Mohammad Mohsin Mansoori

The RBI has been trying to fine-tune government's cash management as low discretionary spending (during election time) is likely to result in swelling of government's cash balances with the central bank, thereby resulting in a frictional liquidity tightness. After the g-sec buyback elicited tepid market response (due to reasons like market expectations of higher price, illiquid g-secs enjoying HTM cover, etc.), the RBI decided to prune T-bill borrowing for Q1 FY25 by a cumulative amount of Rs 600 bn. Today was the first T-Bill auction under the revised calendar that saw sale of Rs 120 bn bills vs Rs 270 bn bills in the prior week. The table below gives the weighted average yields for the two auctions: (i) 3M bills saw a drop of 12 bps week-on-week (ii) 6M bills saw a drop of 7 bps week-on-week (iii) 1Ybills saw a drop of 5 bps week-on-week #indianeconomy #moneymarket #bondauction QuantEco Shubhada Rao Yuvika Singhal

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FOMC and data deluge FOMC is due tomorrow and the expectation of status quo is priced in at 90%. In the September policy the first cut is to come and some enthusiasts are also pricing a double cut of 50 bps with a 10% probability. All these probabilities will be reassessed post the Fed Chief's press conference tomorrow. The communication from Fed has been straight so far: The balance of risk is now shifting towards the mandate of maximum employment from the price stability objective which was the center of concern for the last 3 years. This gradual shift means that if employment conditions go down then we (Fed) would act. We are watching the situation and the incoming data points closely. Now the stock of data on the job front is plentiful, the top of the ladder is Non farm payroll data which comes out monthly and is due this friday for July month. In the NFP there is information about the unemployment rate, LFPR, wage gains. The unemployment rate also comes in multiple variations from U1 to U6. The headline number which we see mostly is U3. U1 refers to people unemployed for 15 weeks or longer as a percentage of the civilian labor force, this is currently at 1.5%. U6 refers to the total unemployed plus all persons marginally attached to the labor force plus part time employed as a percentage of the civilian labor force plus all who are marginally attached to the labor force, this number is 7.4% for June. One can see that the definition of unemployment gets more expansive at each level and the interpretations also vary. Then comes the JOLTS data which is also published by BLS and is due tomorrow. This shows how many openings are there in the economy, how many people are quitting job voluntarily (labour turnover). A high voluntary quits number indicates an economy with plentiful opportunities which in turn means wage pricing power in the hand of job seeker. There is an interesting analysis called Beveridge Curve analysis which the Fed Chief referred to in his last press interaction which shows a graph between job openings and the unemployment rate. If job openings are high and the unemployment is also high it means an inefficient job market where the skills and requirements dont match. In a healthy economy with reskilling options the curve is mostly flat with the leftover unemployment considered as structural.   Then there are weekly jobless claims data points which highlight the new addition to the jobless pools as well as continuing claimants. If the continuing number is high it means that the economy is shedding more jobs than it is creating, a leading indicator of recession. All these variations along with anecdotal evidences go into the decision tool kit of the policy maker where they take chance of tweaking the policy rate. Ultimately it is left to the judgement of the committee and any human judgement is prone to error and biases. More inputs can sometime garble the conviction and allow justifying everything under the sun.

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Actuaries vs Risk Managers Actuaries and risk managers are more than just risk folks. More relevant for banking risk quant risk folks. While primarily using quantitative tools to assess risk, the outcomes have a direct impact to: 1. Business 2. P&L 3. Solvency buffers Actuaries price risk, risk folks building credit scorecards for customer risk assessment and acquisition. Actuaries determine the insurance loss reserves, risk folks work on IFRS9 / CECL loan loss reserves, both of them the cost of doing respective business. Actuaries and risk folks work to determine appropriate solvency buffers i.e capital requirement. Basel 3 / 4 in banking vs Solvency 2 for insurance (or variations of Risk Based Capital RBC). One big difference between actuaries and risk managers is, to qualify as an actuary one needs to pass a long series of professional examination e.g the Society of Actuaries SOA, or Institute and Faculty of Actuaries IFOA. The advantage is generally, insurance companies provide salary increments for exams passed. For risk managers, generally no requirements for exams. However, the Financial Risk Manager FRM is quite popular among banking professionals. Salary is entirely performance based. In both cases, you need to like numbers and formulas. This alone is not sufficient. One needs to like how formulas drive business and profitability. And both have large degree of expert judgement, which for now is relatively safe from AI lol. Image thanks to Prateek Yadav.

