By: John Robertson Capital is the life-blood of financial institutions and has become more readily scrutinized since the global credit crisis. How one manages their capital is primarily driven by how well one manages their risk. The use of economic capital in measuring profitability enhances risk management efforts by providing a common indicator for risk. It provides pricing metrics such as RAROC (risk adjusted return on capital) and economic value added which include expected and unexpected losses consequently broadening the evaluation of the adequacy of capital in relation to the bank’s overall risk profile. The first accounts of economic capital date back to the ancient Phoenicians, who took rudimentary tallies of frequency and severity of illnesses among rural farmers to gain an intuition of expected losses in productivity. These calculations were advanced by correlations with predictions of climate change, political outbreak, and birth rate change. The primary value of economic capital is its application to decision-making and overall risk management. Economic capital is a measure of risk, not of capital held. It represents the amount of money which is needed to secure the survival in a worst case scenario; it is a buffer against expected shocks in market values. Economic capital measures risk using economic realities rather than accounting and regulatory rules, which can be misleading. The concept of economic capital differs from regulatory capital in the sense that regulatory capital is the mandatory capital the regulators require to be maintained while economic capital is the best estimate of required capital that financial institutions use internally to manage their own risk and to allocate the cost of maintaining regulatory capital among different units within the organization. The allocation of economic capital to support credit risk begins with similar inputs to derive expected losses but considers other factors to determine unexpected losses, such as credit concentrations and default correlations among borrowers. Economic capital credit risk modeling measures the incremental risk that a transaction adds to a portfolio rather than the absolute level of risk associated with an individual transaction. In a previous blog I restated a phrase I had heard long ago; “Margins will narrow forever”. How well you manage your capital will help you extend “forever”. Has your institution started using these types of risk measures? The Phoenicians did. Learn more about our credit risk solutions.  

A new comarketing agreement for MainStreet Technologies’ (MST) Loan Loss Analyzer product with Experian Decision Analytics’ Baker Hill Advisor® product will provide the banking industry with a comprehensive, automated loan-management offering. The combined products provide banks greater confidence for loan management and loan-pricing calculations. Experian Decision Analytics Baker Hill Advisor product supports banks’ commercial and small-business loan operations comprehensively, from procuring new loans through collections. MST’s Loan Loss Analyzer streamlines the estimation and documentation of the Allowance for Loan and Lease Losses (ALLL), the bank’s most critical quarterly calculation. The MST product automates the most acute processes required of community bankers in managing their commercial and small-business loan portfolios. Both systems are data-driven, configurable and designed to accommodate existing bank processes. The products already effectively work together for community banks of varying asset sizes, adding efficiencies and accuracy while addressing today’s increasingly complex regulatory requirements. “Experian’s Baker Hill Advisor product-development priorities have always been driven by our user community. Changes in regulatory and accounting requirements have our clients looking for a sophisticated ALLL system. Working with MainStreet, we can refer our clients to an industry-leading ALLL platform,” said John Watts, Experian Decision Analytics director of product management. “The sharing of data between our organizations creates an environment where strategic ALLL calculations are more robust and tactical lending decisions can be made with more confidence. It provides clients a complete service at every point within the organization.” “Bankers, including many using our Loan Loss Analyzer, have used Experian’s Baker Hill® software to manage their commercial loan programs for more than three decades,” said Dalton T. Sirmans, CEO and MST president. “Bankers who choose to implement Experian’s Baker Hill Advisor and the MST Loan Loss Analyzer will be automating their loan management, tracking, reporting and documentation in the most comprehensive, user-friendly and feature-rich manner available.” For more information on MainStreet Technologies, please visit http://www.mainstreet-tech.com/banking For more information on Baker Hill, visit http://ex.pn/BakerHill

This is the first post in a three-part series. You’ve probably heard the adage “There is a little poison in every medication,” which typically is attributed to Paracelsus (1493–1541), the father of toxicology. The trick, of course, is to prescribe the correct balance of agents to improve the patient while doing the least harm. One might think of data governance in a similar manner. A well-disciplined and well-executed data governance regimen provides significant improvements to the organization. So too, an overly restrictive or poorly designed and/or ineffectively monitored data governance ecosystem can result in significant harm; less than optimal models/scorecards, inaccurate reporting, imprecise portfolio outcome forecasts and poor regulatory reports, subsequently resulting in significant investment and loss of reputation. In this blog series, we will address the issues and best practices associated with the broad mandate of data governance. In its simplest definition, data governance is the management of the availability, usability, integrity and security of the data employed in an enterprise. A sound data governance program includes a governing body or council, a defined set of procedures and a plan to execute those procedures. Well, upon quick reflection, effective data governance is not simple at all. After all, data is ubiquitous, is becoming more available, encompasses aspects of our digital lives not envisioned as little as 15 years ago and is constantly changing as people’s behavior changes. To add another level of complexity, regulatory oversight is becoming more pervasive as regulations passed since the Great Recession have become more intrusive, granular and demanding. When addressing issues of data governance lenders, service providers and insurers find themselves trying to incorporate a wide range of issues.  Some of these are time-tested best practices, while others previously were never considered. Here is a reasonable checklist of data governance concerns to consider: Who owns the data governance responsibility within the organization? Is the data governance group seen as an impediment to change or is it a ready part of the change management culture? Is the backup and retrieval discipline — redundancy and recovery — well-planned and periodically tested? How agile/flexible is the governance structure to new data sources? How does the governance structure document and reconcile similar data across multiple providers? Are there appropriate and documented approvals and consents from the data provider(s) for all disclosures? Are systemic access and modification controls and reporting fully deployed and monitored for periodic refinement? Does the monitoring of data integrity, persistence and entitled access enable a quick fix culture where issues are identified and resolved at the source of the problem and not settled by downstream processes? Are all data sources, including those that are proprietary, fully documented and subject to systemic accuracy/integrity reporting? Once obtained, how is the data stored and protected in both definition and accessibility? How do we alter data and leverage the modified outcome? Are there reasonable audits and tracking of downstream reporting? In the event of a data breach, does the organization have well-documented protocols and notification thresholds in place? How recently and to what extent have all data retrieval, manipulation, usage and protection policies and processes been audited? Are there scheduled and periodic reports made to the institution board on issues of data governance? Certainly, many institutions have most of these aspects covered. However, “most” is imprecise medicine, and ill effects are certain to follow. As Paracelsus stated, “The doctor can have a stronger impact on the patient than any drug.” As in medical services, for data governance initiatives those impacts can be beneficial or harmful. In our next blog, we’ll discuss observations of client data governance gaps and lessons learned in evaluating the existing data governance ecosystem. Make sure to read Compliance as a Differentiator perspective paper for deeper insight on regulations affecting financial institutions and how you can prepare your business. Discover how a proven partner with rich experience in data governance, such as Experian, can provide the support your company needs to ensure a rigorous data governance ecosystem. Do more than comply. Succeed with an effective data governance program.