looking to make focused investments along three dimensions: technology, people, and organizational structures and processes.

Advancements could be made in areas such as modeling and testing but, as the study clearly found, investments in capability development will be equally important. As analytics grows in importance, especially within the risk function, better approaches to talent sourcing, development and retention will be essential, especially as the value of top talent becomes clearer to companies.

The range of risks to which an organization is susceptible is increasing in scope and severity; events in the external world—natural disasters, political upheaval and economic crises—should heighten everyone’s awareness of systemic risks, which can only be addressed with a more holistic and integrated approach to data gathering and analysis. However, as our study found, risk analytics is too rarely integrated across functions and business units—a problem that could be addressed by looking at how different groups interact and cooperate. As organizations advance their analytics capabilities they might look for the interdependencies and not get trapped in siloed views or single-dimension structures. This could require upgrades to current data governance capabilities.

Modeling and testing tools are important, but only if they are incorporated into business processes, especially processes for decision making. The ability to leverage analytics technologies to generate timely and relevant business insights depends on changing behaviors and typical ways of working. If tools are available but are not incorporated into workflows, they will likely have minimal impact.

The challenge for all institutions is on focusing their risk analytics effort properly. There is hardly a company anywhere that is not actively involved in the field of analytics. But it’s not just new tools they really want or need. What they actually want is more insightful and timely information to make more effective decisions that drive business value. The human element—and the leadership element—is always essential. It is too easy to get lost in the issues and volumes of data, and also to be romanced by the power of technologies and tools. It is equally important to simply know how to ask the right questions. Generating meaningful insights, and harnessing the power of analytics to anticipate risks before they arise, depends ultimately on knowing what you’re looking for.

We hope the findings of this research will spur discussion and further reflection. Please contact me at [email protected] for more information.

Steve CulpManaging DirectorAccenture Risk Management


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The 2012 Risk Analytics Study—conducted by Accenture Risk Management—is based on a survey of 465 managers and executives from all major geographic regions. Respondents were from the banking, insurance and chemicals industries and all held corporate positions in which they were responsible for developing or utilizing industry-specific analytics capabilities.

The purpose of the study was to assess the relative maturity of risk analytics methods, tools, technologies and processes; to determine their current effectiveness in driving business, customer and market insights to support better decision making; and to identify current trends.

Region

35%

28%

20%

9%

8%

Industry

22%

19%

19%

40%

Europe

North America

China

ASEAN (The Association of Southeast Asian Nations)

Japan/South Korea

Banking

Insurance - P&C

Insurance - Life

Chemicals

About the ResearchAccenture Risk Management: 2012 Risk Analytics Study

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Breakdown of Respondents by Location

Breakdown of Respondents by Industry

Note: Due to rounding, figures may not total 100%


Role

38%

23%

8%

14%

16%

Revenue

28%

25%32%

16%

Greater than $10 billion

$5 billion to $10 billion

$1 billion to $5 billion

$100 million to $1 billion

C-Level Executive (CEO, CFO, COO, CIO, CMO, CRO)

Senior Vice President, Executive VP or VP

Managing Director, Senior Director, or Director

Senior Manager or Manager

Other (Analysts, Technicians, Actuaries, Underwriters, etc.)


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Breakdown of Respondents by Revenue

Breakdown of Respondents by Role



Although the field of analytics has not yet reached full maturity, it has been around long enough that it is fair to ask how effective such solutions are in making a difference to corporate performance—especially when it comes to managing and mitigating risks.

As Jeanne Harris and Tom Davenport put it in their recent book, Analytics at Work,2 there is a “ladder” of analytical applications that increases in sophistication and value as companies proceed up each rung. (See Figure 1.) At the bottom of the ladder is simply getting your data right. Toward the top of the ladder are more predictive capabilities, ultimately arriving at a place where analytics enables optimal responses to be embedded in processes, leading to real-time optimization of performance.

When it comes to risk, though, has anyone reached the top of that analytics ladder— especially companies in the financial services sector, which, by and large, failed to identify, predict and mitigate the risks that led to the 2008 financial crisis?

Accenture Risk Management has completed a global risk analytics study capturing and synthesizing the insights from more than 450 analytics professionals across three industries on how they use risk analytics to tackle industry challenges and market volatility.

The purpose of the study was to assess companies’ current level of risk analytics maturity—their quantitative and qualitative tools and techniques designed to estimate the impact and frequency of specific risks, as well as their ability to use analytics to drive business outcomes and proactively manage risks and rewards. For banks, an outcome-based approach would manifest itself, for example, in the manner in which analytics is embedded in outputs such as pricing and performance management.

Introduction

Source: Jeanne Harris and Tom Davenport, Analytics at Work: Smarter Decisions, Better Results (Harvard Business Review Press, 2010).

