Top 4 Major Challenges of Credit Risk Management in Banks: Why It’s Important

Credit Risk Management Tools for Lenders

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 have helped mitigate some economic risk, it has left many companies 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:

1. Inefficient Data Management

Credit risk management solutions require the ability to securely store, categorise and search data based on a variety of criteria. Any database needs to be updated in real-time to avoid potentially outdated information, as well as be keyword optimised to ensure the easy location of information.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.

Centralising data storage and unsiloing data to create comprehensive datasets can provide a clearer picture of any individual or business and their creditworthiness. With all data readily available for analysis and modeling, risk can be more accurately assessed, and candidates can be qualified based on all available information.

2. Limited Group-Wide Risk Modeling Infrastructure

Sometimes it’s not enough to examine the risk qualities posed by a single entity—a broad, comprehensive view of all risk measures as seen from above is key to understanding the risk posed by a new borrower to the group. Robust stress-testing capabilities and model management that spans the entire modeling life cycle is key to ensuring accurate risk assessment.

Types of Credit Risk

Rating agencies establish credit scores for individuals, which can be utilised by banks 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, occurring 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 a combination of 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. They are favored by the most conservative, risk-averse lenders. Achieving a 360-degree view of your financial organisation’s risk across the entire group can open up new demographics for lending while maintaining a low-risk threshold.

3. 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 quickly and clearly identify strengths and weaknesses associated with a loan.

Risk analytics took an enormous stride forward when financial services giants embraced big data programs, but small and mid-sized banks and credit societies 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 optimise the processes surrounding their risk management operations. By optimising and mitigating risk factors, firms can monetise 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 optimised based on larger, more comprehensive datasets.

4. Less-than-intuitive Reporting and Visualisation

Forget cumbersome, spreadsheet-based processes—to glean the most valuable insights, data and analysis must be presented in an intuitive, clean and clearly visualised way. Stripping away irrelevant data that overburdens 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 societies can integrate analytics capabilities over a broad range of processes using risk analytics software, the end result is increased business flexibility.

However, using analytics to drive transformation requires a robust reporting process that delivers the ability to fully visualise risk both across the organisation and at an individual borrower level.

Analytics platforms must not only provide access to raw data but be able to 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 visualisation tools provide relevant data at every stage of the process, to drive increased speed, improved 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 provides the most accurate assessment of risk possible. Contact us today to find out how we can add value and security to your investments.

 

 

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