With increased focus on risk-return trade-oﬀs, risk management in banks has changed from a compliance driven role to a business strategy defining function.
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 regulatory-related 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? With increased focus on risk-return trade-oﬀs, 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 multi-dimensional 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 top-down or bottom-up 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 business-intelligence 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 non-financial 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 solution-something 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 non-worthy businesses will make matters worse by creating future Non Performing Loans. Improving CreditDecisioning In credit risk modelling, scoring models are developed using state-of-the-art statistical techniques and data aggregation from the bank’s archives. Predictive Analytics-based 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 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 high-powered 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. 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.