Real-time optimization

Institutional action

Predictive action

Differentiated action

Key targets/segments

Data in order

Figure 1

Ladder of analytical applications

Prediction and differentiated action embedded in process


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Predictions of response by target/segment

Optimal response embedded in

real-time process

Key targets and segments

defined

Well-defined, common, clean, and

integrated data

Different approaches for different targets/

segments

It is important to remember that analytics is the means toward an end; that end is the delivery of insights and timely information to make more effective decisions that drive business value.

So, a key to our study was exploring not only data-gathering processes and capabilities, and not only technologies and tools (because it is easy to get lost in the data and enamored by the tools), but also issues related to skills and to techniques such as stress testing and back testing that can help properly manage and direct an analytics capability toward meaningful results.

In terms of the cross-industry findings (for more, see sidebar), we found that investments in risk analytics are increasing. Of the organizations surveyed, 87 percent increased their investments in predictive analytics technologies for managing risk. More than half of the organizations (57 percent) say that risk analytics significantly improves decision making and risk monitoring. However, the maturity of risk analytics is uneven across essential capabilities and functions.

Survey data and experience point to several challenges that banks share with organizations in other industries in improving their risk analytics capabilities:

• Integrating analytics and insights across multiple data sources, and siloed divisions and functions.

• Harvesting and managing data across the enterprise, due in part to ineffective data governance, poor data quality and insufficient data integrity.

• Lagging analytics technologies, with companies not yet reaping the full benefit of IT advancements.

• Lack of expertise and skilled resources, leading to delays and project overruns.

• Inability to communicate results and insights effectively.

Dr. Davide Crippa, the head of wholesale banking risk optimization and reporting for Standard Chartered Bank based in Singapore, summarizes well both the challenge and opportunity of risk analytics: “Risk analytics is more than just mathematics and technology; it is the ability to combine quantitative sciences with sound risk management practice in order to support the decision-making process. It requires intellectual rigor and the constant challenge of data, assumptions and models. And finally it will only be effective if it can be communicated in a clear and suitable fashion to the top level of the organization, where decisions are ultimately taken.”

Banks show strong commitment to improving their capabilities in risk analytics, yet are looking for guidance in several specific areas relating to technology, governance and organizational skills.

“Risk analytics requires intellectual rigor and the constant challenge of data, assumptions and models.” Dr. Davide Crippa, Standard Chartered Bank

Source: Jeanne Harris and Tom Davenport, Analytics at Work: Smarter Decisions, Better Results (Harvard Business Review Press, 2010).


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What is behind these statistics about increased investments in risk analytics? Across geographies, what are the business needs that banks hope to address through better analytics capabilities?

One important goal is to improve credit performance and reduce credit costs. The percentage of non-performing loans is still unacceptably high for most banks, and risk analytics offers the promise of reducing the number of bad loans and lowering costs by reducing capital and letting go of overly risky customers in addition to non-profitable accounts. With advanced risk analytics capabilities, banks can, for example, identify characteristics and trends of non-performing loans and take proactive steps with the counterparties to address issues or even refinance or restructure deals before more serious problems arise.

In some areas, banks are making proactive investments in analytics as a means of achieving competitive advantage—to generate the insights needed to move in a direction that is different from their competitors and seize advantage.

Banks also are looking to better understand the risks in their portfolio. The high concentration of mortgage investments in their portfolios has banks looking to increase their abilities to analyze how their portfolios line up with their risk framework and current risk tolerances.

Regulation is an important factor pushing banks toward greater analytics capabilities. For example, banks in the Asia-Pacific

region are at varying levels of maturity when it comes to risk analytics, and the disparity is driven largely by the different pace of Basel II adoption in the last decade. Countries such as Singapore and Australia are quite advanced, while others like Vietnam and Indonesia are still in their infancy in terms of analytics, and will need greater capabilities to achieve Basel II compliance. Malaysia continues to evolve its analytics capability as a result of a major regulatory milestone for Internal Capital Adequacy Assessment Process (ICAAP) compliance which is scheduled for the first quarter of 2013. Vietnam and Indonesia are seeing increased investments ahead of the impending regulatory push by their respective regulators.

However, countries deemed more advanced are not having an easy time, either, as the ongoing regulatory overhaul is leading to increased demand for analytics capabilities in areas such as managing liquidity positions, evolving liquidity measurement techniques, counterparty credit risk, credit valuation adjustments and integrating these into capital stress testing.

Analytics show promise of helping banks anticipate some of the unintended consequences of regulation. For example, requirements increasing capital can result in restricted lending, or restrictions on proprietary trading may result in lower liquidity in key bond markets, neither of which is desirable. Scenario analysis can help banks deal more proactively with such consequences by helping to assess the impact of different circumstances and responses. For example, in today’s rapidly changing regulatory environment, banks

Across the industries we studied, banking is predicting the greatest increase in risk analytics investments, with 73 percent of respondents foreseeing more than a 10 percent rise. (See Figure 2.) In terms of specific capabilities, risk analytics spending is expected to increase most in areas of data quality and sourcing, systems integration and modeling. (See Figure 3.) Risk analytics leaders in banking invest at higher levels. For example, in the past year, 44 percent of leaders have increased investments at least 20 percent, while only 31 percent of laggards increased investments at that level.

