PayNet Absolute Expected Loss enables lenders to derive Probability of Default, Exposure at Default, and Loss Given Default values to calculate Expected Loss and to determine prudent and defensible reserves for losses.
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About PayNet Absolute Expected Loss
New Current Expected Credit Loss (CECL) regulations (FASB 825-15) that change the loan loss reserve estimation to lifetime Expected Loss (EL) pose new challenges. Optimizing loan loss reserves is one of the most important challenges that a financial institution faces. If a financial institution sets aside too much capital for reserves, it will unnecessarily limit the company’s potential profits due to lower than optimal loan volumes. However, if not enough capital is set aside, the institution’s financial viability is at risk if losses exceed the capital buffers.
A key component in the calculation of EL is Loss Given Default, the estimation of the probable recovery of a realizable asset in the event default occurs, given EL = Probability of Default (PD) x Exposure at Default (EAD) x Loss Given Default (LGD).
PayNet’s Absolute Expected Loss model provides lenders with powerful, consistent, and objective LGD estimates at a contract level that incorporate a large share of factors that are predictive of LGD. The model leverages PayNet’s extensive loss database consisting of 500,000 contracts, spanning two decades and presents 28 quarters of forecasted LGD estimates, in conjunction with PayNet AbsolutePD and EAD projections.
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PayNet Absolute Expected Loss is beneficial to four functions in the lending process.
PayNet Absolute Expected Loss presents 28 quarters of forecasted LGD estimates, in conjunction with PayNet AbsolutePD and EAD estimates to project Expected Loss.
PayNet Absolute Expected Loss can be used to optimally calibrate loan loss reserves in a variety of economic environments in a manner that is geared towards adhering to forthcoming regulatory guidelines.
PayNet’s Loss Given Default model provides a solution for lenders to estimate LGD for all commercial debt contracts in their portfolio in an empirically derived and statistically sound fashion based on the experiences of dozens of lenders and hundreds of thousands of contracts.