Operational Risk Management Systems for Financial Services 2014

<p>According to an OCC review of examined bank risk, operational risk has overtaken credit risk as the most&nbsp;important risk type, and the LIBOR scandal, product mis-selling, fraud in ETF and securities financing markets&nbsp;in Europe have brought it to the forefront. With further neighboring disciplines being incorporated into and&nbsp;extending on from operational risk management, it occupies a position at the core of the modern risk function.</p>

<p>Chartis has noted a number of trends in the demand side, which are listed below:</p>

<p><strong>Preparing for future crises:</strong> The definition of a safe financial institution has changed - it is not one which takes&nbsp;no risks or is risk-compliant, but one which is organizationally and technologically able to withstand the crises&nbsp;of the future.</p>

<p><strong>Observation bias: </strong>The low-frequency, high-severity events cannot be accounted for by traditional operational&nbsp;risk systems, yet firms continue to believe that their risk metrics are effectively managing operational risk. The&nbsp;observational bias of measuring risk in a simplistic manner, and then assuming that these simplistic views of risk&nbsp;are accurate, is one of the biggest dangers facing the financial services sector.</p>

<p><strong>The shortcoming of compliance-based operational risk: </strong>There is a growing divergence between riskmanagement and compliance - regulation is backwards-looking, and will be unable to capture the risk events of the future.</p>

<p><strong>No respite from regulation: </strong>The regulatory cost of operational risk continues to increase in both direct and&nbsp;indirect ways. The costs of databases and data models and non-compliance are coupled with the transformation&nbsp;of market and counterparty risks into operational risk.</p>

<p><strong>Allocation and optimization:</strong> It is critical to optimize the risk taken in the context of the risk appetite of&nbsp;shareholders. Operational risk is becoming a trigger for firms to exit or modify their business portfolios.</p>

<p><strong>The future of operational risk:</strong> The market leaders in operational risk will be those looking ten years or&nbsp;more into the future. The scope of operational risk will widen and come to primacy as the most important risk&nbsp;discipline, incorporating and closely linking with other risk disciplines such as conduct risk, model risk, and&nbsp;reputational risk, and new systems focused on discovery and “unknown unknowns” within data sets will detect&nbsp;the black swan risks just around the corner.</p>

<p>Within the supply side of operational risk management systems, Chartis has observed the following trends:</p>

<p><strong>The range of solutions and functionalities offered by vendors can make it difficult for buyers to decide&nbsp;which solution best suits their operational risk needs.</strong> Consolidation of vendors, increasing requirements and&nbsp;demands from buyers, and continued innovation has altered the operational risk technology landscape.</p>

<p><strong>Operational risk systems are increasingly including cyber-security.</strong> With the increased connectivity of&nbsp;modern banking, including cloud-based and mobile solutions, the potential cost of cyber-security failures has&nbsp;increased dramatically and is swiftly becoming a priority.</p>

<p><strong>Consolidation of systems:</strong> Although some firms are utilizing separate systems for operational risk requirements,&nbsp;cost and complexity pressures are driving them to consolidate into enterprise operational risk platforms.</p>

<p><strong>Linkage of systems:</strong> As an extension of the above trend, neighboring disciplines are being incorporated into&nbsp;operational risk such as audit, compliance, and financial crime. These in turn are leading to the incorporation of&nbsp;advanced analytics from these systems.</p>

<p><strong>Advanced analytics: </strong>Traditional operational risk models have focused on over-fitting and data adequacy. New&nbsp;research methodology is focusing on a variety of analytical methodologies, such as factor based models, fuzzy&nbsp;logic systems, and concept maps.</p>

<p><strong>Quantification:</strong> The sensitivity of loss distribution fitting processes, together with paucity of data, have led to&nbsp;new quantification methods, including graph and network-based models, phylogenetic comparison methods, and<br />
varied network topology.</p>

<p><strong>Model granularity: </strong>Models are becoming increasingly granular, and moving lower in the business lines.&nbsp;Methods of model design are being explored to make numbers more meaningful to specific business lines.</p>

<p><strong>Continuous data: </strong>The interlinked and ad-hoc nature of events has led to the creation of more continuous data and event models, including near misses and focused KRIs.</p>

<p><strong>Loss data collection: </strong>A broad set of internal, external and industry standard data is driving the creation of loss&nbsp;data collection processes, which can condition future expected losses and risk mitigation controls.</p>

<p><strong>Separation of systems, architecture and content:</strong> As every element of an operational risk framework is&nbsp;increasingly representable as data, the move is towards production of elements such as KRIs as standardized&nbsp;data sets. Vendors are increasingly enabling the delivery of business focused data and rules in clear packages.</p>

<p>This report uses Chartis’s RiskTech Quadrant^® to explain the structure of the market. The RiskTech Quadrant^®&nbsp;uses a comprehensive methodology of in-depth independent research and a clear scoring system to explain&nbsp;which technology solutions meet an organization’s needs. The RiskTech Quadrant® does not simply describe one&nbsp;technology solution as the best operational risk solution; it has a sophisticated ranking methodology to explain&nbsp;which solutions would be best for specific buyers.</p>

<p>This report covers the leading vendors offering operational risk solutions for financial institutions, including&nbsp;Chase Cooper, Empowered Systems, IBM, MEGA, MetricStream, NASDAQ BWise, Oracle, Optial, Prometeia,&nbsp;Protiviti, SAP, SAS, Thomson Reuters, Wolters Kluwer, and Wynyard.</p>

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• Topics
• Quantitative Methods
• AI & Machine Learning
• Programming Languages
• Workflow/RPA
• GRC and Operational Risk