A Strong Commitment to Risk AnalyticsAccenture Risk Management: 2012 Risk Analytics Study

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Over the next two years, how does your organization expect investments in risk analytics to change?

Global Insurance – Property & Casualty

ChemicalsBanking

Figure 2

Banking is predicting the greatest increase in risk analytics investments, with 73% foreseeing more than a 10% rise

Increase 0% – 9.9% Increase 10% – 19.9%Increase greater than 20% No change

Decrease 0% – 9.9%Decrease 10% – 19.9%Decrease greater than 20%

Insurance – Life


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have a greater need for capital, as Basel III demands. Scenario analysis enables a more structured assessment of the reduced levels of capital available to generate income. In the mortgage business, companies may have instituted a tighter credit policy such that all loans require at least a 20 percent down payment. Analytics can help determine the impact of that policy on the business’s long-term profitability.

Growth and expansion are additional drivers of risk analytics. The competitiveness and profitability squeeze in advanced economies is driving the larger banks in the region to look outside of their home countries for opportunities in emerging economies. This is necessary for these larger banks to counter the decrease in net interest margin and drive sustainable growth.

In looking towards these growth markets, banks are making investments to enhance their acquisition models, enabling them to “cherry pick” the better customers and

to manage the level of non-performing assets. Some of the local banks, faced with competition from the large regional players, have responded by making similar investments in better customer and risk analytics capabilities.

Local institutions have a number of advantages over global players in that they will usually be more intimately familiar with the dynamics of a local market (for example, why a certain product resonates with locals based on income, culture and perception) and may have a richer, more extensive local data history that a global company entering the market may have difficulty matching. A richer understanding of the local regulatory environment, and deeper relationships with regulatory bodies, is also an advantage. Analytics, as noted, is about more than just the tools; human judgment and experience are also important, and so data interpretation that is more robust, based on local familiarity, can potentially provide an advantage.


In addition to financial risk factors, banks are also incorporating into their risk models effects of various world events and external factors—environmental, political and financial. In an increasingly connected world, natural and industrial disasters, as well as political crises, have generated waves of impact on many regions of the world. These multiple interrelations create a complexity that makes effective risk modeling difficult.

With the increased focus on holding Boards responsible for risk management, senior-level executives are demanding that information and insights be provided in a more timely and accurate way. These demands are leading banks to invest in data integration, enterprise risk and performance dashboards, stress testing and scenario analysis.

Dealing with the huge amounts of data that banks have, and which they accumulate by the petabyte each day, is another ongoing challenge. Our research found that bank executives are not concerned about the availability of data; only seven percent of participants across all industries cited a shortage of data as an issue. The problem, instead, is with the tools that are supposed to make sense of that data to support better identification and mitigation of risks, and with the skills and knowledge that guide the proper use of tools and the interpretation of data.

On the other hand, having a lot of data does not necessarily mean it’s the right data, or data that gives banks the historical perspective they need. One accepted standard currently being used by several banks in North America is to maintain one to two economic cycles of data, or data covering from about 10 to 20 years. This kind of data history enables banks with the right tools and capabilities to analyze trends and influences that can only be understood by taking a long-term view.

Some risks, however, must now be assessed more frequently. For example, country risks are often examined on an annual basis. But the recent upheavals in the Middle East, as well as financial difficulties in some European countries, might indicate the need to look at country risk on a more frequent basis. In fact, a number of banks from all geographic regions are now moving toward a monthly assessment of country risks. The ability to achieve the business goals of risk analytics is currently hampered by the inability to integrate risk analyses across silos. Only 27 percent of those surveyed have a fully aggregated view of risk across their organizations. Nearly half (47 percent) say that risk analytics is being leveraged only in siloed pockets within their firms.In this regard, risk analytics leaders in the banking industry indicate they are more successful than laggards in achieving integrated views of risks. Only 16 percent of banking leaders indicate that they collect data only in pockets, compared with 44 percent of laggards. Forty percent of leaders say that firmwide data is readily available internally, but only 22 percent of laggards say this is so. Sixty-five percent of leaders rate the maturity of their data quality and sourcing capabilities as “excellent,” while only 21 percent of laggards assess themselves at that level.

The ability to achieve the business goals of risk analytics is currently hampered by the inability to integrate risk analyses across silos


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Figure 3

Analytics investments are expected to increase, especially in the areas of data quality and sourcing, systems integration and modeling

Over the next two years, in which areas does your organization expect to increase Risk Analytics investment spend? (Select all that apply)

Banking

Data quality and sourcingModelingBusiness rules developmentManagement use and acceptance

Insurance – Property & Casualty Chemicals

Systems integrationSoftwareReporting and dashboard developmentStaffing

Insurance – Life


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Firms are devoting resources to credit models, incorporating internal as well as external data. The use of stress testing and back-testing techniques is increasing, particularly when internal models are being used.As noted, regulatory pressure is a big factor in driving improved credit risk analytics—helping banks to make more effective, predictive decisions. Supporting this hypothesis is the finding that regulatory reporting is one of the primary factors driving credit risk analytics. (See Figure 4.)

