Tag Archives: Model Management

Risk & Compliance Are Your Analytics Delivering Results?

analytics delivering results

Are your analytics delivering results? The word “analytics” means different things to different people.  Depending on the analytical maturity of your organization, analytics could mean reports on your performance, analytics could mean predictive models, or it could mean fully optimized analytic decisions. No matter where you are on that spectrum, many organizations report that while they have many different analytical systems or models, they don’t know how well they are performing.  Many times organizations implement expert or predictive models with the expectation of enhanced operational performance, but they don’t measure the results, and don’t assess if the model is delivering the business value needed and expected. Measuring and tuning models as is important as implementing models.  Without ongoing monitoring they can fail to achieve the desired results.  If you, as a leader of an organization wants to assess your analytics, there are a number of steps you can take. 1)... [Read More]

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Analytics & Optimization Is Your Analytics Supply Chain Broken?

Simplified view of an analytics supply chain

This is a guest post from Thomas H. Davenport and FICO’s Zahir Balaporia. A version of this post was also published on Data Informed. Businesses across many industries spend millions of dollars employing advanced analytics to manage and improve their supply chains. Businesses look to analytics to help with sourcing raw materials more efficiently, improving manufacturing productivity, optimizing inventory, minimizing distribution cost, and other related objectives. But the results can be less than satisfactory. It often takes too long to source the data, build the models and deliver the analytics based solutions to the multitude of decision makers in an organization. Sometimes key steps in the process are omitted completely. In other words, the solution for improving the supply chain –  advanced analytics – suffers from the same problems that it aims to solve. Therefore, reducing inefficiencies in the analytics supply chain should be a critical component of any analytics initiative... [Read More]

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Risk & Compliance Explore Regulatory Compliance Best Practices at FICO World

FICO World logo

During this period of Congressional gridlock, much of the activity in Washington DC impacting the financial services industry remains focused on the development and implementation of new regulations. As FICO’s premiere client conference, FICO World 2016, heads to the nation’s capital at the end of next month, it is not surprising that regulatory compliance strategies and solutions will be a hot topic of discussion. This year’s compliance sessions complement an action-packed FICO World, which will showcase 80+ breakout sessions grouped into 12 subject matter tracks. The Regulatory Compliance track will feature an impressive list of industry experts sharing best practices that address a diverse range of high-priority regulatory challenges. Here’s a very brief overview of what we’ll cover: Stress testing is a key area of emphasis for regulators around the globe. A panel of experts will discuss how an institution’s stress testing activities should go beyond just meeting regulatory compliance... [Read More]

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Risk & Compliance Model Risk 101: A Checklist for Risk Managers


In my previous Model Risk 101 blog post, I discussed how the first line of defense – model developers and users – are tasked primarily with implementing a consistent, formalized model development approach. That’s a challenge in and of itself – but model risk management and governance doesn’t end there. Once in production, predictive models are subject to performance erosion driven by changes in portfolio risk characteristics or availability of economic data. Across the model lifespan, it is therefore critical to answer questions such as: “Are these models still fit for purpose?” or “What corrective actions are needed in case of model performance breaches?” That’s where your second line of defense comes in. For an integrated model risk management system to work, the second line – primarily risk managers – must go beyond periodic validation to ensure that model use and performance remain in alignment with the portfolio risk profile,... [Read More]

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Risk & Compliance Model Risk 101: A Checklist for Model Developers


I’ve been blogging about the need to ensure checks and balances are in place across the entire model risk management and governance process, an approach often referred to as the “three lines of defense.” In this post, I’ll focus on the first line of defense – model developers and users – providing some insights for enhancing productivity and effectiveness. While banks strive to improve the efficacy of their models and the efficiency of their model development processes, few have implemented effective processes to: a) identify and prioritize models for redevelopment, and b) ensure that all modeling teams are following a standard process. As a result, while model quality at the individual or department level may be generally solid, areas such as documentation, variable usage, data inputs and assumptions, approvals, and other processes will likely vary, sometimes substantially, thereby introducing the potential for both adverse regulatory and business risk. In addition,... [Read More]

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Risk & Compliance Chartis Recognizes FICO in Operational Risk Management


