Total review of all banking credit risks, for management reporting and relationship banking dashboards. Drill into clients by relationship, location, risk weighting and more. Identify credit risk at the regional level and/or drill into data at the individual client level. Review all current bank deposits and loans across all key areas of the bank including retail, commercial and institutional banking. Analysis fee structure and optimise future marketing spend.
Views: 3466 Enterprise DNA
In this tutorial Commercial Bank Revenue Model: Loan Projections, you’ll learn about the key revenue drivers for a commercial bank, with a focus on how to project its loan portfolio based on GDP growth, market share, and addressable loan market sizes. http://breakingintowallstreet.com/ "Financial Modeling Training And Career Resources For Aspiring Investment Bankers" Table of Contents: 1:46: Overview of Revenue for a Bank 6:47: The Step-by-Step Process to Project Loan Growth 15:06: Calculating and Checking the Loan Size in Each Segment 19:39: Recap and Summary For pure-play commercial banks, the vast majority of their revenue will come from “Net Interest Income”: Interest Income on Loans, less Interest Expense paid on Deposits, Debt, and Other Funding Sources. KEY QUESTION #1: What will the bank’s Loans and Deposits be? KEY QUESTION #2: What will the bank’s Interest Rates Earned and Paid Be? Interest rates are a whole separate topic, and Deposits and Funding Sources are usually linked to Loans, so we’re going to focus on the key drivers behind Loans and Loan Growth here. More so than with “normal companies,” commercial banks’ fortunes are heavily linked to the overall economy. Higher GDP growth results in more transactions – more buying and selling – and to more borrowing by both consumers and businesses. A healthy bank will tend to grow its loans more quickly than the GDP growth rate – credit expansion leads economic expansion. So the first key driver of Loan Growth is GDP growth. Some banks might sell more effectively, might offer more favorable terms for lenders, or might have different lending standards, so market share also plays a role (this is key driver #2). The Step-by-Step Process to Project a Bank’s Loan Portfolio Step #1: Determine the sizes of a bank’s markets (e.g., Mortgages, Auto Loans, and Credit Cards) to calculate its market share(s). Step #2: Make each market a percentage of the country’s GDP. Step #3: Project how the country’s GDP changes in the future. Step #4: Project the bank’s market share in each segment and forecast each loan market as a percentage of the country’s GDP. Step #5: Calculate the Loan Size in each segment with GDP * Loan Market Size as a % of GDP * Bank’s Market Share. Steps 1 & 2: Sizing the Loan Markets Possible Sources: Bank’s IPO Prospectus, Industry Reports (UK – De Montfort Group), Bank’s Interim/Annual Reports or Earnings Calls, Equity Research… If you can’t find data on loan market sizes, make it less granular and look at Total Loans in the country instead and calculate the bank’s market share there. The goal is to get a rough sense of whether the bank’s market share is rising or declining over time. Step 3: Projecting GDP Growth You can find any country’s nominal GDP via sources like Wikipedia, Statista, the IMF/World Bank, etc. For the projections, you can consult with similar sources, but you should also consider different cases and think about what happens if growth continues as expected, what happens if it goes above expectations, and what happens if there’s a recession followed by a recovery. Step 4: Projecting Future Market Share and Addressable Loan Market Sizes Approach #1: Follow and extend historical trends (If the bank is losing/gaining market share, continue that; otherwise, keep it steady). Approach #2: Speak with people in the market, such as real estate brokers and new homeowners, and see if you can discern trends from them (“channel checks”). Approach #3: Look for outside sources such as equity research and buy-side research and see what they’re saying. Step 5: Calculating the Loan Size in Each Segment Loan Size = Nominal GDP * Loan Market Size as % of GDP * Bank’s Market Share The harder part is checking your numbers afterward – Do the estimates seem reasonable? Do they accurately reflect different outcomes? You often want the Base or Upside Case to be close to equity research/consensus/management estimates. And the Downside Case should be real (e.g., 2009-style recession) – negative GDP growth, not just 1% growth rather than 2%. RESOURCES: https://youtube-breakingintowallstreet-com.s3.amazonaws.com/Bank-Loan-Projections-Before.xlsx https://youtube-breakingintowallstreet-com.s3.amazonaws.com/Bank-Loan-Projections-After.xlsx https://youtube-breakingintowallstreet-com.s3.amazonaws.com/Bank-Loan-Projections.pdf
Views: 16499 Mergers & Inquisitions / Breaking Into Wall Street
Click here for more Information http://www.audiosolutionz.com/banking/loan-portfolio-management.html Loan Portfolio Management: Major Issues and Risk Ratings In this training session Vincent A. DiCara will understand you about how to develop a sound loan portfolio management system. For More Videos: - http://www.youtube.com/user/SolutionzAudio You can also connect with us on Twitter, Facebook, Google+ and LinkedIn and get the most updated 3Banking & Finance, Leadership, Teamwork, Education. Connect with us on Twitter: -https://twitter.com/SolutionzAudio Facebook: - https://www.facebook.com/AudioSolutionz Linkedin: -http://www.linkedin.com/company/audio-solutionz Google+:- https://plus.google.com/+Audiosolutionz1/posts