A second factor is developing the ability to drive more granular, detailed data, with the ultimate goal of delivering insights on a near on-demand basis. That is, banks are trying to do a better job of understanding the risks they’re taking on every transaction. So, especially in the US, for example, banks are looking to improve their ability to understand capital allocation at the pre-transaction level and to conduct more segmented analysis.

Our study found that banks do have processes in place for collecting historical data from legacy systems into certified data warehouses. (See Figure 5.) North American banks are further ahead in terms of having certified data warehouses (77 percent have such a capability in place). Risk analytics leaders are clearly ahead in this area. Ninety-one percent say their IT department has an organized method of collecting historical data from legacy systems into a certified data warehouse, compared with only 44 percent of laggards.

Credit Risk AnalyticsAccenture Risk Management: 2012 Risk Analytics Study

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Within banking, the focus of risk analytics is especially on credit risk, which has been driven by regulatory requirements as well as a focus on improving the credit quality of different portfolios.

Credit risk analytics can support different business areas. Specify the contexts where credit risk analytics is employed or where you plan to use it within the next 12 months. (Select all that apply)

OverallNorth

America Europe China ASEAN

RegulatoryBasel II and III reporting 56% 52% 74% 53% 41%Regulatory reporting 65% 74% 60% 58% 68%

Credit processing

Authorization workflow 56% 40% 66% 69% 51%Credit renewal 63% 66% 58% 60% 71%Override 25% 22% 22% 24% 32%Credit monitoring – early warning 63% 64% 66% 51% 71%

Risk governance

Credit policy 59% 56% 54% 60% 66%Risk analysis 77% 72% 86% 69% 80%C-level/Executive reporting 36% 34% 32% 44% 34%Commercial branch reporting 32% 30% 30% 47% 22%

Capital management

Capital planning and budgeting 47% 38% 42% 58% 51%Capital allocation 51% 44% 40% 64% 56%Performance measurement & management 44% 48% 44% 40% 44%Portfolio (risk/return) analysis 48% 50% 48% 51% 44%Risk-based pricing 35% 52% 34% 31% 20%

Marketing

Risk-based pricing 49% 42% 44% 69% 44%

Rating advisory 38% 26% 38% 56% 34%

Incentive program 25% 34% 18% 33% 12%

Figure 4

Credit risk analytics is currently employed mostly within risk governance analysis and regulatory reporting


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Figure 5

Half of all banks’ IT departments have an organized process to collect data from legacy systems into a certified data warehouse

Effective risk analytics depends on structured and secured data collection. What is the data collection and storage process in your organization?

IT department has an organized data process to collect all historical data from legacy systems into a certified data warehouse to perform risk analytics

Data are collected in a local risk database within the department with no data quality controls or source certification

IT plans to deliver a risk data warehouse in the near future to enable risk analytics

No structured and secured data collection and storage process

Figure 6

42% of banks calculate risk indicators by collecting data from the execution environment that feeds monitoring dashboards

Key risk indicators are regularly calculated collecting data from the execution environment feeding monitoring dashboard

An early warning system is scheduled to be released soon which will include the key risk indicators

Key risk indicators are calculated on a spreadsheet from time to time

No key risk indicators are available

Overall North America

Europe China ASEAN


Europe China ASEAN

An early warning system is an effective way to monitor credit portfolio performance. Are key risk indicators and dashboards available within the firm?


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In practice, however, this data collection is manually intensive, leading to significant issues with data quality and reconciliation with the general ledger. Although it is rare to attempt to move toward a single data platform, at least one institution in Asia is in the process of moving toward a common platform with an integrated dashboard.

For the most part, current analytics capabilities generally prevent banks from translating data into capabilities that are more predictive in nature—helping banks manage downside risk as well as support growth. Banks want to gain a competitive edge when it comes to predicting when the market is going to turn, but they also need to anticipate potential problems as they work to improve their compliance capabilities.

Of the banks participating in our study, 42 percent globally are regularly able to collect data from the execution environment and calculate key risk indicators in something akin to a monitoring “dashboard.” Banks in North America are above the average at 64 percent, as is Europe at 49 percent. Asia-Pacific banks generally lag their global counterparts in this area. However, close to half of the banks in China and the ASEAN region (countries in the Association of Southeast Asian Nations) plan to develop dashboard capabilities soon. Risk analytics leaders exceed their peers by a wide margin in this area. Seventy-seven percent of leaders regularly calculate key risk indicators by collecting data from the execution environment, compared with only 37 percent of laggards.

