| |December 201919ANY LENDER WITH MULTIPLE SOURCES OF CAPITAL HAS TO DECIDE WHICH SOURCE SHOULD FUND EACH DOLLAR OF LENDINGanalysts to generate reporting for banks. This process works, but it is not optimal for three key reasons:1. Efficiency: Running a manu-al capital-allocation and reporting process requires significant work and a team that expands linearly with the number of capital sources. Organizations adopting a manual approach inevitably spend more time on report generation and fire drills than on strategic analysis and capital planning.2. Operational Risk: Hu-man-centric processes are prone to error, and the likelihood of er-ror increases with the inherent complexity of a growing business. In treasury and capital markets, the impact of an operational er-ror can be high. Mistakes such as double-pledging, mis-reporting, and incorrect collateral eligibility assessment can force a lender into technical default and potentially lead to insolvency.3. Yield Optimization: When an organization needs to spend so much of its time and attention pro-ducing reports, allocating loans, and avoiding human error, not nearly enough time is spent on fi-nancial optimization in matching capital to loan originations. De-pending on the lender, this can be a massive unseen opportunity cost.How to Build a More Efficient SystemAt Kabbage, it is only natural to apply our core competencies in technology and analytics to capital markets. Kabbage uses software developed at Orchard (now owned by Kabbage) to programmatical-ly evaluate thousands of capi-tal-to-loan allocation simulations each day and select the allocation that maximizes our ability to ex-tend credit to borrowers in the most capital efficient way. Following in this line of think-ing, capital markets processes can become more reliable by incorpo-rating principles from software engineering. Practices that can minimize the risk from human error include:· Unit testing: writing code to test discrete components of a pro-gram with certain inputs and com-pare the results to an expectation. For instance, if the eligible receiv-ables for a warehouse facility mul-tiplied by the advance rate should always equal the collateral base, then a unit test would compare the actual values in a borrowing base report to the expected calculation. The test can then generate alerts when something does not match, avoiding a situation where a lender sends an incorrect report to a bank.· Version control: the use of software to track changes in code and understand how various ver-sions of a technical system have evolved, particularly if multiple people are working on a project. For instance, if rules and logic are stored in spreadsheets, it is hard to track who made a particular change at a particular time. By con-trast, if rules and logic are stored in code under a version control sys-tem, it is possible to see a history of all changes ever made to a piece of code. This facilitates collaboration across multiple developers--from proposing to reviewing and accept-ing changes to code.· Data Integrity Automation: the development of systems to constantly monitor data for com-pleteness, structure, and accuracy. With all the data that exists with-in a modern lending business, it is impossible to check for every po-tential inconsistency without auto-mation. If there are low-probability but high-impact errors that could happen within data, it makes sense to develop code to check for them constantly and generate alerts if and when they occur.The past several years have demonstrated that the capital mar-kets for lending will evolve through incremental change rather than overnight revolution. While assets like equities, currencies, and com-modities--as well as non-financial markets such as online advertis-ing--can often trade in fully-auto-mated, data-driven markets, the path is more difficult with loans, which are less-fungible assets that have a long history of manual anal-ysis and complex structuring. Fur-thermore, the most common vehi-cles for large-scale financing of lending activities require not only the ability to analyze data and pre-dict risk, but a sophisticated knowl-edge of the capital markets, deep relationships with providers of capital, and the ability to establish trust. Fortunately, the technology now exists to automate and opti-mize the mathematical and proce-dural aspects of loan funding, while freeing capital markets and treasury teams to focus on the im-portant work such as ensuring that credit can be extended to borrowers in the most efficient way.
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