Views: 900 AudioSolutionz
This video explains the: 1.Loan Portfolios and Expected Loss 2.Computing Expected Loss 3.Credit Risk Models and Credit Optionality Click the following link for more details http://goo.gl/yZrxqS
Views: 1859 Simplilearn
Download Excel Files: Start File https://people.highline.edu/mgirvin/YouTubeExcelIsFun/ExcelFinanceTricks1-17.xls Finished File https://people.highline.edu/mgirvin/YouTubeExcelIsFun/ExcelFinanceTricks1-17Finished.xls Full Page With All File Links: http://people.highline.edu/mgirvin/excelisfun.htm See how to use the PMT and RATE functions for a complete Debt/Loan Analysis. Debt Analysis with input variables: % Down Payment, APR, Years, Points, Extra Fee. See how to calculate Amount to Borrow, Monthly Payment, Actual Cash Received, Adjusted APR, and PMT w Balloon Payment. In This Series learn 17 amazing Finance Tricks. Learn about the PMT, PV, FV, NPER, RATE, SLN, DB, EFFECT, NOMINAL, NPV, XNPV, and the CUMIPMT functions that can make your financing tasks much easier in Excel. See how to use the PMT function in the standard way, but also see how to use it while incorporating a Balloon payment or a delayed payment. Lean how to translate a Nominal interest rate into an Effective Interest rate. Learn how to calculate how long it takes to pay off a credit card balance. Lean how to calculate the Effect Rate on a Payday loan. And many more financing Tricks!! The Excel Finance Tricks 1-17 will show an assortment of Excel Financing Tricks! Formula
Views: 25135 ExcelIsFun
4 Key Financial Ratios for Banks i.e. fundamental analysis for banking stocks are as follows 1. Financial Leverage or Equity Multiplier 2. Return on Assets 3. Return on Equity 4. NIM or Net Interest Margin These are profitability ratios or risk ratios. With the help of these 4 Financial Ratios for Banks, you can decide which banking stocks are fundamentally strong or weak. 1. Financial Leverage or Equity Multiplier: This ratio is calculated by dividing total capital or asset to net worth of the bank. The maximum value is 15. If this value exceeds 15 then it implies that bank is taking a high risk by accepting more deposits. 2. Return on Assets: It is the profitability ratio arrived by dividing Net Profit / Total Assets. The idea value is 1% or more than that. 3. Return on Equity: Net Profit divided by Net Worth is Return on Equity. The idea value is 15% or more. You can also calculate by multiplying Equity Multiplier and Return on Assets 4. NIM or Net Interest Margin: This is a very important financial ratio. You can calculate by (Interest Earned - Interest Expended) divided by Total Assets. The max value is 3% i.e. higher NIM means the bank is disbursing more loans to improve NIM and it reduces the return on assets. It is not considered a good sign. If you liked this video, You can "Subscribe" to my YouTube Channel. The link is as follows https://goo.gl/nsh0Oh By subscribing, You can daily watch a new Educational and Informative video in your own Hindi language. For more such interesting and informative content, join me at: Website: http://www.nitinbhatia.in/ T: http://twitter.com/nitinbhatia121 G+: https://plus.google.com/+NitinBhatia #NitinBhatia
Views: 17398 Nitin Bhatia
hese videos go through the syllabus objectives for the Financial Exams of ST5/F105/SA5/F205. They are raw, unedited and contain a large amount of opinion. I've taken a skeptical approach to the subject and my views may not be correct. Feel free to correct me in the comment section below. I'll be releasing a new video every day
Views: 15692 MJ the Fellow Actuary
hese videos go through the syllabus objectives for the Financial Exams of ST5/F105/SA5/F205. They are raw, unedited and contain a large amount of opinion. I've taken a skeptical approach to the subject and my views may not be correct. Feel free to correct me in the comment section below. I'll be releasing a new video every day ----------------------------- Let's Keep in Contact ----------------------------- Hit the subscribe button if you would like to see more on Youtube. Join our Actuarial Science Community on Facebook - https://bit.ly/2AyCN1p MJ’s Udemy courses - https://bit.ly/2AyCUtR MJ's awesome website - https://www.mjactuary.com -----------------------------
Views: 29330 MJ the Fellow Actuary
“Dust off the Credit Policy because the auditors are here” is a statement too often uttered in many organizations. In this webinar we will discuss the importance of credit policy to avoid organizational conflict and mitigate risk. We will also consider and address these questions: How to quantify identified risk using financial statements, credit reports and other intelligence; credit enhancement guidelines in credit policy; customer profitability in credit decisions; internal credit ratings, bad debt reserves, asset classifications and write-offs; delegation of authority; and bad debt loss accountability. About the speaker: Gerald Sahd has 25 years of experience in the credit industry in areas such as consumer collection, Commercial Lending, Institutional Banking, Private Banking, Commercial Real Estate, Trade Credit and Captive Financing. A graduate of the University of New Mexico in finance and economics, he also earned a Commercial Banking Certificate in 1993 from the Western States School of Banking. He also completed several advance commercial lending courses at BofA’s Training Center in San Francisco, California. Some of the positions Gerald has held include Vice President of Commercial Banking at BofA, Director of Franchise Financial Services at Medicine Shoppe, Int. and Credit Administration Officer at Cardinal Health. Currently he serves as Manager of Global Credit at Novus International where he oversees credit operations in Brussels Belgium; Indiatuba, Brazil, Shanghai, China; Chennai, India. Gerald serves as Educational Chair for Forius Agri-Credit Business Group
