Is Risk Management today only about Managing Risk_
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Is Risk Management today only about Managing
Risk?
India Infoline News Service | Mumbai | March 10, 2016 13:01 IST
With increasing levels of economic volatility and global interconnectivity, a “good” economy can turn
“bad” much more quickly today than 25 years ago. Globalised economies enable companies in one
country to tap into markets in other countries, thereby reducing their exposure to local factors and
reducing point failures. However the very same interconnections also create issues where a strong local
economy does not guarantee the strength of the companies based in that economy. As per data from S&P
Dow Jones Indices, Foreign sales account for more than 40% of the total S&P 500 turnover, with 261
companies in the index tallying more than 15 per cent of their revenues outside of the United States. As a
healthy financial services sector is vital to a functioning economy it is little wonder that banks are
mandated to comply with such a wide range of global standards and frameworks, including Basel III,
which focuses on market, credit and operational risks.
The cost of compliance continues to rise. In 2013 HSBC reported that it was going to more than double
the number of people in compliance to 5,000 – a figure which has now increased to 7,000. In 2014
Deutsche Bank reported EUR1.3b in extra regulatoryrelated spending of which 400m was related to
additional staff. In 2015 Citigroup reported that about half of the bank’s $3.4b efficiency savings were
being ‘consumed by additional investments’ in regulatory and compliance activities. So what are the
banks getting for all this additional investment?
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With increased focus on riskreturn tradeoffs, risk management in banks has changed from a compliance
driven role to a business strategy defining function. In a recent study grading companies on efficient risk
management, the top 20 percent organizations were found to perform three times better on earnings
before interest, taxes, depreciation and amortization (EBITDA) than the bottom 20%. So how can
financial institutions make their risk management practice more efficient? The whitepaper aims to
highlight the key aspects of traditional enterprise risk management and how the use of analytics, can
improve the effectiveness of any risk management program by enhancing credit quality, improving credit
decisioning and enabling a 360 degree view of customer.
The Cornerstone for Effective Risk Management
Defining a multidimensional Enterprise Risk Management (ERM) framework is the cornerstone for
effective risk management. The Committee of Sponsoring Organizations of the Treadway Commission
(COSO) established an integrated framework to help banks derive business value while meeting
compliance requirements. In alignment with the framework, it is imperative that banks focus on the key
issues that form the crux for ERM.
Risk Culture
Is the set of norms and traditions that govern the behaviour of the individuals and groups of an entity to
determine how risks are identified, understood and responded. It is about being aware of ethics, best
practices and the risk appetite of the organization. In the EY report, “Shifting focus: Risk Culture at the
forefront of the banking”, 61% of the banks have aligned their risk appetite by changing their risk culture
while 74% of them termed enhanced communication of risk values to be one of the top initiatives to
strengthen the risk culture.
Risk appetite
Is the amount of risk that the firm is willing to accept in pursuit of its goals and objectives. It is
determined by the kind of risks the bank will take or accept in differing contexts. Further, risk appetite
statements with topdown or bottomup collaboration and defined metrics are crucial for embedding the
risk appetite throughout the organization. They would help in monitoring the performance of the business
groups or portfolios.
Stress Testing and Capital Management
Although stress testing is a regulatory mandate for capital planning, it can also assist the bank’s top
management in assessing the business model‘s sustainability towards market volatility and as a tool for
the strategic decision making. There is a growing necessity to refine stress testing to improve balance
sheet and P&L forecasting under different scenarios. Centralized testing models are the need of the hour
with the integration of bank’s risk and finance functions.
Risk Assessment & Reporting
This lies at the heart of the risk management framework that helps banks align their business objectives
with the risk appetite or what experts term as “embedding the risk”. There are businessintelligence tools
that provide insights into the risk profile of the banks Regulatory mandates like Basel ensure that banks
are aware of and deal with the conventional risks. However, in order to have a holistic view of the bank’s
risk, some of the nonfinancial risks like reputational risk should also be considered. In addition, the
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risk, some of the nonfinancial risks like reputational risk should also be considered. In addition, the
methodologies and the approaches adopted by the banks should neither succumb to the regulatory
pressure nor should they overly rely on backward looking models. Forward looking approaches by
considering varied scenarios help banks in being prepared for contingencies. It provides a total
understanding of the top risk drivers and throws light at the root causes and early warning signals.
Evolution of Predictive Analytics
With the exponential growth and availability of data, both structured and unstructured, big data comes
into the picture and can be combined with historical transactional data to uncover new opportunities.
