In his book, The Gates of the Forest, Nobel-prize-winning author Elie Wiesel wrote ‘God made man because he loves stories.’ The abundance of myths worldwide seems to prove the author’s point! From the popular view that Napoleon Bonaparte was a short man (he was actually of average height) to the belief that milk can worsen your cold symptoms (it doesn’t), misunderstandings and myths abound in almost every sphere of life. And when a subject is as complex as credit risk monitoring, there are bound to be a few misconceptions around it. Like with most misconceptions, it is important to set the record straight here. So, here are 10 common misconceptions about credit risk monitoring along with their corresponding truths.
10 common misconceptions about credit risk monitoring
Misconception #1: Managing risk is all about credit origination
Misconception #2: Monitoring risk is an occasional activity
Misconception #3: Preventing loss is a credit risk manager’s only goal
Misconception #4: Credit risk monitoring is purely speculative in nature
Misconception #5: Analyzing historical data is the best way to predict risk
Misconception #6: False positives and negatives are a given in credit risk monitoring
Misconception #7: For effective credit risk monitoring, you need a large team
Misconception #8: Compliance and risk monitoring are one and the same thing
Misconception #9: All available software solutions are the same
Misconception #1: Managing risk is all about credit origination
The reality: Ongoing monitoring is just as important as initial vigilance, if not more.
There’s no doubt that assessing your borrower’s creditworthiness right off the bat is a pivotal step in mitigating risk. However, as the popular maxim goes, it’s not how you start that matters, but how you finish. The reality is that your borrower’s fortunes can change very quickly for a variety of reasons ranging from poor internal organization to overall market collapse. A recent example of this is Quibi, a streaming service platform that went from hero to zero in the space of just 6 months. The Netflix-wannabe platform raised an impressive $1.75 billion and launched its app in April 2020. Just 6 months later, it shut down due to a lack of subscribers.
So, loan origination is just the tip of the iceberg in terms of the work that needs to be done to manage credit risk. The monitoring that follows the initial vigilance very often constitutes the bulk of the labor in the risk mitigation process.
Misconception #2: Monitoring risk is an occasional activity
The Reality: Monitoring risk should be a 24/7/365 activity
Traditionally, financial institutes slot their borrowers into high, medium, or low-risk categories depending on their creditworthiness assessment. Risk teams then typically create a monitoring schedule with high-risk borrowers getting more due diligence in the form of monthly reviews and the low and medium-risk borrowers being relegated to an annual or biannual review schedule. These review schedules may be sufficient when the economy is stable. In times of market stress, however, risk exposures of even supposed safe-bet investments can change, as the Covid-19 pandemic has shown us. Given factors such as geopolitical unrest, increasing disease outbreaks, and global warming, we can expect volatile events like the pandemic to occur more frequently now. To navigate this unpredictability, investors should now design dynamic risk monitoring systems that provide vigilance on a 24/7/365 basis. Read our blog on what your post-Covid risk management should look like for more details.
Misconception #3: Preventing loss is a credit risk manager’s only goal
The reality: Preventing loss is just one of the many goals a credit risk manager has.
Credit risk managers are often mistakenly typecast as inflexible, rigid individuals who are singularly focused on preventing loss for their organization. But risk managers do so much more than just that. On the one hand, they manage risk by establishing boundaries for acceptable risk exposure in accordance with their stakeholder’s interests. On the other, they ensure their organization stays profitable. In other words, they aim at creating a perfectly balanced credit environment where the yin of preventing loss is perfectly balanced against the yang of achieving growth and cash flow. In addition, credit risk managers also aim at staying ahead of the curve by planning and strategizing for possible negative outcomes. They also have the crucial task of creating internal accountability and transparency both with stakeholders and borrowers.
Misconception #4: Credit risk monitoring is purely speculative in nature
The reality: Credit risk monitoring is more a science than an art.
At its core, credit risk monitoring is a numbers game. And numbers, unless they’ve been fudged, very rarely lie. Ever since economist William Sharpe developed his Nobel-prize-winning capital asset pricing model (CAPM) in the 1960s, financial risk management has been scientifically driven by sophisticated mathematical tools and complex calculations. The emergence of big data and its utility in quantifying risks has only further pushed the field towards the science side of the equation. Of course, It is important to note that there is some art involved here too. After all, predicting the future has a speculative element to it since no human being or machine can anticipate what lies ahead with 100% accuracy. That said, today’s AI models can process enormous amounts of real-time data and make fine-grained connections within it. This greatly increases its predictive accuracy.
Misconception #5: Analyzing historical data is the best way to predict risk
The reality: Analyzing historical data along with alternate data gives you a more holistic view of your borrowers.