It is worth noting, however, that significant percentages of banks in every region—roughly from 20 percent to 30 percent—either do not have key risk indicators available or deliver them only in rudimentary fashion on a spreadsheet that is only used occasionally. (See Figure 6.) While in smaller banks, the effects of not having risk dashboards available may be limited or counterbalanced by a set of efficient credit processes, large banks lacking risk indicators may experience an increase in their delinquency and default rates and a more limited ability to react to economic changes.

In general, we can conclude that credit risk analytics can have a positive impact on banks’ balance sheets; it can give them more control over assets, and more flexibility and effectiveness in the credit process improvement. However, the challenge is to be able to develop a global vision over the analytical data to keep all the puzzle pieces together, and to generate insights from that data.

Current analytics capabilities prevent banks from translating data into capabilities that are more predictive in nature


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Credit rating models have become a distinctive commodity to improve credit quality and have boosted the credit risk analytics field. Banks are working to improve their internal models to increase their ability to deliver insights. Based on our survey data, more than three-fourths of banks use a mix of both internal and external models to improve credit quality. However, we also saw evidence of regional differences. More European banks are likely to use only internal models (18 percent versus 13 percent globally) while in China, banks are more likely to use only external models (14 percent versus 7 percent globally).

Influences here have to do, first, with a bank’s maturity in the analytics space—since between five and seven years of data are required to validate an internal model. China’s tendency toward external models, for example, is largely rooted in that absence of data history. Second, a move toward internal modeling may be driven by regulations. Under Basel II, banks and their supervisors need to assess the soundness and appropriateness of internal credit risk measurement and management systems, and thus it is important to develop the means to assess those rating systems.

The more widespread adoption of bank models (rating models for the bank asset class) is also being driven by the use of external models from organizations such as Standard & Poor’s (S&P) and Moody’s. External models can be effective for banks for both management and regulatory purposes. External models have become more precise and accurate because of providers’ capability to collect detailed regional information about customers’ behaviors. These models are helping banks improve their quality of credit and manage their non-performing loan rates more effectively.

The use of external models can also help smaller firms compete more effectively because they do not necessarily have to ramp up and staff a dedicated internal team of analytics experts, and they can get products and services to market faster.

Many financial institutions do not have sufficient data to create internal models, and thus rely primarily on external data modeling. However, in Europe as well as North America, medium-sized banks are now evolving to the next level of credit risk analytics by developing their internal rating models through the collection of historical customer data within their systems and also by leveraging the knowledge and experience they have gained in recent years as they have used external models.

The Basel II framework generally classifies exposures into five categories: sovereigns, banks, corporate, retail and others. Where rating models have been adopted, respondents are primarily assessing their bank portfolios (75 percent) and retail portfolios (73 percent). (See Figure 7.)

Modeling

External models are helping banks improve their quality of credit and manage their non-performing loan rates more effectively


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Better modeling is a critical component of credit risk analytics.

Overall

ASEAN

China

North America

Europe

Banks

Retail (mortgage, loans, cards, current accounts)

Large corporate, mid corporate, small & medium enterprises

Countries

No models available

Other (leasing, project finance)

If rating models have been adopted, which portfolios (e.g., corporate, retail) do they cover? (Select all that apply)

Figure 7

For most respondents, rating models cover the Banking and Retail portfolios


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One of the challenges in developing better modeling capabilities has been finding and/or developing the required skills—not only the technical and quantitative capabilities, but also the business savvy to build meaningful models.

Noel D’Cruz, head of risk portfolio management for OCBC Bank Ltd, emphasizes the criticality of having both the right technology tools and the right skills: “As incomes grow and countries modernize there is huge potential to use analytics in risk management. This will require substantial investment in systems

to capture and store data efficiently, and also analytical skills to transform data into insights for competitive advantage.”

Risk analytics leaders in the banking industry are less challenged by staffing problems. Fifty percent of leaders rank the maturity of their analytics staffing as “excellent,” while only 15 percent of laggards rank themselves that high. About one-fourth (24 percent) of laggards note that finding skilled staff to perform modeling is having a major impact on the effectiveness of their risk analytics processes, while only 10 percent of leaders are similarly challenged.

Talent problems are rooted partly in a general shortage of skills in the job market, but also in the fact that data requirements are often vague as delivered from the business to IT. Banks have been working

to improve their internal skills. According to our study, most banks have a dedicated risk analytics group within their credit risk department with between one and twenty people deployed. (See Figures 8 and 9.)

What are the relative merits of having a large analytics group versus a smaller one? Bigger is not always necessarily better. However, with a more expansive group, risk departments have the opportunity to specialize so that more models are available to handle more specific types of risk—operational risk models, market risk models, risk grading models, product risk models, and so forth. With a larger group, firms can validate and reconfigure the models more frequently with a larger set of indicators. Internal risk models can also be tailored to improve accuracy rates for issues such as default predictions or loss estimates.