Views: 2485 Credit2B
Financial data analysis is as much a broad area as Finance. You can use it for managing/mitigating different types of financial risk, taking decisions on investment, managing portfolio, valuing assets etc. Below are a few beginner level projects you can try working on. 1- Build a Credit Scorecard Model - Credit scorecards are basically used to assess credit worthiness of customers. Use German Loan data-set (publicly available credit data) to build credit scorecard for customers. The data set has historical data on default status of 1000 customers and the different factors that are possibly correlated with the customer’s chances of defaulting such as salary age, marital status etc. and attributes of the loan contract such as term, APR rate etc. Build a classification model (using techniques like Logistic Regression, LDA, Decision Tree, Random Forest, Boosting, Bagging) to classify good and bad customers (default and non default customers) and use the model to score new customers in future and lend to customers that have a minimum score. Credit scorecards are heavily used in the industry for taking decisions on grating credit, monitoring portfolio, calculating expected loss etc. 2- Build a Stock Price Forecasting Model - These models are used to predict price of a stock or an index for a given time period in future. You can download stock price of any of the publicly listed companies such as Apple, Microsoft, Facebook, Google from Yahoo finance. Such data is known as uni-variate time series data. You can use ARIMA (AR, MA, ARMA, ARIMA) class of models or use Exponential Smoothing models. 3- Portfolio Optimization Problem - Assume you are working as an adviser to a high net worth individual who wants to diversify his 1 million cash in 20 different stocks. How would you advise him? you can find 20 least correlated stocks (that mitigates the risk) using correlation matrix and use optimization algorithms (OR algos) to find out how you would distribute 1million among these 20 different stocks. 4- Segmentation modelling - Financial services are increasingly becoming tailored made. Doing so helps banks in targeting customers in a in a more efficient way. How do banks do so? They use segmentation modelling to cater differently to different segments of customers. You need historical data on customer attributes & data on financial product/services to build a segmentation model. Techniques such as Decision Trees, Clustering are used to build segmentation models. 5- Revenue Forecasting - Revenue forecasting can be done using statistical analysis as well (apart from the conventional accounting practices that companies follow). You can take data for factors affecting revenue of a company or a group of companies for a set of periods of equal interval (monthly, Quarterly, Half year, annual) to build a regression model. make sure you correct for problem of auto-correlation as the data has time series component and the errors are likely to be correlated (that violates assumptions of regression analysis) 6- Pricing Financial Products : You can build models to price financial products such as mortgages, auto loans, credit card transactions etc. (pricing in this case would be charging right interest rate to account for the risk involved, earn profit from the contract and yet be competitive in the market). You can also build models to price forward, future, options, swaps (relatively more complicated though) 7- Prepayment models - Prepayment is a problem in loan contracts for banks. Use loan data to predict customers could potentially prepay. You can build another model in parallel to this to know if a customer prepays, when is he likely to prepay in the life time of the loan (time to prepay). You may also build a model to know how much loss the company would incur if a section of the portfolio of customer prepay in future. 8 - Fraud Model - These models are being used to know if a particular transaction is a fraudulent transaction. Historical data having details of fraud and non-fraud transactions can be used to build a classification model that would predict chances of fraud happening in a transaction. Since we normally have high volume of data, one can try not just relatively simpler models like Logistic Regression or Decision trees but also should try more sophisticated ensemble models. ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity
Views: 4017 Analytics University
