In Brief

Managing Profitability Under CECL Through Loan Pricing (Part 2)

BY BEN MURRELL, FI CONSULTING Part 2: Applying the Conceptual Loan Pricing Framework to CECL As organizations implement CECL, a key question is how CECL estimates should factor into loan origination and pricing decisions. In Part 1 of this series, we illustrated the mechanics of pricing with a hypothetical loan. In Part 2, we use......

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Managing Profitability Under CECL Through Loan Pricing (Part 1)

BY BEN MURRELL, FI CONSULTING Part 1: Defining a Conceptual Framework for Loan Pricing Background As organizations implement CECL, a key question is how CECL estimates should factor into loan origination and pricing decisions. Linkage is important because CECL will require that expected losses be reserved at time of origination. Correctly pricing in new reserves......

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White Paper: Four Pitfalls to Avoid During CECL Implementation

BY MARK JORDAN, FI CONSULTING Building a durable, defensible Current Expected Credit Loss (CECL) process that reflects your organization’s view of its credit risk requires painstaking focus on the fundamentals of building and defending your bank’s own view of its risk exposure. This requires bringing a disciplined and methodical approach to building the “case” for......

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A Framework for Multiple Economic Scenarios Under CECL

BY ROBERT CHANG AND MARK JORDAN, FI CONSULTING Background We present a pragmatic approach to generating multiple economic scenarios for the new FASB current expected credit loss (CECL) accounting standard. While the guidance does not explicitly mention the number of scenarios that should be used when measuring expected credit losses, financial institutions should consider a......

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Four Key Questions When Estimating Current Expected Credit Losses (CECL)

BY ROBERT CHANG AND MARK JORDAN, FI CONSULTING Under the FASB current expected credit loss (CECL) accounting standard, public entities are required to estimate losses over the contractual term of the financial asset or group of financial assets. However, this requirement is eased to allow entities to make or obtain “reasonable and supportable” forecasts of......

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Banks with Ontology and Knowledge Graph Capabilities well-positioned to meet…

FI Consulting’s Mark Jordan co-authored a blog with Element 22 discussing the importance of data governance and accessibility. Recent surveys find that many institutions are still in the planning stage for CECL, and that obtaining data necessary to support model development is among their most pressing tasks. In the recent past certain banks have met......

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Take An Integrated View of Mission, Cost, and Risk in…

BY JOSH MEIKRANTZ, FI CONSULTING Introduction Federal credit agencies expend resources—in the form of costs and risk— in order to achieve policy missions. While agencies measure program credit costs and performance against mission, typically these efforts are undertaken independently and risk is not considered at all. Government credit agencies should assess costs, mission, and risk......

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WHITE PAPER: Current Expected Credit Loss (CECL)

Lessons from the Federal Government Experience with Lifetime Expected Credit Loss Federal loan programs that oversee loan and loan guarantee portfolios — such as the Troubled Asset Relief Program (TARP), the Federal Housing Administration (FHA) Mortgage Insurance Program, Department of Veterans Affairs (VA) Home Loan Program, and the Department of Agriculture (USDA) Rural Development Program......

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Managing the Federal Balance Sheet: The Allure of Loan Sales

The idea of monetizing the federal government’s massive credit portfolio by selling government-held loans into the market surfaces periodically. Loan asset sales have several features attractive to policymakers. They bring in private capital to support areas traditionally served by government programs. They take risk off the government’s balance sheet. They can also appear to offer......

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RInno – An Open Source Solution to Help Install R…

If you are trying to improve your company’s data science infrastructure, you will need people who can code in R, Python, or Java. Doing data science without staff with these skills would be like trying to build a database without staff who know SQL. R, Python and Java perennially top the list of tools Data......

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