The Skills Shortage

Figure 8

Most banks have a dedicated risk analytics group with between one and twenty people deployed

Credit risk analytics usually focus on the development of internal rating models and on key risk indicators. Is there a dedicated risk analytics group within the credit risk department? If so, how many people are deployed to the group?

>20

11 – 20

1 – 10

0 – Planning to establish a dedicated group

0 – Not existent


Europe China ASEAN


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Banks that are now enjoying a competitive advantage in the credit risk space are those that were ahead of the pack in setting up focused teams dedicated to analytics. Emerging markets across Asia-Pacific, for example, are having more difficulty staffing internal credit risk analytics teams. Our study found that the bigger credit risk analytics teams (those with more than 20 personnel) are prominent in North America, while banks in the Asia-Pacific region are more likely to have smaller teams. The size of an analytics team may be relative to need, yet the advantage lies in having a dedicated group that is able to drive insights and information to the right people at the right time, thereby enabling better decision making.

Figure 9

85% of banks have a risk analytics group for credit risk validation

Is there a risk analytics group for credit risk model validation? If so, how many people are deployed to credit risk analytics?

>20

11 – 20

1 – 10

0 – Planning to establish a dedicated group

0 – Not existent


EuropeChina ASEAN


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Overall

ASEAN Data quality controls performed when collecting historical data (ETL, external tools, manual controls)

Data policy

Data quality department

None

Which data quality/data management processes and tools are in place to ensure a clean and coherent environment for risk analytics? (Select all that apply)

Figure 10

Relatively few banks across all regions have a data quality department

A critical dimension of modeling, and of developing better predictive risk capabilities, has to do with the quality of the data itself. “Garbage in, garbage out,” as the old saying goes.

In our study, we asked banks about the maturity of their data quality controls and the benefits they are looking to deliver from improved controls.

According to the results, significant majorities of banks across all regions have quality controls in place over the collection

of historical data. ASEAN institutions are mostly likely to do so at 85 percent, and Europe is least likely, at 64 percent. ASEAN firms are also the most likely to have data policies (87 percent) compared with North America (65 percent), China (69 percent) and Europe (59 percent).

Perhaps not surprisingly, risk analytics leaders are more likely to perform data quality controls when collecting historical data: 91 percent say they do so, compared with only 71 percent of laggards.

Fewer banks across all regions have a data quality department. The ASEAN region leads again in this area at 64 percent, while only 32 percent of North American banks have such a department. (See Figure 10.)

What are some possible reasons for ASEAN banks being ahead of their global counterparts in the area of data quality controls? First, over the past 10 years, many ASEAN firms have undergone a technology refresh such as core banking replacement, finance/general ledger implementation or warehouse implementation. Thus, a great deal of the data collection, data quality, governance and management capabilities have been established along with such programs. Second, ASEAN banks in the advanced countries (and some banks in emerging countries) that have responded to Basel II requirements would also have established all the data-related initiatives.

Data Quality ControlsAccenture Risk Management: 2012 Risk Analytics Study

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Europe

North America

China

The effort to improve data quality controls is being driven by several factors, including regulatory pressure and the need to reduce costs. The business is also demanding faster speed to market of data-dependent products, and also looking to reduce the complexity of the business environment by generating new insights.

Numerous people and multiple departments are involved in the value chain of data management and in making sure that the data is available. Many banks currently suffer from poor metadata management—the layer that helps them understand the true definitions of their data and deal with it in a sustainable way. That adds costs as expensive resources can devote excessive amounts of time to data cleansing and validation. It is not uncommon for some banks in North America, for example, to spend from 60 percent to 80 percent of their time collecting and aggregating data and then cleansing it before they can even get to an analytics and reporting step.

The other issue has to do with the complexity of the systems and technology environment. Over time, the IT function often provides workaround or temporary solutions to address specific needs, creating a spider web of systems where maintaining data integrity and consistency becomes increasingly difficult. Interim solutions work, but then tracking back consistently to source systems becomes more problematic. Often there is inconsistency in data definitions which causes problems later when it comes to data reconciliation. As banks work to improve their compliance capabilities, executives want better assurance that the basic data can be trusted, especially in cases where they must sign off on the financials in a way that requires them to take personal responsibility for its validity and accuracy.

Banks that are ahead of the curve are often those with centralized data management. In Asia-Pacific, for example, some leading banks have created data governance councils or similar structures to improve data quality and the processes by which the data is translated into insights to support decision making and innovation. For example, Accenture helped an Asian financial institution establish a data governance council, one important element of overall data governance and management. To make such a council work, banks could establish clear data ownership—something often difficult to achieve. Most organizations tend to delegate this management task to IT, but our experience suggests that business-driven ownership often results in better outcomes and more structured data.

