Skilled credit risk managers are a finite resource

by Guest Contributor 3 min read February 2, 2016

Understanding the behaviors of best-in-class credit risk managers

For financial institutions to achieve superior performance, having the appropriate set of credit risk managers is a prerequisite. The ability to gain insight from data and customer behavior and to use that insight for strategic advantage is a critical ingredient for success. At the same time, the risk-management community is under increasing pressure to understand and explain underlying trends in credit portfolios — and to monitor, interpret and explain these trends with ever-greater accuracy.

A common problem financial institutions face when confronting staff resource needs is the difficulty in recruiting and retaining experienced risk-management professionals. The risk-management community is notoriously small, and hiring expertise from within this community is extremely difficult. Skilled risk managers truly are a finite resource, but their skill set is in huge demand.

Hiring the right talent is crucial to job satisfaction, leading to higher engagement levels and reduced attrition costs. On top of that, employee engagement is vital to an organization’s success. It drives employee productivity and fosters a culture of innovation, which leads to higher profitability for the entire organization. Building, attracting and retaining risk-management resources requires a commitment to engaging in staff personal development. A great way to support employee engagement is to invest in their personal and professional development, including opportunities for training and team building. If an organization can show that it is committed to developing its people and providing opportunities for career growth, employee engagement levels will rise, with all the benefits this entails.

Typically, financial institutions bridge the resource skill gap by either hiring skilled statistical and analytical experts or developing in-house resources. Both of these approaches, however, require significant on-the-job training to teach employees how to link raw statistical techniques and procedures to influencing the profit and loss statement of the business line which they support. The challenge is often broadening the understanding of these skill set “silos” and their contribution to the overall portfolio. By opening that view, the organization generates additional value from these resources as lines of communication are improved and insights and opportunities found within the data are shared more effectively across the organizational team.

Experian’s Global Consulting Practice provides a solution to this problem.

Our two-day Risk and Portfolio Management Essentials training workshop offers the opportunity to understand the behaviors of best-in-class risk managers.

  • What are the tools and enablers required for the role?
  • How do they prepare for the process of managing credit risk?
  • What areas must risk managers consider managers across the Customer Life Cycle?
  • What differentiates the good from the great?

To complement the training modules, Experian® offers an interactive, team-based approach that engages course participants in the build options of a defined portfolio. Participants leverage the best-in-class techniques presented in the sessions in a series of competitive, team-based exercises. This set of cross-organizational exercises drives home the best-in-class techniques and further builds understanding that resonates across the organization long after the course is concluded.

For our current offerings, locations and to register click here.

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When a credit view and a fraud view are generated separately, they must be reconciled instead of read together in the same instant. Every handoff is a place where a decision slows down and a signal gets missed. It's why a fraud pattern that would be obvious next to the credit file often surfaces only after the funds are already gone. Fragmentation also makes it harder to optimize lending strategies over time. When data, analytics and decisioning are disconnected, credit unions have less visibility into what's working, what's changing and where strategies need to be adjusted. An integrated decisioning ecosystem closes those seams by design, with credit, fraud and lending strategy running on one platform and accessed through a single Application Programming Interface (API). Identity and compliance capabilities can be an integrated decisioning flow. The data doesn't need to be reconciled after the fact because it was never separated in the first place. Stop overengineering, start removing friction When decisioning underperforms, the instinct is to build something more custom: a proprietary model, tighter segmentation, more configuration.  Custom modeling has its place for institutions with the scale and data to justify it. But often, for small or mid-sized credit unions, the bigger opportunity is a flexible decisioning strategy that uses a strong, continuously refreshed model. This shouldn't be either/or. The right approach gives credit unions a strong decisioning strategy today, with the ability to introduce more customized analytics and modeling as their needs evolve.  The same principle applies to strategy management: credit unions can gain greater control through self-service capabilities as they build a more sophisticated data science team.   That kind of flexibility is important as credit unions modernize their decisioning infrastructure. 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A smaller number are flagged for quick step-up verification and cleared within minutes. Only genuinely ambiguous cases reach an underwriter, who is working from one complete picture instead of reconciling multiple signals. The member has an answer before the weekend is over. The credit union books the loan before competitors even open on Monday. Built to scale This kind of process holds up today. However, the better test is whether it holds up eighteen months from now when application volume has grown, new products are live and the data flowing through the system looks nothing like it does right now. Future-proofing isn't only about handling more volume. It's about having room to be nimbler and make adjustments as quickly as the market changes. A credit union that relies on standard models within a decisioning strategy may want more customized analytics and greater control over its strategies tomorrow. 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