On November 12, 2015, Trevor Harris, Arthur J. Samberg Professor of Professional Practice Accounting at Columbia Business School, presented The Expected Rate of Credit Losses on Banks’ Loan Portfolios. The presentation was part of the Program for Financial Studies' No Free Lunch Seminar Series titled Current Research on Issues in Bank Valuations. The Program for Financial Studies' No Free Lunch Seminar Series provides broader community access to Columbia Business School faculty research. At each seminar, attended by invited MBA and PhD students, faculty members introduce their current research within an informal lunch setting. Learn more at http://www.gsb.columbia.edu/financialstudies/
Views: 717 Columbia Business School
We turned the Webinar microphone over to LoanPricingPRO® client Michael Scheopner, Chief Risk Officer of Landmark National Bank, Manhattan, Kansas, (now CEO of the bank), to hear him tell how the bank went from a 4% to a 12% ROE in just two years, improving pricing and decreasing risk. With the help of Austin Associates and LoanPricingPRO® , the bank implemented credit risk adjusted ROE profit targets that were specific by loan size and by product type, and which bore a close relationship to the current actual ROE’s of the commercial loan portfolio for loans of same size. Existing loans previously earning ROE’s of 5% ‐ 13%, were increased quickly to 6% ‐ 16%, which helped to move the bank as a whole from 4% to 12% overall. Michael explains in his own words how this change was implemented and how it helped the bank recover from the great recession of 2008.
Views: 203 ProBank Austin
RAROC is a risk-adjusted performance measure (RAPM): risk-adjusted return divided by economic capital (i.e., the capital reserved to cover unexpected losses). For more financial risk videos, visit our website! http://www.bionicturtle.com
Views: 43644 Bionic Turtle
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Jake Xia This lecture focuses on portfolio management, including portfolio construction, portfolio theory, risk parity portfolios, and their limitations. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 531867 MIT OpenCourseWare
"Four Keys to Lending” will discuss loan products, pricing, underwriting and collections.
Views: 582 NCUAchannel
This short revision video looks at the basic balance sheet of a commercial bank. This is a basic model of the balance sheet of a commercial bank. Assets are “owned” by the bank. Liabilities are “owed” by the bank e.g. customers can walk into a bank or use an ATM machine to withdrawal some/all of their deposits. - - - - - - - - - MORE ABOUT TUTOR2U ECONOMICS: Visit tutor2u Economics for thousands of free study notes, videos, quizzes and more: https://www.tutor2u.net/economics A Level Economics Revision Flashcards: https://www.tutor2u.net/economics/store/selections/alevel-economics-revision-flashcards A Level Economics Example Top Grade Essays: https://www.tutor2u.net/economics/store/selections/exemplar-essays-for-a-level-economics
Views: 28337 tutor2u
In this video series-1, Richard Turner, the former Chief Credit Officer, ShoreBank, shares his "tricks of the trade" for loan analysis. Richard explains the Lending Process as an intersection of the three circles: Market, Management and Mathematics. He further explains the dynamic relation between the three components. Turner has been involved in making loans and training credit staff for well over 30 years. Much of that experience was spent in the United States but also in a number of foreign countries, primarily in Eastern Europe.
Views: 1332 MicroSave
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Views: 2325 Chico Crypto
Effective management of the loan portfolio and the credit function is fundamental to a bank's safety and soundness. According to the FDIC's Manual of Examinations Policy, "the examiner's evaluation of a bank's lending policies, credit administration, and the quality of the loan portfolio is among the most important aspects of the examination process." For more information, and to register for the workshop, click here: http://web.cbaofga.com/events/Credit-Administration-Workshop-9141516-4408/details
Views: 120 cbaofga
Even with strong credit analysis approaches and tools, a good SME loan can become a problem loan. How can a financial institution manage problem loans and prevent further deterioration of the bank’s loan portfolio? This webinar will provide techniques and tools to identify, measure, and manage problem loans. This webinar discusses: · Early warning signs of a non-performing loan (NPL) · Tools to identify and measure NPLs · Approaches and tools needed by loan officers to manage problem loans · Protections your FI should have in place to prevent further deterioration of the portfolio
Views: 634 CapitalPlus Exchange (CapPlus)
Manage Rate Risk through Interest Rate Swaps, Caps and Floors Join Martin McConnell, Banking Consultant with Provident Risk Management, for a look at hedging strategies that can assist banks looking to minimize interest rate risk. Whether you are a bank that is considering the use of derivatives, or if you currently use derivatives, this informative webcast will provide you with insight into portfolio hedging, liability hedging, commercial loan hedging and the importance of independent valuation. This webcast, the first in an ongoing series on derivatives use within banks, will introduce hedging concepts for banks, and the risk management solutions needed to support hedging activities. Community banks, smaller regionals, building societies, credit unions and more will benefit from a close look at interest rate hedging through the use of swaps, caps and floors as well as key steps to ensure a successful derivatives strategy.