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🚀 Using Reject Inferencing in Building Application Scorecards: A Double-Edged Sword? 🎯 In the world of credit risk modeling, reject inferencing has emerged as a powerful technique to enhance the robustness of application scorecards. By inferring the likely performance of rejected applicants, lenders aim to fine-tune their models and potentially uncover hidden opportunities. However, the question remains: Should reject inferencing be used, or does it introduce unnecessary model bias? Pros of Reject Inferencing: 1. Increased Data Utilization: Incorporates data from both accepted and rejected applicants, providing a more comprehensive dataset. 2. Model Enhancement: Helps in identifying and correcting biases in the initial approval process, potentially improving the predictive power of the scorecard. 3. Market Expansion: Enables lenders to fine-tune their risk appetite, potentially opening doors to underserved segments without compromising risk management. Cons of Reject Inferencing: 1. Potential Bias: Inferencing relies on assumptions about rejected applicants, which could introduce bias if the assumptions are incorrect. 2. Complexity: The process adds an extra layer of complexity to model development and validation, requiring rigorous testing to ensure reliability. 3. Regulatory Scrutiny: Increased scrutiny from regulators who may question the fairness and transparency of inferred decisions. My Opinion: - One can argue that when done correctly, reject inferencing can significantly improve model performance and fairness. It requires robust techniques to minimize bias, such as using advanced machine learning methods and thorough validation processes. - However, improper implementation can exacerbate existing biases, particularly if the inferred data does not accurately represent the true risk profile of rejected applicants. Ultimately, the decision to use reject inferencing should be guided by a thorough cost-benefit analysis, considering the specific context and regulatory environment of the lender. It's essential to strike a balance between innovation and caution, ensuring that the technique enhances model accuracy without compromising fairness. Conclusion: Reject inferencing holds great potential but must be approached with care. Transparent methodologies, robust validation, and continuous monitoring are key to leveraging its benefits while mitigating risks. #CreditRisk #DataScience #MachineLearning #RiskManagement #FinTech --- Sources: - "Reject Inference and Bias Adjustment in Credit Scoring" [Journal of Financial Risk Management] - "Enhancing Credit Risk Models: The Role of Reject Inference" [Data Science Central] - "Regulatory Perspectives on Credit Scoring Models" [Financial Stability Board]

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Understanding CCAR and PPNR Modeling: An Indian Perspective In the realm of financial risk management, CCAR (Comprehensive Capital Analysis and Review) and PPNR (Pre-Provision Net Revenue) modeling are crucial for ensuring the stability of financial institutions. While these concepts are integral to the U.S. banking sector, they have significant parallels in the Indian context. CCAR Modeling: A Shield Against Financial Turbulence CCAR, implemented by the Federal Reserve, assesses the capital adequacy of large U.S. banks. It ensures that these institutions have robust capital planning processes to withstand severe economic stress. Banks must submit comprehensive capital plans, including projections of revenue, losses, reserves, and capital levels under various stress scenarios. In India, the RBI conducts a comparable exercise through its Stress Testing Guidelines for Banks. These guidelines require banks to perform stress tests to evaluate their resilience against adverse economic conditions. Although the specifics differ, the goal remains the same: maintaining financial stability and protecting the banking system from potential crises. PPNR Modeling: Gauging Operational Efficiency PPNR modeling is a key component of CCAR, focusing on estimating pre-provision net revenue. This involves forecasting a bank’s revenue streams and operational costs before accounting for loan loss provisions. Accurate PPNR models are crucial for understanding a bank's capacity to generate income and manage expenses under stress. In the Indian context, banks perform similar analyses through Earnings at Risk (EaR) assessments. EaR measures the potential decline in a bank's earnings due to adverse movements in market variables like interest rates and foreign exchange rates. By evaluating their earnings potential, Indian banks can identify vulnerabilities and enhance their risk management strategies. Bridging the Concepts: A Path Forward While CCAR and PPNR are specific to the U.S. regulatory environment, their core principles resonate globally, including in India. Both regions emphasize rigorous stress testing and robust revenue modeling to safeguard financial stability. Key Takeaways for Indian Financial Institutions: 1. Adopt Comprehensive Stress Testing: Emulate the detailed approach of CCAR by incorporating diverse and severe stress scenarios into your risk assessment frameworks. 2. Enhance Revenue Forecasting: Strengthen your PPNR or EaR modeling techniques to ensure accurate projections of revenue and operational costs, enabling better capital planning. 3. Regulatory Alignment: Stay abreast of RBI guidelines and global best practices to ensure your institution's resilience against economic shocks. By leveraging these insights, Indian banks can fortify their financial health and contribute to a more stable financial system. #Finance #Banking #RiskManagement #CCAR #PPNR #StressTesting #IndianBanking #FinancialStability #CapitalPlanning

