The Future of Model Risk Management for Financial Services Firms

Banks have been using credit scoring models for decades, but since the financial crisis of 2008, regulators have formalized the discipline of model risk management (MRM), driving the need for more rigorous, enterprise-level model information management. Regulators now want to evaluate bank models to access their trustworthiness – not blindly accept the numbers they generate. This paper explores how next-generation MRM is integral to successfully running a financial services business – both for compliance and decision making purposes. Learn why decision makers today are judged not just on outcomes, but on the processes and decision support tools they use to realize them. And see why it’s absolutely critical that your firm be able to manage ever-growing numbers of models – what’s needed to do that effectively.
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Discovering the Business Value of Streaming Analytics

Many analytics and BI tools limit your ability to get insight in time to make a critical business decision. Once you detect a pattern, you have to work with a data scientist to choose data sets for more analysis, clean the data of noise, and code a query, all while the data becomes less and less relevant with passing time.

This resource explains streaming analytics and describes how it can enable real-time decision-making based on current evidence. Learn how you can resolve business problems more quickly and make data-driven decisions.
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Thirsting for Insight? Quench It With 5 Data Management for Analytics Best Practices

“Unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics is full of challenges. In fact, most data scientists spend 50 to 80 percent of their model development time simply preparing data. SAS adheres to five data management best practices that provide access to all types of raw data and let you cleanse, transform and shape it for any analytic purpose. As a result, you can gain deeper insights, embed that knowledge into models, share new discoveries and automate decision-making processes across your business.
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Crossing the IDMP Data Chasm

The IDMP data chasm is a comprehensive and demanding challenge – but it can be crossed with the right preparation, approach and solution. This white paper highlights eight IDMP challenges, and how they can be addressed with an IDMP data hub solution from SAS. As a trusted advisor, it includes the SAS recommendations on IDMP in an MDM context, and finally broadens the perspective by looking beyond the approach of solely adopting IDMP for compliance.
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Ptak Associates Report: BMC’s NGT = DB2 Utilities for the 21st Century

In this analyst report, you’ll learn how the operational demands of the digital age will require next generation DB2 management technology that can optimize infrastructure performance and deliver excellent response to customers.
Read the report to:
  • Learn about the operational challenges of dynamic databases
  • Explore how next generation technology can enhance data management
  • See how BMC can cut DB2 management costs while improving application availability
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New Approaches to Cut Mainframe Operating Expenses

To get more from your IT budget, you need to get mainframe MLC costs under control - without sacrificing service delivery. This eBook presents tools and solutions to help you intelligently optimize when and where workloads run to improve efficiency and lower costs.

You'll learn how to:

  • Make intelligent decisions about utilization with full visibility into business impact
  • Recover underutilized capacity and reduce your rolling peaks
  • Optimize where your subsystems run to eliminate excess DB2 and IMS instances
  • Lower monthly license charges by up to 20 - 30 percent
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“Optimizing Monthly License Charge Costs To Better Leverage Your Mainframe”, A commissioned study conducted by Forrester Consulting on behalf of BMC, February 2016

BMC commissioned Forrester Consulting to conduct an in-depth study of IT and senior corporate managers in US enterprises with more than 1,000 employees to evaluate mainframe strategies and how they impact mainframe operating costs. Forrester found that by optimizing mainframe Monthly License Charge (MLC) costs, these companies have lowered operational costs and increased automation, which improved their productivity. Learn about four recommendations to help you save.
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Adapt to Digital Business with Single System Image (SSI) Monitoring Capabilities for Mainframes

Meet the demand for performance and availability with simpler mainframe monitoring.

Finding and resolving issues is critical to keep revenue-generating digital services running their best. In this paper, you’ll learn how to achieve a single, simpler view across your mainframe environment before performance and availability issues impact your business.

Read the white paper to:

  • Improve the efficiency and productivity of mainframe staff
  • Maintain performance despite growing complexity
  • Shrink MTTR, reduce system outages and lower TCO

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Five Database Must-Haves for Meeting Evolving Needs

By 2017, 80% of enterprises will have adopted a hybrid IT strategy composed of public cloud, on-premises and private cloud components. In an increasingly “cloud-first” era, agile, scalable infrastructure matters more than ever.

This Battlecard looks at the changing nature of business, the growing importance of data to all parts of the enterprise, and offers five key considerations when evaluating database options for tomorrow’s enterprise.

Key points will include

  • Supporting Next-Generation Applications
  • Big Data and the Enterprise
  • What Hybrid means to the database
  • The Economics of Cloud
  • Security and the Database

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Deep Learning – The Next Evolution in Programming

Even if you haven’t recognized it, you’ve benefited from deep learning. Deep learning is an advancement essential to cognitive computing, as it partners computers with humans to help businesses automate labor-intensive processes. This progressive approach allows for a more advanced interpretation of data, and the effects are astounding. With the growth of the Internet and the shift to online business, enterprises worldwide struggle everyday to make sense of the staggering amount of unstructured data. Deep learning offers a solution.
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Data Sheet: Hadoop-as-a-Service

IBM® BigInsights™ on Cloud provides Hadoop-as-a-service on IBM’s SoftLayer® global cloud infrastructure. It offers the performance and security of an on-premises deployment without the cost or complexity of managing your own infrastructure.

BigInsights is an industry standard Apache Hadoop offering that helps enterprises cost-effectively manage and analyze big data.

BigInsights on Cloud provides the following features and benefits:
  • Managed operations provide 24 x 7 monitoring
  • IBM Open Platform with current and stable Apache Hadoop components
  • Dedicated bare metal nodes for enhanced performance, data privacy and security
  • High value in-Hadoop analytics features, including Big SQL, Big Sheets, Text Analytics, Big R and Machine Learning

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DataWorks Solution Brief

IBM DataWorks is a fully managed data preparation and movement service for IBM Cloud Data Services. It enables business analysts, developers, data scientists and engineers to put data to work with a simple yet powerful cloud based interface. DataWorks gives both technical and non-technical users across the enterprise a point and click solution to discover, cleanse, standardize, transform and move data in support of application development and analytic use cases.
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Foundational Methodology of Data Science

In the domain of data science, solving problems and answering questions through data analysis is standard practice. Often, data scientists construct a model to predict outcomes or discover underlying patterns, with the goal of gaining insights. Organizations can then use these insights to take actions that ideally influence future outcomes for the better. The flow of IBM's data science methodology ensures that as data scientists learn more about the data and the modeling, they can return to a previous stage to make adjustments, iterate quickly and provide continuous value to the organization.

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