Views: 16874 FINCADAnalyticsSuite
IBISWorld provides industry intelligence that helps Commercial Banks support their Relationship Managers, Credit Analysts and Loan Officers to create more loans, better loans and more diversified loans. With detailed analysis on hundreds of industries, we help you understand your clients’ business operating risks and trends through performance data and analysis on the market; supply chain information; forecasts; risk scores; operating strengths and weaknesses; analysis of external drivers; major player market strategies and industry profit and cost benchmarks. Also, our Risk model provides an early warning of changing risk and “stress testing” capabilities that allow you to monitor the impact of changing external variables on your loan portfolio.
Views: 629 IBISWorld
While the appropriate level of complexity of a Bank’s Risk Measurement System is specific to each institution and portfolio type – and we know that- one size does not fit all -- we are seeing more and more Banks adopting a “dual risk ratings” process. I should note that a risk rating should not be confused with a credit rating issued by a credit rating agency. In this dual system, the probability of default (PD) is estimated separately from the loss given default (LGD). The expected loss for a given loan is then calculated as their product. This method is also among several valid options for estimating expected credit loss explicitly contemplated in F.A.S.B.’s (Financial Accounting Standards Board) proposed standards update, called the current expected credit loss (CECL) model. This indicates that Banks are already thinking about ways to replace the existing incurred-loss estimation approach to an expected loss type of model. And as we know, the allowance for credit losses is one of the most significant estimates on a Bank’s financial statement and regulatory report because it has a direct impact on earnings. Dual risk rating systems that separate PD and LGD assessments have initially emerged because a single risk rating may not support all of the functions that require credit risk evaluations. Borrower risk ratings typically support deal structuring and administration, while facility risk ratings support Allowance for Loan and Lease Losses (ALLL) and capital estimates. So how do Banks build these systems in practice? For banks with sufficient internal data, PD, LGD, and EAD models are typically based in large part on the bank’s own historical default and loss experience. However, most banks lack sufficient data for creating such models. Banks that find themselves in this situation have several options: • First, it is often possible, for example for Community Banks with relatively straightforward portfolios, to estimate expected loss directly (without going into individual components), based on their historical loss experience coupled with a judgmental assessment of the current economic environment. • Second, banks can build robust custom PD or LGD models based on external data, such as that which is provided by S&P Global Market Intelligence, which can be sampled in such a way as to represent the bank’s own portfolio. • And the third option for banks facing data constraints is the use of vendor models and scorecards to estimate PD and LGD—and, in turn, expected loss. These models should be reviewed to make sure they are appropriate relative to the composition of the bank’s loan portfolio. Out-of-sample validation, calibration, and benchmarking are all common exercises we perform to ensure that the model is applicable. In practice, we find that often times banks prefer to rely on vendor models and scorecards regardless of their internal data situation, since such models have undergone model validation, are maintained by the vendor, and represent leading industry practices. In summary, the dual risk rating system requires a risk rating on the credit worthiness of the borrower and a risk rating based on the facility of the loan. The two risk ratings are then combined using a matrix such as the one shown on this slide to develop an overall composite loan quality risk rating. That’s all for today, but if you are interested in learning more about this topic, or any of the solutions we covered, please complete the short form that will appear on your screen. Thank you for watching.