Beyond establishing data governance and management, many banking leaders are moving from disparate data sources to data virtualization and data visualization to enable real-time risk monitoring. Integration, again, is key. Banks are looking to gain a more aggregated, enterprise view of data across lines of business, products and counterparties than they have today.

The effort to improve data quality controls is being driven by several factors, including regulatory pressure and the need to reduce costs


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Advanced risk analytics may involve back-testing and stress testing of the internal models or risk indicators discussed earlier. Our study found that, across all regions, more than three-fourths (77 percent) of banks use stress testing regularly to verify capital adequacy. (See Figure 11.)

Stress Testing

Stress testing is particularly prevalent among the risk analytics leaders in the banking industry. Ninety-six percent of the leaders indicate that stress testing is executed within a regular timeframe to verify capital adequacy, compared with 74 percent of laggards.

Although this sounds like a strong endorsement, in fact the majority of firms are mostly performing either regulatory stress testing or balance sheet stress testing. Certainly such testing is not without value. In the euro zone, a majority of banks that had adopted stress testing procedures passed the European Central Bank tests in 2011, demonstrating that they had adopted the right risk management capabilities to manage crisis scenarios.

In an ideal situation, banks would engage in comprehensive or integrated stress testing—taking a bottom-up approach in an integrated fashion across risk types and across capital and liquidity. This would include a combination of sensitivity measures (baseline, mild stress and severe stress) with regulatory, historical and hypothetical scenarios. Finally, the board would determine and approve the plausible scenarios and the results would factor into capital planning.

In the ASEAN region, the majority of the banks are still using regulatory capital. Only a handful of banks are using economic capital for the purpose of ICAAP or capital management. Hence most of the regulatory stress tests undertaken by the banks are on their regulatory capital.

Better and more complete stress testing will become increasingly important. Because of various world events—from the financial crisis to numerous man-made and

natural disasters—risk organizations are in many cases struggling to keep up with increased demands from senior leadership for stressed simulation, impact analysis and sensitivity analysis. In many cases, stress analyses are not performed in a timely manner.

In some cases, banks cannot produce results from a stress simulation for days or even weeks. This is in part due to an inability to bring disparate data together, either physically or virtually. Much of the data is usually still based on a month-end snapshot and may take up to a few days after month-end to be loaded into the data mart for analysis. Increasingly, banks will need risk analytics capabilities that can increase that speed—in certain conditions and in certain areas developing the ability to perform some simulations in near real time.

A more effective technology infrastructure is also critical, with complex event processing (CEP) technology to enable real-time or near real-time risk monitoring, analysis and control. These technology capabilities must also be supported with expertise, capabilities and proven methodologies.

Another challenging area is back-testing—tests performed by the credit risk analysis team prior to stress testing to see if the models are performing in accordance to reality. If the models are making incorrect predictions, banks are at risk of producing distorted and incorrect forecasts. Our study found that annual back-testing to validate model performance is being conducted by only about half of banks (54 percent). This is an important area for improvement, as having the right models and parameters in place makes a significant difference in the capital a bank needs to hold and in how it handles contingencies.

Effectiveness also depends on developing integrated stress testing capabilities rather than stringing together siloed, single-risk tests in what we could call an “aggregated stress testing” approach, which is more common. There are at best only a handful of banks that have established an integrated stress testing capability.

In the traditional, aggregated approach, stress testing is driven by the risk models used for various risk types and is usually bottom-up. For example, for credit risk stress testing, banks construct stressed scenarios; then, based on the stressed scenarios, they develop migration matrices. The matrices are subsequently used as inputs to compute the stressed credit value at risk (VaR). The stressed credit VaR can then be compared against the normal credit VaR.

Moving beyond this siloed approach to an integrated stress testing capability provides numerous benefits to banks:

• Covers both capital and liquidity, providing for a holistic view of the solvency and liquidity situations during periods of stress, thereby enabling an improved contingency funding plan.

• Covers different risk types, enabling the bank to model inter-risk correlations and understand their impacts, especially during periods of stress.

• Provides a time horizon beyond one year—typically about three years—allowing the bank to link this to capital planning and management, and the strategic planning and budgeting process.

• Improves capabilities through an iterative approach, with first-order effects, second-order effects, and so on.


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Overall

ASEAN

Europe

China

North America

Figure 11

For 77% of banks, stress testing is conducted regularly to verify capital adequacy

Stress testing is executed within a regular timeframe to verify capital adequacy

Back testing is performed at least once a year to verify the model performances

No advance techniques are applied

Advanced risk analytics may involve back testing and stress testing of the internal models or risk indicators. To what extent are advanced risk analytics techniques applied in your organization?


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Finding the Balance

Important technologies and calculation engines are now available that are critically important to the future of banks and the entire industry. At the same time, it is possible to develop an over-reliance on analytics, so a balance needs to be found.

As quoted earlier, Dr. Davide Crippa of Standard Chartered Bank highlights a critical dimension of this balance when he notes that risk analytics “is more than just mathematics and technology.” It requires, Crippa says, a balance between the quantitative sciences and sound risk management approaches.