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EAD xLGD x PD for Loss Estimation Many of us know this equation with reference to calculation of Expected loss. However, if we go deeper in statistics, we would observe that this is a general equation for any loss estimation or decision making. In simple words formula is :- EAD x LGD x PD = exposure x severity x frequency We can read simple applications in Risk Management as follows :- 1. Expected loss is equal to EADx LGD x PD ( PD is through the cycle PD) 2. Unexpected loss , RWA calculation is also on same lines, (Refer my earlier Linked in post) Simple equation is :- EAD x LGD x PD (UL) - (EAD x LGD x PD (EL)) Deducting EL because expected loss is already taken care separately for direct capital charge. 3. IFRS9 Impairment provision is also same, just that PD is different, Here PD is Point in Time PD plus some other overlays are involved. The whole impairment engine is basically aligned to focus on this PD which is very complex and include macro economic factors as well including country risk, currency risk etc. 4. Advance approach (AMA) for operational risk also involves poison distribution for frequency and other distribution for severity ( eg logarithmic distribution). This is yet to be adopted by even largest banks in the world . As of now data accumulation is in progress for normal distribution. Enough of this boring stuff, Lets Let’s have fun as well while learning🤣:- 1. When we cross the road over decision making is also EAD x LGD x PD. Here exposure is me crossing Road , LGD is injury might happen given the accident takes place and PD is probability of accident. If I am with my child and family, I would not cross the road because exposure is very high. If I am crossing a freeway or highway, I would dare to do that because LGD is very high as accidnet can be fatal. I would not cross a very clumsy Road with high traffic because PD is too high. 2. You are going to write exam. Loss of pen not working is also calculated by same formula. Ead x Lgd x PD Exposure here is failing exam if pen doesnt work LGD :- Loss of marks scoring if pen doesnt work PD :- Probability of pen not working. We fill pen with adequate ink to reduce PD , and we have back up pens to mitigate LGD.🤣 Its everywhere if we see with open eyes🤣🤣 Happy learning ❤️

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Another article considering Credit Risk and Market Risk for the operational efficiency of banks, titled Operational efficiency in the presence of undesirable byproducts: an analysis of the Indian banking sector under traditional and market-based banking framework is also published. you may please access it at https://lnkd.in/dxU34QdZ 

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In the October MPC, I expect the RBI to change its policy stance to 'neutral' from 'withdrawal of accommodation' for several reasons: 1. There is no accommodation left to withdraw, and this has been the case for some time. The RBI has cited rate transmission as a justification, but the transmission has largely run its course. Maintaining the current stance will not lead to further transmission. 2. Monsoon, reservoir levels, and Kharif sowing are all favorable for the food inflation outlook. It is important to note that headline inflation has been driven entirely by food. As Kharif produce arrives in the market during winter, food inflation is expected to ease, aligning the inflation trajectory for FY26 with the 4% target. A neutral stance would provide the RBI with the flexibility to act as necessary, depending on the data. 3. A petrol and diesel price cut could be imminent, given the healthy marketing margins of OMCs. While an escalation in Middle Eastern conflicts could push oil prices higher and diminish hopes for a price cut, the base case currently assumes a reduction in pump prices. FYI - A 10% cut in petrol and diesel prices could lower inflation by 50-55 bps. 4. The RBI needs the flexibility to act appropriately in the December policy meeting. Leading indicators are already showing signs of economic weakness. By December, the RBI will have a clearer picture of growth, and if a slowdown is confirmed, a rate cut may be necessary. Overall, there is a significant likelihood of a stance change in the October policy.

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Master Risk Management with Financial Derivatives This informative Video, led by industry expert Shri Subhash Chander Kalia (former ED Union Bank of India), dives deep into the world of financial derivatives and their crucial role in risk management. Valuable Downloads:  1. PPT on Financial Derivatives and Risk Hedging Strategies 2. Summary of the Webinar 3. Dos and Don’ts for Bankers 4. Glossary of Terms 👩‍🎓🧑‍🎓 Enroll today and let’s grow together