Views: 1789 S&P Global Market Intelligence
Through a case study, this video explains the method, as to how to calculate capital requirement for a asset portfolio of a bank. Very useful for CAIIB exam and JAIIB exam
Views: 82232 Ns Toor
Learn more about credit risk modeling with R: https://www.datacamp.com/courses/introduction-to-credit-risk-modeling-in-r Hi, and welcome to the first video of the credit risk modeling course. My name is Lore, I'm a data scientist at DataCamp and I will help you master some basics of the credit risk modeling field. The area of credit risk modeling is all about the event of loan default. Now what is loan default? When a bank grants a loan to a borrower, which could be an individual or a company, the bank will usually transfer the entire amount of the loan to the borrower. The borrower will then reimburse this amount in smaller chunks, including some interest payments, over time. Usually these payments happen monthly, quarterly or yearly. Of course, there is a certain risk that a borrower will not be able to fully reimburse this loan. This results in a loss for the bank. The expected loss a bank will incur is composed of three elements. The first element is the probability of default, which is the probability that the borrower will fail to make a full repayment of the loan. The second element is the exposure at default, or EAD, which is the expected value of the loan at the time of default. You can also look at this as the amount of the loan that still needs to be repaid at the time of default. The third element is loss given default, which is the amount of the loss if there is a default, expressed as a percentage of the EAD. Multiplying these three elements leads to the formula of expected loss. In this course, we will focus on the probability of default. Banks keep information on the default behavior of past customers, which can be used to predict default for new customers. Broadly, this information can be classified in two types. The first type of information is application information. Examples of application information are income, marital status, et cetera. The second type of information, behavioral information, tracks the past behavior of customers, for example the current account balance and payment arrear history. Let's have a look at the first ten lines of our data set. This data set contains information on past loans. Each line represents one customer and his or her information, along with a loan status indicator, which equals 1 if the customer defaulted, and 0 if the customer did not default. Loan status will be used as a response variable and the explanatory variables are the amount of the loan, the interest rate, grade, employment length, home ownership status, the annual income and the age. The grade is the bureau score of the customer, where A indicates the highest class of creditworthiness and G the lowest. This bureau score reflects the credit history of the individual and is the only behavioral variable in the data set. For an overview of the data structure for categorical variables, you can use the CrossTable() function in the gmodels package. Applying this function to the home ownership variable, you get a table with each of the categories in this variable, with the number of cases and proportions. Using loan status as a second argument, you can look at the relationship between this factor variable and the response. By setting prop.r equal to TRUE and the other proportions listed here equal to FALSE, you get the row-wise proportions. Now what does this result tell you? It seems that the default rate in the home ownership group OTHER is quite a bit higher than the default rate in, for example, the home ownership group MORTGAGE, with 17.5 versus 9.8 percent of defaults in these groups, respectively. Now, let's explore other aspects of the data using R.
Views: 32196 DataCamp
In this video, I analyze the merger between IDFC bank and Capital First, known as IDFC First ---- Link to my stock market course : https://sagarrv.com/ ---- Capital First was lead by Mr. Vaidyanathan . He had 27 years of experience in retail and he will be leading the new entity. IDFC bank had a loan book more concentrated in infrastructure and corporate loans. The new entity will focus more on retail and wholesale with the goal of 70 % of their portfolio in retail and the rest in wholesale. Connect with me: Website : https://sagarrv.com/ Twitter: https://twitter.com/SagarRV1 Email: [email protected] #IDFCbank#CapitalFirst#IDFCFirst
Views: 38003 Dr. Sagar RV
Welcome to the Investors Trading Academy talking glossary of financial terms and events. Our word of the day is “Concentration Risk”. Concentration risk is the risk posed to a financial institution by any single or group of exposures which have the potential to produce losses large enough to threaten the ability of the institution to continue operating as a going concern. In other words, it's the opposite of a diversified portfolio. For example, an institution may have a concentration of loans in a certain geographic area. If that area experienced an economic downturn an unexpected volume of defaults might occur, which could result in significant losses to or failure of the institution. Or an institution may have a concentration in a certain type of lending, for example construction lending. If construction slows unexpectedly, the impact to the institution could be significant. By their very nature community banks and credit unions have some degree of concentration risk; geographically, within their customer/member base, and by products they specialize in and offer. The smaller the geographic area served, the more limited the customer base is, and the fewer number of products offered all lead to increased concentration risk. Concentrations can also exist in asset categories, such as residential real estate, automobiles, business loans within asset categories, such as junior position home equity lines of credit within a residential category, indirect auto loans within an automobile category, or SBA loans within a business loans category, or as loan quality rating categories, such as a concentration of lower quality credits (loans). Lastly, concentrations can exist in seemingly unrelated categories. A classic example is a financial institution that invests in mortgage back securities in its investment portfolio, while at the same time investing in mortgage loans in its loan portfolio. By Barry Norman, Investors Trading Academy
Views: 2222 Investor Trading Academy
A number of banks in Europe are still experiencing high levels of non-performing loans (NPLs). These NPL levels have a negative impact on bank lending, internal resources, and capital constraints of a bank. The last two years we have seen a flurry of political and supervisory activities focused on tackling the overhang on NPLs in the European market which have led in disposals of loan portfolios by many banks. According to the "Transparency Exercise" of the European Banking Authority just over Euro 1 trillion of NPLs are still on bank's books in the Eurozone. We will give an overview about regulatory and political initiatives and discuss this with a focus on transaction specifics in Central Europe, Spain, Germany, Luxembourg, and Italy. Enhance your understanding of the specific features and legal issues of NPLs in various key European countries that may impact your company. (Live presentation was aired on 27 Sep 2017) http://www.deloitte.com/dbriefs/deloittelegal