Noel D’Cruz of OCBC Bank Ltd also stresses the importance of judgment and interpretive capabilities in risk analytics in that the conclusions drawn from a quantitative analysis “must be carefully interpreted because past trends and behaviors are not always a strong predictor of future trends and behaviors.”

Developing more comprehensive and integrated capabilities is, as we have argued, increasingly important. Integrated stress testing, for example, is an important means by which the science of risk management can be turned into more of an art, such that it can be communicated and appreciated by a wider audience. An effective stress testing framework encompasses a wider spectrum of macro-economic, social, political and environmental considerations and forecasts and so can help banks avoid the tunnel vision that can prevent them from making good decisions and taking timely action.

The Accenture 2012 Risk Analytics Study underscores the commitment banks have to improve their analytics technologies, tools and teams. At the same time, it highlights the challenges banks face—particularly in the areas of skills and integrated approaches—that need to be addressed before risk analytics becomes a mature capability.

The Accenture 2012 Risk Analytics Study underscores the commitment banks have to improve their analytics technologies, tools and teams

1 “Counting on Analytical Talent,” Accenture 2010. http://www.accenture.com/us-en/Pages/insight-counting-analytical-talent-summary.aspx

2 Analytics at Work: Smarter Decisions, Better Results. http://www.accenture.com/us-en/Pages/insight-analytics-smarter-decisions-better-results-summary.aspx


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Risk analytics is increasingly important for banks as they cope with a complex regulatory and competitive environment.

About the Authors

Based in London, Steve has more than 20 years of global experience working with clients to define strategy and execute change programs across risk management and the broader finance function.

Steve has responsibility for leading the Risk Management practice across all dimensions, from setting the strategic direction through enabling the full breadth of our corporate capabilities. In addition, he leads our efforts on large scale transformation projects across Finance and Risk (F&R) for our largest financial services clients. Prior to his current role, Steve was responsible for Accenture’s Finance & Performance Management consulting services for global banking, insurance and capital markets institutions. With his extensive risk management, performance management experience and business acumen, Steve guides executives and their teams on the journey to becoming high-performance businesses.

Steve CulpManaging Director, Accenture Risk Management

Fred KimExecutive Director, Accenture Risk Management, Banking Industry Group

Christopher LohExecutive Director, Risk Management

Alberto StoraceSenior Director, Accenture Analytics & Financial Services Lead for Italy, Greece, Central & Eastern Europe, Russia and Middle East

Fred is an executive director—Risk Management, North America banking industry lead based in Chicago. Fred has more than 18 years of consulting and industry experience in financial services and risk management across North America where he worked with global and regional banks to transform their businesses and risk capabilities. His extensive experience in risk management, business architecture and operating model strategy, credit transformation, and operational efficiency helps executives and their firms become high-performance businesses.

Based in Singapore, Christopher is the practice lead for Accenture Risk Management in Southeast Asia. He has over 14 years of industry and consulting experience in financial services and risk management across Asia-Pacific and the United Kingdom where he worked with regional and global corporations. His extensive experience in risk management, capital management, risk and regulatory compliance, operating models and strategy, banking and credit transformation, and operational efficiency helps client executives and their firms become high- performance businesses.

Based in Milan, for the last seven years Alberto has focused on implementing risk systems (for credit, counterparty and operational risk) around Basel II themes. A key contributor to the implementation of a dedicated risk analytics competency center in Milan and his specialized experience in the risk IT area, Alberto works with clients on their journey to high performance.


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About Accenture Management ConsultingAccenture is a leading provider ofmanagement consulting servicesworldwide. Drawing on the extensiveexperience of its 16,000 managementconsultants globally, AccentureManagement Consulting works withcompanies and governments to achievehigh performance by combining broadand deep industry knowledge withfunctional capabilities to provideservices in Strategy, Analytics, CustomerRelationship Management, Finance andEnterprise Performance, Operations, RiskManagement, Sustainability, and Talentand Organization.

About Accenture Risk ManagementAccenture Risk Management consultingservices work with clients to create andimplement integrated risk managementcapabilities designed to gain highereconomic returns, improve shareholdervalue and increase stakeholder confidence.

For more information about Accenture Risk Management please visit www.accenture.com/riskmanagement

About AccentureAccenture is a global managementconsulting, technology services andoutsourcing company, with more than246,000 people serving clients inmore than 120 countries. Combiningunparalleled experience, comprehensivecapabilities across all industries andbusiness functions, and extensive researchon the world’s most successful companies,Accenture collaborates with clients tohelp them become high-performancebusinesses and governments. The companygenerated net revenues of US$25.5 billionfor the fiscal year ended Aug. 31, 2011.Its home page is www.accenture.com.

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Documents

Accenture ACN 2012 Risk Analytics Study Insights for the Banking Industry