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#Collectionstratgy for Data Segregation. To address the segregation of debt data into "Connected" (RPC: Regular Paying Customers) and "Not Connected" (NC) categories, and to convert NC to RPC, you can follow these steps:  1. Segregation of Data:   - Connected (RPC): These are customers who have made regular payments on their debts. They are typically in good standing and have a history of timely payments.   - Not Connected (NC): These are customers who have not made payments or have irregular payment histories. They may be at risk of default or have already defaulted.  2. Analyzing the NC Data:   - Identify Reasons for Non-Payment: Conduct analysis to understand why these customers are in the NC category. This could include factors like financial difficulties, disputes over billing, or lack of communication.   - Segment the NC Customers: Further divide NC customers into sub-categories based on the likelihood of converting them to RPC. Factors to consider could include payment history, contact information availability, and communication preferences.  3. Strategies to Convert NC to RPC:   - Enhanced Communication:     - Reach Out: Use multiple channels like SMS, email, and phone calls to reconnect with NC customers.     - Offer Payment Plans: Provide flexible payment plans that accommodate their current financial situation.   - Incentives:     - Discounts or Waivers: Offer discounts on outstanding amounts or waive late fees to encourage payment.     - Rewards for Compliance: Provide incentives for customers who start making regular payments, such as discounts on future purchases or services.   - Legal and Credit Consequences:     - Highlight Implications: Clearly communicate the legal consequences of non-payment, such as collections or legal action, as well as the impact on their credit score.   - Regular Follow-Up:     - Monitor Progress: Regularly follow up with NC customers who show interest in becoming RPC. Track their progress and adjust strategies as needed.     - Customer Support: Provide ongoing support to address any concerns or barriers they may face in making payments.  4. Implementing and Monitoring:   - Data Tracking: Continuously track the movement of customers from NC to RPC categories. Use this data to refine your strategies over time.   - Feedback Loop: Collect feedback from customers who have been successfully converted to RPC to understand what worked and apply those insights to others.  5. Automation and Tools:   - CRM Systems: Utilize Customer Relationship Management (CRM) tools to automate communication, track payment histories, and monitor customer interactions.   - Analytics: Use data analytics to predict which NC customers are most likely to convert to RPC, allowing you to focus your efforts where they are most needed. This approach should help in effectively managing debt data and improving conversion rates from NC to RPC.

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Can Politics introduce a bias, which distorts usual borrower's repayment-behaviour, and the Data-registry ? And hence, the Credit Risk Models ? Modeler can't change the politics, but can address and minimize the impact, through model-design. (an Old-work) #predictivemodel #cibilscore #transunion #experian #crif #muktibodh

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India has witnessed a steady increase in the imports of various critical inputs for the manufacturing sector. For example, India’s imports of circuits (integrated circuits, printed circuits, and memories, etc.) have more than doubled since FY21. In fact, India’s electronic deficit has increased despite a surge in mobile phone exports due to increased imports of input components and peripherals. The demand for lithium-ion batteries (accumulators), almost all of which is imported, has also risen in tandem with the increasing penetration of electric vehicles (EVs). Approximately 75% of India’s lithium-ion imports come from China. Similarly, India’s solar industry heavily depends on China for PV cells and module imports. China accounts for 60-65% of total PV cells and modules imports. Consequently, India's contribution to value-added remains low in the manufacturing sector due to its dependence on imports for critical inputs. However, this deficiency is now being actively addressed. 1. The government has approved the establishment of four semiconductor units under a $10 bn incentive scheme for semiconductor and display manufacturing. 2. The government imposed a basic customs duty on solar PV cell and module imports starting April 2022. The Approved List of Models and Manufacturers (ALMM) was introduced in 2021 as a non-tariff barrier to boost domestic manufacturing. It was paused in FY24 but reinstated on April 1st, 2024. 3. Lithium blocks within the country are undergoing auction processes, while efforts are underway to acquire lithium blocks from other nations as well, aimed at mitigating risks and ensuring secure supply chains. Domestic value added in manufacturing is expected to improve gradually as backward linkages develop within India. However, it's important to note that this will be a lengthy process. Furthermore, China expanded its manufacturing and exports during a period of rapid globalisation. We are now in a deglobalisation phase, and many developed and developing countries are implementing very active industrial policies to boost domestic manufacturing. Consequently, India will face significant hurdles in ramping up its manufacturing and exports.

