Terms Beginning With 'f'

Fair Debt Collection Practices Act (FDCPA)

  • January 02, 2020
  • Team Kalkine

Fair Debt Collection Practices Act (FDCPA) protects the debtors from the collectors ? from means and mode of collection and the time at which the contact be made along with shielding against unwanted beyond the limit call. It limits the conduct and procedures of third-party debt collectors on behalf of another person or entity.

Net amount after factoring in all debits and credits in a financial repository at a given moment. If an account balance drops below zero, it demonstrates a net debt.

What are accounts payable? Accounts payable is the amount of cash a company is liable to pay to its suppliers and clear dues. As current liabilities of the company, accounts payable is required to be settled over the next twelve months.  It also shows the obligations of the business over the next year. Accounts payable is required to be repaid in a short period, depending on the relationship with suppliers. It is essentially a kind of short term debt, which is necessary to honour to prevent default.  As a part of the company’s working capital, it is widely used in analysing the cash flow of the business and cash flow trends over a period. Accounts payable may also depict the bargaining power of the company with its vendor and suppliers.  A vendor or supplier may give the customer longer credit period to settle the cash compared to other customers. The customer here is the company, which will incur accounts payable after buying goods on credit from the vendor.  There could be many reasons why the vendor is providing a more extended credit period to the firm such as long term relationship, bargaining power of the firm, strategic needs of the vendor, the scale of goods or services.  By maintaining a more extended repayment period to supplier and shorter cash realisation period from the customer, the company would be able to improve the working capital cycle and need funds to support the business-as-usual.  However, prudent working capital management calls for not overtly stretching the payable days as it might lead to dissatisfaction of supplier. Also, investors tend to closely watch the payable days cycle to determine the financial health of the business. When the financial conditions of a firm deteriorate, the management tend to delay the payment to their suppliers. What is accounts payable turnover ratio? Accounts payable turnover ratio shows the capability of a firm to pay cash to its customer after credit purchases. It is counted as an essential ratio to analyse the cash management attribute of the firm and its relationship with vendors or suppliers.  It is calculated by dividing purchases by average accounts payable.  Purchases by the company are calculated as the sum of the cost of sales and net inventory in a given period:  Now let’s understand this the help of an example. Let us suppose, Cost of sales of Company XYZ for the period was $60,000, and XYZ began with inventories worth $21000 and ended at $15000. Accounts payable at the beginning was $20000, and $15000 at the end.  Now the purchases will be $66000 (60000+21000-15000). The average accounts payable will be $17500. Therefore, the accounts payable turnover ratio will be 3.77x.  Dividing the number of weeks in a year by the accounts payable turnover ratio will give the number of weeks the company takes on average to settle its payables. In this case, it will be around 13.8 weeks (52/3.77). 

What is Data Mining? Data mining is a process that facilitates the extraction of relevant information from a vast dataset. The process helps to discover a new, accurate and useful pattern in the data to derive helpful pattern in data and relevant information from the dataset for organization or individual who requires it. Key Features of data mining include: Based on the trend and behaviour analysis, data mining helps to predict pattern automatically. Predicts the possible outcome. Helps to create decision-oriented information. Focuses on large datasets and databases for analysis. Clustering based on findings and a visually documented group of facts that were earlier hidden. How does data mining work? The first step of the data mining process includes the collection of data and loading it into the data warehouse. In the next step, the data is stored and managed on cloud or in-house servers. Business analyst, data miners, IT professionals or the management team then extracts these data from the sources and accordingly access and determine the way they want to organize the data. The application software performs data sorting based on user’s result. In the last step, the user presents the data in the presentable format, which could be in the form of a graph or table.         Image Source: © Kalkine Group 2020 What is the process of data mining? Multiple processes are involved in the implementation of data mining before mining happens. These processes include: Business Research: Before we begin the process of data mining, we must have a complete understanding of the business problem, business objectives, the resources available plus the existing scenario to meet these requirements. Having a fair knowledge of these topics would help to create a detailed data mining plan that meets the goals set up by the business. Data Quality Checks: Once we have all the data collected, we must check the data so that there are no blockages in the data integration process. The quality assurance helps to detect any core irregularities in the data like missing data interpolation. Data Cleaning: A vital process, data cleaning costumes a considerable amount of time in the selection, formatting, and anonymization of data. Data Transformation: Once data cleaning completes, the next process involves data transformation. It comprises of five stages comprising, data smoothing, data summary, data generalization, data normalization and data attribute construction. Data Modelling: In this process, several mathematical models are implemented in the dataset. What are the techniques of data mining? Association: Association (or the relation technique) is the most used data mining technique. In this technique, the transaction and the relationship between the items are used to discover a pattern. Association is used for market basket analysis which is done to identify all those products which customer buy together. An example of this is a department store, where we find those goods close to each other, which the customers generally buy together, like bread, butter, jam, eggs. Clustering: Clustering technique involves the creation of a meaningful object with common characteristics. An example of this is the placement of books in the library in a way that a similar category of books is there on the same shelf. Classification: As the name suggests, the classification technique helps the user to classify and variable in the dataset into pre-defined groups and classes. It uses linear programming, statistics, decision tree and artificial neural networks. Through the classification technique, we can develop software that can be modelled so that data can be classified into different classes. Prediction: Prediction techniques help to identify the dependent and the independent variables. Based on the past sales data, a business can use this technique to identify how the business would do in the future. It can help the user to determine whether the business would make a profit or not. Sequential Pattern: In this technique, the transaction data is used and though this data, the user identifies similar trends, pattern, and events over a period. An example is the historical sales data which a department store pulls out to identify the items in the store which customer purchases together at different times of the year. Applications of data mining Data mining techniques find their applications across a broad range of industries. Some of the applications are listed below: Healthcare Education Customer Relationship Management Manufacturing Market Basket Analysis Finance and Banking Insurance Fraud Detection Monitoring Pattern Classification Data Mining Tools Data mining aims to find out the hidden, valid and all possible patterns in a large dataset. In this process, there are several tools available in the market that helps in data mining. Below is a list of ten of the most widely used data mining tools: SAS Data mining Teradata R-Programing Board Dundas Inetsoft H3O Qlik RapidMiner Oracle BI