CROs across the globe are looking to use structured and unstructured data to make accurate risk
predictions along with understanding the potential impact of a range of risks. They are also looking at
linking them better to the organization’s strategy. Currently, there are several challenges impeding the
banks from applying ERM effectively. For instance, extracting and aggregating data continues to be the
top challenge in improving stress testing. Credible risks quite often go unnoticed. The intrinsic
challenges in risk management necessitate a more cohesive ERM solutionsomething can be made
possible with the usage of risk analytics. While analytics previously was synonymous with business
intelligence, today the level of sophistication has increased with more focus on data exploration,
segmentation, statistical clustering, predictive modelling and event simulation & scenario analysis
leading to better insights. By embedding predictive analytics into the ERM delivery approach,
organizations can monitor performance through risk sensitivity analysis, model key risk events scenarios,
and become more risk intelligent in developing intervention and mitigation strategies. It helps the bank
chart the best course of action for the future. Pricing decisions can be made by the use of analytics
thereby giving a deeper understanding of risks. The bank can also use analytics to fight against credit risk
and manage their portfolios optimally.
Driving Effective Risk Management in Financial
Organizations
Enhancing Credit Quality
With deteriorating credit quality, addressing credit risk primarily due to default has become the top
most priority for the banks. This has resulted in an increased focus on internal stress testing over the past
12 months. Traditionally, banks rely heavily on the credit bureau’s score for making a loan decision or,
in the absence of a credit bureau, on internal scoring models. However, scoring models from credit
bureaus and internal scoring models are based on the historical credit profile of the borrower which may
not accurately reflect the current situation and therefore might not help the underwriter make an informed
decision. This may lead to turning down potential clients which reduces profits and may damage the
bank’s reputation. On the other hand, accepting nonworthy businesses will make matters worse by
creating future Non Performing Loans.
Improving Credit Decisioning
In credit risk modelling, scoring models are developed using stateoftheart statistical techniques and
data aggregation from the bank’s archives. Predictive Analyticsbased scorecards allow the bank to
rapidly identify which loans should to be approved, which loans should be rejected and which loans
should be subject to further investigation. The decision process for loan approval or rejection becomes
more robust by devising a decision map using both the model score and the score from the credit bureau.
Enabling a 360o View of Customer
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Consider a customer who has a medium Credit Bureau score as well as a medium risk model score. His
case, by default, falls into the Refer/on hold bucket of the business risk strategy map created using
statistical scoring models. In such a case, the underwriter usually sends the application for further field
investigation leading to increase in time and costs. In the meantime the customer may decide to take loan
from some other bank and thereby the first bank loses a potential good customer.
By combining big data and highpowered analytics, it is possible to:
Create a unified view of the customer covering all his/her touch points including web crawling data, call
centre interactions, social media activities, branch interactions etc.
Recalculate entire risk portfolios in minutes
Quickly identify valuable customers
Detect fraudulent behaviour using clickstream analysis and text mining
By leveraging big data in the underwriter decision making stage, the decisions for refer/on hold
applications can be made after analysing the current behavioural and risk patterns of the customer. The
amount of investigations for on hold applications is reduced thus bringing down the time and costs
involved and freeing up people to focus on more important activities. In addition, fraudulent customers
can be detected easily as well.
The Rise of Social: More Data More Insights
Social media has changed the way people interact and firms across the globe are trying to leverage social
data in their efforts to stay ahead of competition. Social Network Analysis (SNA) (Exhibit 7) includes
pattern analysis and network linkage analysis to uncover the large amount of data that can be linked to
show relationships. To gain customer insights, one looks for clusters and how those clusters are linked
with the other clusters. Public records such as social media behaviour, address change frequency,
criminal records and foreclosures are all data sources that can be integrated into the model.
This will generate many insights at the time of underwriting and therefore the credit decision process can
be enhanced substantially. By integrating this with transactional systems, even fraud risks can be
mitigated in real time. Exhibit 8 shows the mechanism of risk modelling with SNA.
While some banks have begun to see real benefits of these enormous data sources, many are still working
in isolated silos. Others, while having a multidimensional and integrated ERM framework, are still not
utilizing predictive analytics at the optimal level. With the exponential growth and availability of data,
banks can gain a strategic advantage by using predictive analytics to make improved risk predictions that
are better aligned to current and future economic conditions, and hence quickly adjust to dynamic market
conditions and steer their portfolios through uncertain times.
How can Nucleus Help?
Nucleus Lending Analytics is designed to provide comprehensive business insight into credit risk
management of banks and other financial institutions. The solution uses sophisticated credit scoring
models to allow credit risk managers and credit analysts create predictive scorecards. It also incorporates
defined metrics that provide a unified view of customers across lines of businesses and channels. The
solution focuses on the three key tenets of efficient risk management in lending: Informed Decisioning,
Enhanced Portfolio Management and Fraud.
About the Authors