Views: 1003 Deloitte Dbriefs Legal
How Do Banks Make Profit? is the part 1 of the 3 part series on Banking Stocks Fundamental Analysis. The fundamental analysis of the banking stocks is completely different from the Fundamental Analysis of other stocks. The financial ratios for banking stocks analysis are different from the traditional financial ratios. In the 2nd part of this series, we will learn the financial ratios for Banking Stocks Fundamental Analysis. In the 3rd part of this series, we will learn how to analyze the bank stocks. For a complete analysis, it is important to understand the cost and revenue of the banks. The cost includes the interest on deposits and the cost of running bank operations. The revenue or income for the bank includes interest income from the loans and other income from the distribution of financial products & fees from the services offered by the bank. To earn a profit, the interest income from the loan should be more than interest paid on the deposits. The banks can raise the deposits based on the net worth of the bank. Higher the net worth of the bank, higher the deposit banks can raise. The total capital raised depends on the financial leverage or equity multiplier. Financial leverage equals total capital divided by the net worth. If you liked this video, You can "Subscribe" to my YouTube Channel. The link is as follows https://goo.gl/nsh0Oh By subscribing, You can daily watch a new Educational and Informative video in your own Hindi language. For more such interesting and informative content, join me at: Website: http://www.nitinbhatia.in/ T: http://twitter.com/nitinbhatia121 G+: https://plus.google.com/+NitinBhatia #NitinBhatia
Views: 17736 Nitin Bhatia
Watch a step-by-step example that illustrates how to use MATLAB® to perform stress testing based on economic scenarios. Download the Code Used in this video: http://bit.ly/2KtojSS Learn more about MATLAB for Finance and Risk Management: http://bit.ly/2KrVtSX This video uses a simplified loan portfolio dataset to make it easier to understand the workflow. The example shows how to: • Import data into the MATLAB workspace • Join two tables together using the outerjoin function • Fit data using a generalized linear model • Predict the probability of default (PD) based on the fitted model and adverse economic scenarios • Calculate the expected loss using predicted PD Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders.
Views: 1463 MATLAB
Support this podcast through your donation: https://paypal.me/valueinvesting I will create three episodes to explain the basics of analyzing bank stocks which are a black box to many investors. This episode covers the bank business model in terms of how they make money, what deposit structure and lending portfolio mix are generally good for investors. Additionally, I explain why it is important for investors to look at Common Equity Tier 1 (CET1) ratio and understand the current capital level of a bank. The next two episodes will cover how you can analyze the financial statements of a bank and how you can identify undervalued banks in the stock market. Click here (https://amzn.to/2JnWsm3) to learn more about the book referenced in this episode.
Views: 36 Jun Kim, CFA
Deep Future Analytics, an advanced analytics CUSO that provides modeling, data pooling, and CECL analysis for credit unions and community banks, will be presenting its new CECL Strategy Software. DFA’s software performs all the portfolio management functions that you need to effectively manage your portfolio. Deep Future Analytics’ CECL Strategy Software is the newest element of its Portfolio Management Tool. It enables easy creation of user-selected economic and origination scenarios, and generates scenario-based forecasts on any variable / segment / vintage. The integrated solution provides real-time validation, creates documentation needed for regulators, and projects price margins with user-supplied rate sheets, economic scenarios. DFA’s algorithms provide custom forecasts at the loan level for your institution. And, our shared anonymized data pool provides you with the benefit of our ‘data scale’, resulting in improved forecasts. With CECL Strategy Software you will be able to: At the Loan Level create: Collections Watch List – Ranks your loans from most likely to default to least, better informing your collections staff LOC Opportunities – Ranks credit card accounts from least likely to default to most, helping your marketing department or call center to determine which members are the best candidates for an offer of a line of credit increase. Payoff Predictor – Knowing the probability of attrition for the months ahead allows your marketing department to focus their attention on those auto loans most likely to pay off early. Pricing and Risk Matrix Heat Maps – allow you to price for profitability, with a quantified, granular view of the risk for new originations. At the Portfolio Level create: Risk Monitoring – Understand the drivers of past performance. ALLL – Next 12 months or next calendar year Lifetime Loss Rate – Know your ALLL under the proposed CECL regulation. Stress Tests – Scenario driven, delivers all of the loan level and portfolio outputs given extreme economic scenarios. Joseph Breeden, DFA’s COO and Chief Scientist will demonstrate: Selecting economic and origination scenarios Flexible viewing of historical portfolio and economic data Analysis of all model components and fit statistics Forecasting scenarios on any variable / segment / vintage Real-time validation of all model components Calculating the CECL number, loss reserve calculations under the new rules Margin Projections with user-supplied rate sheets, economic scenarios and CECL loss estimates. Featuring Joe Breeden - Webinar.jpg.gif Joseph Breeden, COO and Chief Scientist, Deep Future Analytics, LLC; Founder, Prescient Models, LLC. Breeden brings more than 20 years of experience leading financial institutions through financial modeling, allowing clients to achieve real understanding of portfolio dynamics for retail lending where those problems originated. Previously he was co-founder of Strategic Analytic, where he led the design of advanced analytic solutions including the invention of Dual-time Dynamics. Dr. Breeden has created financial models through the Mexican Peso Crisis, Asian Economic Crisis, 2001 Global Recession, Hong Kong SARS Recession, US Mortgage Crisis, and the Global Financial Crisis. These crises have provided Dr. Breeden with a rare perspective on crisis management and the analytics needs of executives for strategic decision-making. He currently serves as associate editor for the Journal of Risk Model Validation. About Deep Future Analytics Deep Future Analytics brings advance predictive analytics to Credit Unions and Community Banks. We apply the power of the latest advances in modeling techniques and data pooling to your institution, and surpass the state of the art of many of the larger banks that are using legacy systems and out dated models.