What is EBITDA? Earnings Before Interest, Taxes, Depreciation, and Amortisation (EBITDA) is a widely used financial metric in evaluating cash flows and profitability of a business. Market participants closely track EBITDA and apply it in decision making extensively. Although conventional investors like Charlie Munger had raised concerns over the use of EBITDA, it is very popular in markets, and M&A transactions are mostly priced on EBITDA-based valuation like EV/EBITDA (x). EBITDA is not recognised by IFRS and GAAP but is used extensively in the Corporate Finance world. It is now a mainstream financial metric that companies look to target. EBITDA depicts operational cash generation capacity of a firm in a given period. It acts as an alternative to financial metrics like revenue, profit or earnings per share. EBITDA allows to evaluate a business operationally and outcomes of operating decisions. Non-operating items are excluded to arrive at EBITDA. EBITDA excludes the impact of capital structure or debt/equity, and non-cash expenses like depreciation and amortisation. A particular criticism of EBITDA has been the inappropriate outlook of capital intensive businesses, which incur large depreciation expenses. Business with large assets incurs substantial costs related to repair and maintenance, which are not captured in EBITDA because depreciation expenses are accounted to calculate EBITDA. Meanwhile, EBITDA can paint an appropriate picture for asset-light business with lower capital intensity. While revenue, profit and earning per share remain sought-after headline generators for corporates, EBITDA has also found its growing application in the corporate finance world and is now a mainstream metric to evaluate a business financially. Perhaps the growth of asset-light business models has also added to the use of EBITDA. Its debt-agnostic approach to evaluate businesses has given reasons to investors, especially for high growth firms during capital expenditure cycles. But EBITDA has been present for close to four decades now. In the 1980s, the growth in corporate takeovers through leverage buyout transaction was on a boom. EBITDA grew popular to value heavy industries like broadcasting, telecommunication, utilities. John Malone is credited for coining this term. He was working at TCI- a cable TV provider. Since EBITDA has remained an important metric to determine purchase price multiples and is highly used in M&A transactions. EBITDA’s application in large businesses with capital intensive assets that are written down over a long period has been a source of concern for many investors. Although EBITDA is an effective metric to evaluate the profitability of a firm, it does not reflect actual cash flow picture of a firm during a period. Also, it does not account for capital expenditures of the firm, which are crucial in successfully running a business. EBITDA does not give a fair cash flow position because it leaves out crucial items like working capital, debt and interest repayments, fixed expenses, capital expenditure. At the outset, there can be times when EBITDA may overstate performance, value and ability to repay debt. How to calculate EBITDA? NPAT: Net Profit after tax is the amount reported by a firm in the given period. It is present on the income statement of the firm and is used in the calculation of earnings per share of an entity. To calculate EBITDA, interest expense, tax, depreciation and amortisation are added to NPAT. Interest Expense: Firms can employ debt in their capital structure, and interest expense is funds paid to lenders as interest costs on principal debt. Most companies have different financing structure, and excluding interest payments enable comparing firms on operating grounds through EBITDA. Tax: Firms also pay income tax on profits. Excluding taxes gives a fair picture of the operating performance of the business since tax vary across jurisdictions, and sometimes according to size of business as well. Depreciation: Depreciation is the non-cash expense to account for the steady reduction in value of tangible assets. Firms can incur depreciation expense on machinery, vehicles, office assets, equipment etc.  Amortisation: Amortisation is the non-cash expense to account for the reduction in the value of intangible assets like patents, copyrights, export license, import license etc. Operating Profit: Operating profit is the core profit of a firm generated out of operations. It includes cash and non-cash expenses of a firm, excluding income tax and interest expenses. Operating Profit is also called Earnings Before Interest and Tax (EBIT). Read: EBIT vs EBITDA What is TTM EBITDA and NTM EBITDA? Trailing Twelve Months (TTM) or Last Twelve Months (LTM) EBITDA represents the EBITDA of the past twelve months of the firm. It allows to review the last operation performance of the business. Whereas NTM EBITDA represents 12-month forward forecast EBITDA of the firm. NTM EBITDA is also one-year forward EBITDA. Market participants are provided with consensus analysts’ estimates for a firm, which also include NTM EBITDA, NTM EPS, NTM Net Income or NPAT. What is EBITDA margin? EBITDA margin is the percentage proportion of a firm EBITDA against total revenue. It indicates the operational profitability of the firm and cash flows to some extent. If a firm has a higher margin, it means the level of EBITDA against revenue is higher. It is widely used in comparing similar companies and enable to evaluate businesses relatively. If a firm has a total revenue of $1 million and EBITDA is $800k, the EBITDA margin is 80%. What is adjusted EBITDA? Adjusted EBITDA is calculated to provide a fair view business after adding back non-cash items, one-time expenses, unrealised gains and losses, share-based payments, goodwill impairments, asset write-downs etc.

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