This entry has a more recent update; check this out here: 10 Major Challenges of Credit Risk Management in Banks
Major Challenges To Successful Credit Risk Management
With the global financial crisis still recent, credit risk management is still the focus of intense regulatory scrutiny. While stricter credit requirements as a “top-down” approach has helped mitigate some economic risk, many companies are struggling to overhaul their approach to credit risk assessment.
In the scramble to implement risk strategies to improve overall performance and secure a competitive advantage, a company must overcome significant credit risk management challenges, such as inefficient data management, limited group-wide risk modeling infrastructure, lacking risk tools, less-than-intuitive reporting and visualization:
Inefficient Data Management
Credit risk management solutions require the ability to securely store, categorize and search data based on various criteria. Any database needs to be updated in real-time to avoid potentially outdated information and be keyword optimized to ensure the easy location of information.
Getting data out of silos and into models
Data in the financial industry can be highly siloed, causing inefficiencies across the lending life cycle. Siloed data can mean vital information is overlooked at a critical step in the lending process or that data is duplicated, causing difficulty in risk modeling. Streamlining how data is gathered, segmented, stored, and accessed is critical to effective risk management.
Centralizing data storage and un-siloing data to create comprehensive datasets can provide a clearer picture of any individual or business and their creditworthiness. Risk can be more accurately assessed with all data readily available for analysis and modeling, and candidates can be qualified based on all available information.
Limited Group-Wide Risk Modeling Infrastructure
Sometimes it’s not enough to examine the risk qualities a single entity poses—a broad, comprehensive view of all risk measures, as seen above, is key to understanding the risk a new borrower poses to the group. Robust stress-testing capabilities and model management that span the entire modeling life cycle are key to ensuring accurate risk assessment.
Types of Credit Risk
Rating agencies establish credit scores for individuals, which banks can utilize to help determine default risk. The following types of risk may be relevant to the decisioning process.
Credit spread risk
Generated by the shifting difference between interest rates and the risk-free return rate.
Default risk
Occurring when borrowers become unable to make contractual payments.
Downgrade risk
Generated by dropping issuer risk ratings.
Concentration/industry risk
It occurs when there is too much exposure to any single industry or sector of investors.
Institutional risk
Generated by breakdowns in legal structure or entities overseeing contractual agreements between a lender and debtor.
Calculating Credit Risk
Predicting who will default on loans is a major factor in measuring credit risk. There are two primary ways to calculate risk.
- The first method combines exposure (the total amount intended to be collected over the life of the loan), default probability, and loss rate (the percentage of the exposure that can be collected if the debtor defaults).
- The second method is based on the borrower’s FICO score, debt-to-income ratio, and potential debt (available credit).
These methods of calculating risk are fairly rigid and create tight, inflexible parameters. The most conservative, risk-averse lenders favor them. A 360-degree view of your financial organization’s risk across the entire group can open up new demographics for lending while maintaining a low-risk threshold.
Lacking Risk Tools
Identifying portfolio concentrations or re-grade portfolios is essential to ensure you’re seeing the big picture. A comprehensive risk assessment scorecard should be able to identify strengths and weaknesses associated with a loan quickly.
Risk analytics took an enormous stride forward when financial services giants embraced big data programs. Still, small and mid-sized banks and credit unions found adoption slower due to the investment involved. Thanks to modern tools now available to institutions of all sizes, big data in banking is more accessible than ever.
Risk analytics can be considered a transformation, allowing banks to align their strategy, governance, culture, business models, and technology to optimize the processes surrounding their risk management operations. By optimizing and mitigating risk factors, firms can monetize the risk management process and strengthen their returns from lending practices.
This transformative shift in financial services is made possible by accessible analytics, improved infrastructure, strategic partnerships with data clearinghouses, and credit scorecard systems optimized based on larger, more comprehensive datasets.
Less-than-intuitive Reporting and Visualization
Forget cumbersome, spreadsheet-based processes—to glean the most valuable insights, data and analysis must be presented intuitively, cleanly, and visually. Stripping irrelevant data that overburdened analysts and IT can help zero in on the most pertinent information.
Modern risk management tools present user-friendly front-end interfaces and flexible back-end configurations. When banks and credit unions can integrate analytics capabilities over a broad range of processes using risk analytics software, the result is increased business flexibility.
However, using analytics to drive transformation requires a robust reporting process that can fully visualize risk across the organization and at an individual borrower level.
Analytics platforms must provide access to raw data and spot patterns, and create models that deliver meaningful insights, allowing users to make the business case for digital transformation. Meeting major challenges in credit risk management means ensuring data reaches the right people at the right time to drive informed decisions in every area of the lending landscape.
Implementing smart data modeling for decisioning and leveraging alternative data sources throughout your credit application processing system is a holistic approach to risk analytics. Comprehensive reporting and visualization tools provide relevant data at every process stage to drive increased speed, efficiency, and data-driven decision-making.
Fintech disruptors are reaching new markets and influencing customer expectations in all established lending segments. To maintain a competitive edge, traditional lenders must be fully ready to embrace digital solutions to meet these challenges.
The credit risk management software and analytics solutions offered by GDS Link are state-of-the-art and provide the most accurate risk assessment possible. Contact us today to learn how we can add value and security to your investments.
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