Views: 276 OnApproach, LLC
Join our MemberShip Program for Exclusive Research Content: https://www.youtube.com/channel/UCPohbSYq4IXhv0yxiy-sT4g/join Make your FREE Financial Plan today: https://investyadnya.in Yadnya Book - 108 Questions & Answers on Mutual Funds & SIP - Available here: Amazon: https://goo.gl/WCq89k Flipkart: https://goo.gl/tCs2nR Infibeam: https://goo.gl/acMn7j Notionpress: https://goo.gl/REq6To Find us on Social Media and stay connected: Blog - https://blog.investyadnya.in Telegram - http://t.me/InvestYadnya Facebook Page - https://www.facebook.com/InvestYadnya Facebook Group - https://goo.gl/y57Qcr Twitter - https://www.twitter.com/InvestYadnya #InvestYadnya #YIA
Views: 12583 Yadnya Investment Academy
This video showcases GTBank's new internet banking interface. Features that our customer's can expect; 1. A more visually appealing and modern user interface 2. A dynamic Login number pad that changes with each page reload 3. A more user friendly "Add Services" page 4. A user friendly categorization of menu items for Internet banking function 5. A new default landing page with a dashboard showing trend analysis of customer transactions as follows - a. Deposit and Withdrawals on selected accounts over 12 months b. eChannel Usage shows analysis of (POS, ATM, Web payments, IVR, Mobile banking, internet banking) over 3 months c. Loan repayments showing total loan portfolio d. My Apps center to download Mobile Money App
Views: 39604 GTBank
Financing rental properties the right way is a video about the two most commonly used ways to finance rental properties for real estate investors. The first way to finance a rental property is Investor A who purchases a $100,000 property and leaves $20,000 in the deal. He starts with $100,000 capital to invest. After 5 houses leaving in $20,000 this investor will run out of money. Investor B finances his rental properties using the BRRRR method which stands for Buy Rehab Rent Refinance Repeat. You are buying a house at a discounted rate and then forcing the appreciation upwards and value up to where the house is appraised at $100,000. So say you bought it for $50,000 then had $20,000 in repairs and then $10,000 in carrying, financing, and closing costs your total liability is now $80,000. The bank will lend you $80,000 or 80% of the $100,000 appraised value loan to value. Now you have a financed house and your original capital to reinvest. You can do as many rent houses as you want now. financing rental properties I buying rentals I rental properties I landlords I financing houses I cash flow I rent houses I Connor Steinbrook I Investor Army I calculating rental numbers. Learn More About Our Home Study Program: Flip Army - How To Flip Houses The Investor Army Way https://info-investorarmy.clickfunnel... Contact us at: [email protected] For More Resources And Opportunities To Take Your Business To The Next Level Go To…… http://www.investorarmy.com/ Visit Our Other Youtube Channel “Investor Army Podcast” For More Videos By Connor Himself https://www.youtube.com/channel/UCmay... Follow Us On….. Facebook: https://www.facebook.com/InvestorArmy/ Twitter: https://twitter.com/Investorarmy Linkedin: https://www.linkedin.com/in/connor-steinbrook-58b2b9a1/ Google+: https://plus.google.com/u/0/108318927307224577838 iTunes: https://itunes.apple.com/us/podcast/investor-army-podcast/id1234085118 Blubrry: https://www.blubrry.com/investorarmypodcast/ Instagram: https://www.instagram.com/investor_army/?hl=en
Views: 94978 Investor Army