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Underwriting
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==Bank underwriting== In [[banking]], underwriting is the detailed [[Credit (finance)|credit]] analysis preceding the granting of a [[loan]], based on credit information furnished by the borrower; such underwriting falls into several areas: *Consumer loan underwriting includes the verification of such items as employment history, salary and [[financial statements]]; publicly available information, such as the borrower's credit history, which is detailed in a [[credit report]]; and the lender's evaluation of the borrower's credit needs and ability to pay. Examples include [[mortgage underwriting]]. *Commercial (or business) underwriting consists of the evaluation of financial information provided by small businesses including analysis of the business balance sheet including tangible net worth, the ratio of debt to worth (leverage) and available liquidity (current ratio). Analysis of the income statement typically includes revenue trends, gross margin, profitability, and [[Debt Service Coverage Ratio|debt service coverage]]. Underwriting can also refer to the purchase of [[corporate bond]]s, [[commercial paper]], government securities, municipal general-obligation bonds by a [[commercial bank]] or dealer bank for its own [[Deposit account|account]] or for resale to investors. Bank underwriting of corporate securities is carried out through separate holding-company affiliates, called [[securities affiliate]]s or Section 20 affiliates.{{Citation needed|date=June 2023}} Of late, the discourse on underwriting has been dominated by the advent of [[machine learning]] in this space. These profound technological innovations are altering the way traditional underwriting scorecards have been built, and are displacing human underwriters with automation. [[Natural language understanding]] allows the consideration of more sources of information to assess risk than used previously.<ref>{{cite web|url=https://www.businessnewsdaily.com/10203-artificial-intelligence-insurance-industry.html|author=Adam C. Uzialko|title=Artificial Insurance? How Machine Learning is Transforming Underwriting|date=September 11, 2017|access-date=April 28, 2019|publisher=Business News Daily}}</ref> These algorithms typically use modern data sources such as SMS / Email for banking information, location data to verify addresses, and so on. Several firms are trying to build models that can gauge a customer's willingness to pay using social media data by applying natural language understanding algorithms which essentially try to analyse and quantify a person's popularity / likability and so on, with the premise being that people scoring high on these parameters are less likely to default on a loan. However, this area is still vastly subjective.{{Citation needed|date=June 2023}}
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