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Understanding the Current NPA Rates of Credit Cards in India Non-Performing Assets (NPA) related to credit cards are a significant area of concern for both banks and consumers. NPAs indicate the level of financial distress and risk associated with credit cards, and understanding these rates can provide valuable insights into the broader economic climate and credit market dynamics. Current NPA Rates in India As of the latest data, the NPA rates for credit cards in India reflect both the economic challenges faced by consumers and the resilience of the banking sector. Recent reports from the Reserve Bank of India (RBI) and financial institutions provide the following insights: Overall NPA Trends: The NPA rates for credit cards have been fluctuating, with recent figures showing a moderate increase. This trend aligns with broader economic challenges and changing consumer behavior. As of early 2024, the NPA ratio for credit cards stands at approximately 2.5% to 3.0%, up from around 2.2% in the previous year. Impact of Economic Factors: Economic slowdowns, inflationary pressures, and changes in employment patterns have contributed to the rise in NPA rates. Consumers facing financial difficulties are more likely to default on their credit card payments, which in turn affects the overall NPA metrics. Regulatory Measures: The RBI continues to monitor and regulate NPA levels, implementing measures to mitigate risks. These include guidelines for prudent credit management, increased provisioning requirements, and support mechanisms for distressed borrowers. Implications for Consumers and Financial Institutions For consumers, high NPA rates highlight the importance of managing credit responsibly and staying informed about their financial health. Regularly monitoring credit card statements, understanding interest rates, and maintaining a budget are crucial practices to avoid falling into the NPA category. For financial institutions, managing NPAs involves enhancing credit risk assessment, improving collections processes, and providing support to borrowers facing financial difficulties. Effective NPA management ensures the stability and profitability of financial institutions while contributing to the overall health of the credit market. Looking Ahead The future of credit card NPAs in India will depend on various factors, including economic recovery, changes in consumer spending patterns, and the effectiveness of regulatory measures. Both consumers and financial institutions must stay vigilant and adapt to these evolving conditions to navigate the credit landscape successfully. What are your thoughts on the current NPA rates for credit cards? How do you think they will impact the financial sector and consumers in the coming months? Share your insights and experiences in the comments below!

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🔥 Analysis of IDFCFIRSTB for LONG 📢 Investments in the securities market are subject to market risks, read all the related documents carefully before investing. ❗ ❗ DISCLAIMER: Please remember that we are not SEBI-registered or authorized analysts. The charts, levels, and stocks posted will be only for educational or study purposes and should not be considered as a signal to trade or a suggestion to buy or sell in any way. I will not be responsible for any of your losses or profits. We are not here for market manipulation, and any kind of legal action will not be entertained. ❗ ❗ IDFC FIRST Bank Religare Broking Ltd Zerodha Groww 5paisa TradingView #Banknifty #Stockmarket #Trading #Investment #Money

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RBI Extends PCA Framework to Government-Owned NBFCs: A Move Toward Financial Stability In a significant regulatory update, the Reserve Bank of India (RBI) has extended its Prompt Corrective Action (PCA) framework to include government-owned NBFCs, effective from October 1, 2024. This move is aimed at enhancing oversight of these institutions, which play a critical role in India’s financial system. The PCA framework will introduce restrictions on dividend payouts, equity infusions, and contingent liabilities, all geared towards ensuring financial prudence and reducing systemic risk. This proactive approach allows the RBI to intervene early when financial stress indicators arise, ensuring timely corrective measures. As NBFCs continue to grow in size and influence, this enhanced supervision will help safeguard the financial health of these institutions and, by extension, the broader financial ecosystem. #RBI #NBFCs #PCA #FinancialStability #BankingReforms

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My take on the #monetarypolicy - RBI on a long pause is what I think. Unless something gives and if #RBI is correct on the inflation path that it predicts, chances of a rate cut in FY25 appears slim. Clearly any rate cut could also have implications for banks’ deposit growth and that might not be what RBI would wish for at this moment. Looks like along with inflation coming down, RBI would also like to see #C-D ratio coming lower. #YES Bank

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India's near-term growth outlook is pegged as being upbeat basis three expectations : 1) Strong private consumption recovery, 2) rise of business capex, and 3) Averting an export recession. What are high frequency data telling about these hopes? Data underscore emerging pockets of weakness in urban consumption and ebbing consumer confidence; business capex revival is finally visible in high frequency data, imparting longevity and quality to India’s investment cycle; and, services exports continue to mitigate lacklustre goods exports performance. We maintain that FY24 growth will be weaker than the RBI's forecast, but strong nevertheless. Read in our latest Asia macro weekly: India's growth: hopes and facts https://lnkd.in/dYgZ5F6E #india #growth #rbi #outlook

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NBFCs with #networth over Rs 150 crore have Expected Credit Loss (ECL) #framework applicable to them. ECL is a forward-looking approach to #riskmanagement, but there could be some changes in the norms that could improve enhanced #financialstability and #consumer #affordability. I write in BW Businessworld with suggestions for newer norms : aligning ECL provisions with expected income, creating dedicated reserves separate from P&L, and addressing inconsistencies such as ECL’s inclusion in #Tier2 #capital and its #tax treatment. These suggestions aim to reduce #inflationary pressures on #credit #pricing, strengthen institutional buffers, and ensure ECL serves as a true safeguard during #stress scenarios. https://lnkd.in/dTbsJX-G #NBFC #ecl #RBI Reserve Bank of India (RBI) #ifrs #lending

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