Credit Risk Evaluation

CLOs are Cracking: Welcome to the World of Unintended Consequences A big selloff of existing collateralized loan obligations (CLOs), which buy and pool buyout debt, has slowed the issuance of new CLOs. This makes sense - because the selloff pushed down the prices of existing CLOs, that's what investors will buy until issuers price new CLOs more attractively. This in turn has created a headache for banks looking to offload buyout debt they would have gotten off of their balance sheets by repackaging it into new CLOs. This is not a minor issue; CLOs are a ~$1.4 trillion market. Part of the pressure the market is under is by design. CLOs typically pay floating rates, so they yield less when rates look like they might fall faster than previously believed. And because they are backed by debt used to help finance leveraged buyouts, they also have exposure to credit risk. You're seeing the heightened market pressure show up in ETFs that own CLOs. The $20 BN Janus Henderson AAA CLO ETF (JAAA) recently saw nearly $600 MM of withdrawals, the biggest single-day outflow since the fund’s inception in 2020. This alone was enough to put pressure on valuations overall. ETF prices normally trade in line with net-asset values because specialized traders (aka, "authorized participants" / APs) will buy shares of the ETF whenever they drop below the NAV because they can then redeem the ETF with the issuer in exchange for the underlying assets, which they then sell. It's essentially a risk-free profit. But some CLO-focused ETFs are trading at discounts to the value of their portfolios wider than 4%. The fact that the APs are not stepping in to pick up what should be free money makes us wonder how accurate those NAVs are. As the macro environment continues to deteriorate, and at an accelerating rate, the banks looking to offload the loans they made via new CLOs must be going through the same grim math. Needless to say, when even the specialists hesitate to step in, buyer beware... For the full picture, read these very thorough articles by Carmen Arroyo Nieto, Scott Carpenter, and Katie Greifeld: https://lnkd.in/eWBV527F https://lnkd.in/eiFA73Bx #investing #stocks #bonds #CLOs #ETF #stockmarketcrash #tariffs #TradeWar

CLOs becoming a victim of their own success? 🥹 I used to be a big buyer of CLO tranches back in the 2000s. An asset class I am super familiar with and which I like to follow for the opportunity set that CLO equity funds represent. Bloomberg is highlighting an interesting, somewhat disconcerting trend in CLO land which warrants a closer look 👀 in my view: "The $1.3 trillion CLO market is about to become a victim of its own success because managers can’t create the bonds fast enough to meet demand and are running out of things to buy. A slowdown in M&A after borrowing costs rose is continuing to deprive the lenders of the leveraged loans that the industry was built on. About $311 billion of M&A deals have been announced and completed so far this year, roughly $1 trillion below the same level two years ago when interest rates began to rise. 💡That may soon end up impacting the equity arbitrage which may hurt new issuance in the coming months. It’s also sent more managers into the secondary market, where about 60% of loans now trade above par, making it that much harder to find bargains to put together a portfolio. 💼 'There’s too much demand for CLO bonds and too little loan supply. CLO managers can’t keep up much longer,' said Pratik Gupta, who leads CLO research at Bank of America. Demand for the safest CLO tranches soared this year after an influx of money into ETF. Banks have also been piling into the AAA bonds, and some Japanese 🇯🇵 institutions may scoop up more of the debt. On top of that, Bank of America estimates that about $64 billion of the debt has been paid back so far this year, including amortizations and called CLOs, meaning asset owners have more capital to put to work. 'If you’re an existing investor, you’re getting so much money in the door that’s creating demand in and of itself,' said Amir Vardi, an MD at UBS Asset Management. 'Forget about increasing the budget to get more,' he said on a panel. 'You’re just trying to keep what you have invested.' Demand is so strong that even an 86% increase so far this year in US sales of new issue CLO bonds from the same period in 2023 hasn’t been enough to sate investors’ appetite. As a result, spreads on the AAA debt have compressed by more than 100 basis points over the benchmark since late 2022. ⚠️ Lenders are also trying to circumvent the dearth of paper by increasing their holdings of corporate bonds — both investment-grade and junk — in an attempt to preserve arbitrage returns, Gupta said. The rise of private credit is also crimping opportunities for leveraged loan lenders by winning business from them. 'The supply and demand balance is out of whack, it’s become more difficult to find assets at attractive levels,' said Christina O’Hearn, PM for the leveraged loan and CLO business at Pretium Partners. 'We expect to see continued refi & reset activity but not as many new issue CLOs.'" (+++Opinions are my own. Not investment advice. Do your own research.+++)