Historical data continues to play a big role in credit risk monitoring. It gives managers crucial reference points and benchmarks for calculating risky behavior. The problem with historical data is that it can reveal risk only after a negative credit event has happened. So, relying only on historical data gives you one-dimensional, reactive risk monitoring ability. On the other hand, adding external data such as transaction data, newsfeeds, and online footprints to the mix can give you a multidimensional view of your borrower. Importantly, these additional data sources can prove invaluable in predicting credit risk well before the default occurs, allowing you to be proactive in your risk monitoring.
Misconception #6: False positives and negatives are a given in credit risk monitoring
The reality: False results are avoidable with some help.
Due to the time-consuming nature of the work, risk monitoring systems that rely heavily on manual or semi-automated methodologies cannot give managers complete portfolio analysis. The resulting partial examination is what causes the false results that are all too common in the financial risk monitoring world. Managers can avoid these dreaded outcomes by augmenting their legacy systems with ML-powered programming that allows them to monitor their portfolios completely, thereby significantly reducing false outputs. According to a study conducted by McKinsey, banks that used AI-augmented systems achieved a Gini Coefficient (a metric that shows an ML model’s accuracy) of above 90% for their early warning systems, significantly improving their ability to accurately predict and mitigate risk.
Misconception #7: For effective credit risk monitoring, you need a large team
The reality: The right system is more effective than a large team
You can only do so much manually, no matter the strength and experience of your team. For example, imagine you have 2000 clients in your portfolio and each analyst on your team can monitor 10 borrowers in a day. You would need a 30-member team to analyze all your borrowers at least once a week. Taking it one step further, if you would like to monitor your entire portfolio daily, that would require a 200-member team!
A better way to go about it is to invest in an automated system that does all the time-consuming, repetitive, and labor-intensive tasks on your behalf. You and your team now only need to deal with the tasks that require contextual understanding and industry expertise. As a bonus, automated systems also get rid of human errors that tend to crop up in a manual-heavy system. This way, you can be more efficient even with a smaller team.
Misconception #8: Compliance and risk monitoring are one and the same thing
The reality: They are two distinct departments.
It would be unwise to assume that a financial institute compliant with regulatory requirements is automatically set up to mitigate risk. Similarly, it is foolish to presume that a company with a robust risk strategy will automatically stay compliant. The two departments, though closely aligned, have distinct goals and approaches. While risk monitoring deals with identifying and dealing with potential credit risk, compliance is all about obeying a regulatory body’s rules and regulations. So, while the former plays in grey areas, the latter sticks to black and white. To ensure optimum balance, banks and other financial institutions should have an organizational framework that gives equal credence to both risk and compliance monitoring.
Misconception #9: All available software solutions are the same
The reality: All credit risk management software solutions are not created equal
The credit risk management process starts when a potential borrower applies for a loan and ends only when he or she has repaid the lender completely. In between these two bookends lies several steps such as assessing borrower creditworthiness, calculating interest rates, setting term limits, loan disbursal, debt collection, and credit monitoring. Most of the software solutions available on the market today are geared towards helping companies assess risk during the loan origination stage. In other words, these can help with assessing risk and not with monitoring it. TRaiCE is one of the few solutions out there that help managers streamline the crucial but arduous process of continuously monitoring your borrower’s credit health after the loan disbursal stage.
Misconception #10: Software solutions take time to implement, need technical expertise to use, and increases a bank’s regulatory risk
The reality: It doesn’t have to be that way
According to a survey by SAP, banks have 3 major concerns about adopting software solutions. The first is regulatory compliance and the second is the reliability of currently available solutions. Third, banks are concerned about the difficulty of implementing and integrating software solutions with their existing system. Since we cannot speak for all the software platforms out there, we will stick to why this isn’t the case with TRaiCE.
Our powerful AI-augmented platform gives financial institutes complete and daily risk monitoring. Yet, it has a user-friendly interface that is easy to master even without any technical expertise. In addition, TRaiCE sidesteps regulatory concerns by clearly explaining the reasoning behind its alerts and predictions. Furthermore, we deploy TRaiCE in a secure environment following all ISO-27001 security best practices and are armed with a vast array of compliance certifications. Last but not least, we do not have a never-ending implementation process. In fact, we aim at onboarding our customers and providing value to them within 4 - 8 weeks!
Conclusion
Most myths are relatively harmless (like the Napoleon one). That said, myths and misconceptions can become a problem when you allow them to dictate terms. The 10 credit risk misconceptions highlighted above are as common as they are problematic. They can cause investors to either develop flawed risk monitoring systems or be satisfied with mediocre ones, both of which are not desirable. So, we hope this blog has succeeded in setting the record straight!