Despite increasing resources dedicated to operational risk management, it continues to burden banks—to such an extent that a recent Basel Committee review states: “Overall, banks have made insufficient progress in implementing the Principles originally introduced in 2003 and revised in 2011.” According to a recent independent research published by Chartis, “Operational Risk Management Systems for Financial Services,” a staggering 98% of the top 50 operational risk losses in the previous 12 months – amounting to $60 billion – were due to conduct risk (specifically focusing on suitability or fiduciary failures and improper business or market practices). A theme common to many risk loss events is lack of tools and processes to help align and drive operational risk management. In fact, while banks continue to invest scarce resources in boosting areas such as stress testing and capital adequacy, the efforts committed to compliance aren’t necessarily being leveraged within the overall risk... [Read More]

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Risk & Compliance Best Practices in Capital Adequacy Reform


We’ve been blogging about how financial institutions are struggling to implement many critical best practices around model management and capital adequacy, including many practices mandated by regulators. This is exemplified by a recent survey of global FIs, which we conducted with Chartis. In many best practice areas, larger institutions are at the vanguard, perhaps because they are under greater regulatory scrutiny. However, these larger institutions still have major issues to address, and smaller FIs should anticipate that greater scrutiny is on its way. Comparison of larger and smaller FIs in best practice areas Source: Chartis Research 2015 Among the numerous challenges reported by survey participants, data quality issues still plague many institutions. Our survey found that approaches to data quality varied across organizational type. Generally, Tier 1 institutions delegated the responsibility to individual business lines, whereas smaller institutions have greater centralization of responsibility around data quality and data strategy. Leaders... [Read More]

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Risk & Compliance Survey: Major Gaps in Model Risk Management


We’ve been blogging about our recent survey, conducted with Chartis, where we queried global financial institutions (FIs) about their capital adequacy programs. As part of the survey, we asked FIs about their practices in model risk management. What we discovered is that following industry-standard best practices is the exception, not the norm. Regulators mandate that all financial institutions have adequate processes in place for model risk oversight and control of every analytic model utilized throughout the credit lifecycle. Industry best practice is to develop such practices using a tri-layered defense: Solid and effective controls exerted by the business, including formal processes for definition, development, implementation and ongoing monitoring of models. Enterprise risk functions and committees that establish standards for model governance, validation and monitoring of adherence to established policies. Independent audit and assessment of both the design and effectiveness of the controls and policies from the first two lines of... [Read More]

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Risk & Compliance Capital Adequacy Programs: Untapped Opportunities


While globally financial institutions (FIs) are working to meet regulatory requirements, it’s demanding an unprecedented spend on human resources and technology. Our recent survey of such institutions, conducted jointly with Chartis, indicated that many are simply overwhelmed by the number of regulations with which they must comply. The unfortunate consequence is that, for many, planning around stress testing and capital adequacy has become a reactive exercise. Quite simply, that’s a missed opportunity. In fact, by adopting efficient capital-focused strategies, FIs can drive improvements in risk management across the business, particularly in retail banking and capital markets. Some of the best practices reported by survey participants included: Establishing and enforcing enterprise standardized model risk management practices; using industry standards, benchmarks and calibration tools to consistently compare, contrast and report on portfolio performance. Optimizing retail product portfolios for low capital consumption, by restructuring products and exiting from unprofitable business lines. Focusing on... [Read More]

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Analytics & Optimization Improving Model and Data Governance with Auto-Encoders

Binary code

One of our latest innovations in fraud and cybersecurity addresses a fundamental issue affecting predictive analytics: Data doesn’t sit still. What do I mean by that? As any data scientist can tell you, a model development project begins with the lengthy process of collecting, identifying, cleansing and normalizing data. This is often the longest part of the process of building a model. There are many checks of data integrity to ensure proper data quality. The paradox is that the data we’re spending so much time working with isn’t necessarily the data we really care about. The data we really care about is the data that the model will analyse in the future — which will be different than the data we’re studying to build the model. Analytics methodologies include data governance to ensure that a model developed on today’s data will make good predictions based on future data. For instance,... [Read More]

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