"Since the pandemic, buyers on auto-dealer lots have encountered surging sticker prices and smaller incentives from automakers to lessen the blow. To afford an automobile, more consumers, especially lower-income families, have resorted to buying used cars and taking out longer loans. Now, more are falling behind on their loans, signaling that lower-income consumers are struggling to afford payments as wages stagnate and unemployment ticks higher. While the economy has remained strong, and Wall Street has kept buying subprime auto loans, the auto market is evidence that not all is well under the hood. The percentage of new-car buyers with credit scores below 650 was nearly 14% in September, roughly one in seven people, J.D. Power said last month. That is the highest for the comparable period since 2016. And the portion of subprime auto loans that are 60 days or more overdue on their payments hit a record of more than 6% this year, according to Fitch Ratings, while delinquency rates for other borrowers have remained relatively steady." https://lnkd.in/eSbFaJaU

At Rich Data Co we saw a gap in the market for banks to better utilise their customers’ transaction data to understand the financial health of their business and commercial customers. AI plays a key role in predicting the cashflow health of businesses. This enables bankers to understand their customer’s past, present and most importantly, their future. With these capabilities, bankers are able to do 3 things:    1️⃣ Seeing warning signs in real time: RDC applies AI to transaction data to identify cashflow deterioration. Cashflow is a leading indicator for early warning, while many other factors are lagging behind business operation problems. Many banks rely on risk rating changes to identify early warnings. This could be triggered by a review of financial statements (often 18 months old), behaviour data changes or banker judgement. While all these factors are important, they are likely to be too late given it is backward looking. This is like comparing driving a car looking out the front window vs. looking at the rear view mirror.  2️⃣ Identify lending opportunities: A businesses cash flow position goes through ups and downs, especially seasonal businesses such as retailers. The prediction of cashflow health allows bankers to look into the future and provide lending to customers when they need it the most. This also allows banks to assess loan suitability to lend responsibly. Banks need to assess how the business can pay the loan back with the cashflow it generates, i.e. the primary source of repayment. Lending to businesses with a strong cashflow will be less risky for banks and provide affordable loans for the business to grow. 3️⃣ Improving efficiency in the customer review: Continued assessment of customer risk enables banks to drive efficiency in their customer review obligation required by the regulators.   This is a paradigm change in how banks manage their business and commercial lending portfolio. We have seen enlightened banks embracing and leveraging AI to realise significant benefits for both the bank and their customers. This paradigm change moves banks from assessing credit risk only a few times, to ongoing. This is like comparing banks taking a static picture of their customers’ financial health vs. making a movie by ongoing observation of their customers. Static picture vs. a movie of a customer's financial health, which one do you think would be more accurate and timely?     The difficulty in applying AI in this domain is how to achieve cashflow prediction accuracy to a banks lending standard. If you'd like to hear more details, RDC is always open to chat.   https://lnkd.in/gjshBwbb   #FutureOfCredit #MachineLearning #ArtificialIntelligence  

A systematic approach to Credit Assessment specially in banks : The "7 C’s of Credit "are key factors that lenders and credit analysts use to evaluate a borrower’s creditworthiness. Here's a concise overview of each: 1. Character Refers to the borrower’s reputation, integrity, and track record for repaying debts. Assessed through: -Credit history like eCIB reports - References - Background checks from suppliers/buyers/competitors/existing banking relationships 2. Capacity The borrower’s ability to repay the loan from earnings or cash flow. Assessed through: - Financial Statements - Personal Networth Statement - Debt service coverage ratio (DSCR) / Current ratio - Existing obligations - Debt Burden calculations 3. Capital The borrower’s own investment or equity in the business or project. - Shows commitment and reduces lender risk. 4. Collateral Assets/collateral offered to secure the loan and mitigate lender’s risk in case of default. Includes: - Property -inventory - Equipment - corporate guarantees 5. Conditions External and internal factors that affect repayment, like: - Industry health - Economic trends - Regulatory environment - Purpose and terms of the loan 6. Cash Flow Refers to the borrower’s actual inflow and outflow of cash and its adequacy to service the debt. - Crucial for determining repayment capacity. 7. Commitment Indicates the borrower’s willingness to contribute or take risk(e.g., personal guarantees, equity contribution). Demonstrates seriousness about the business and project.

How Banks Ensure Regulatory Compliance: Conducting Treasury Activities Regulatory compliance is a cornerstone of modern banking, ensuring financial institutions operate within legal frameworks. For banks, particularly in treasury activities, maintaining compliance is crucial to uphold trust, manage risk, and avoid significant penalties. Here is how banks ensure regulatory compliance in their treasury operations: Understanding Regulatory Requirements: Banks must have a comprehensive understanding of relevant regulations, including international directives and national rules. These cover capital adequacy, liquidity management, and risk assessment. Robust Internal Controls: Implementing robust internal controls is essential. Compliance departments monitor and enforce adherence to regulatory standards through regular audits and reviews of treasury activities. Effective Risk Management: Banks use risk management frameworks to identify, assess, and mitigate risks in their treasury operations. This includes market risk, credit risk, and operational risk, maintaining a conservative approach. Training and Education: Continuous training ensures staff are aware of regulatory changes and understand their roles in compliance. Specialised training for treasury staff focuses on specific compliance requirements. Technology and Automation: Advanced software solutions monitor transactions, manage data, and generate compliance reports. These tools detect potential compliance issues in real-time for prompt corrective actions. Regular Reporting and Documentation: Accurate and timely reporting to regulatory bodies is essential. Comprehensive documentation of all treasury activities ensures transparency and provides a clear audit trail. Engagement with Regulators: Proactive engagement with regulators keeps banks informed about upcoming regulatory changes and provides guidance on compliance matters, addressing issues before they escalate. Scenario Analysis and Stress Testing: Conducting scenario analysis and stress testing helps ensure compliance under various market conditions. Banks assess the impact on their treasury activities to ensure they can withstand adverse conditions. Ensuring regulatory compliance in treasury activities is a multi-faceted process requiring understanding regulations, implementing robust controls, managing risks, continuous education, leveraging technology, accurate reporting, engaging with regulators, and conducting scenario analysis. By prioritising compliance, banks navigate the complexities of the regulatory landscape, contributing to the stability and integrity of the financial system.

Private credit didn’t replace banks — it entangled them. 👇 ________________________ BACKGROUND: Since 2008, the dominant narrative has been simple: Private credit stepped in where banks stepped out — helped by lighter regulation. But that story is incomplete. Banks and private debt funds aren’t competitors. They’re co-dependent — and the risks are now deeply intertwined. ________________________ HOW BANKS ARE LINKED TO PRIVATE DEBT FUNDS: A great FT piece breaks this down, but here’s the simplified map of how banks fund the very private credit ecosystem that supposedly replaced them: 1️⃣ Upstream – financing the investors ‣ Banks lend to LPs to fund commitments ‣ Banks lend to GPs via subscription lines → All secured by LP capital 2️⃣ Midstream – financing the funds’ assets ‣ "Loan-to-loan" facilities for SPVs at 60–70% LTV ‣ Repo structures: sell loans today, buy them back later ‣ NAV loans → Banks finance the portfolio construction itself. 3️⃣ Downstream – financing the same companies the funds lend to → Banks and private credit funds often finance the same borrower without realising it. ________________________ WHERE SYSTEMATIC RISK EMERGES: Two accelerants turbo-charge the feedback loop: ‣ CLOs → banks buy slices of the very credit risk they once avoided ‣ Significant Risk Transfers → banks hedge loan books with private credit funds And this is where things can get messy — fast: ‣ Borrowers are already highly levered ‣ A closed loop of leverage + shared exposure with almost no visibility on concentration risk ‣ Most private credit loans are floating-rate, amplifying sensitivity to rate shocks ‣ Any downturn (defaults rising or rates staying higher-for-longer) → levered, correlated losses across banks and funds ________________________ So the questions nobody has answered yet: 1️⃣ How much bank capital is truly at risk if private credit hits a stress cycle? 2️⃣ And more importantly — Who actually holds the risk when everyone is financing everyone else? 👉 What do you think? ________________________ 👋 Follow me Andrea Carnelli Dompe' (PhD) for weekly private markets insights 🔔 Tap the bell on my profile and you'll be notified when I post #PrivateMarkets #PrivateDebt #PrivateCredit #Banking #RiskManagement #ECB #FinancialStability

Should every lending decision be AI-driven? Banks are experimenting with GenAI credit models. But here’s the nuance: 👉 Not all credit risk is a “prompt-and-predict” problem. My view: 🛑 Low-ticket, rule-driven loans (personal, auto) → Use scorecards + automation 🛑 SME working capital with stable patterns → Use predictive ML on transaction flows 🛑 Complex project finance, syndicated loans → Use structured analytics + expert committees ✔️ High-variance SME/retail mix, where qualitative signals matter (e.g., sector shifts, sentiment from contracts/emails) → GenAI adds real lift 💡 AI isn’t about “faster yes/no.” It’s about augmenting risk insight where rules fail and variance is high. What are your thoughts on the AI-driven decision making? #ArtificalIntelligence #AI #Banking #Lending #GenAI #AgenticAI #Finance #SME #RetailBanking