Economic Benefits of Score Market Competition for Conforming Mortgages

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This study evaluates the economic consequences of that competitive shift, specifically focusing on the impact to credit score costs that are ultimately borne by mortgage borrowers.

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AI in the Global South at the Global SME Finance Forum in Johannesburg

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I have always found that innovation comes from conversation not isolation. Yesterday’s panel discussion on minimum viable regulation for AI in the Global South at the Global SME Finance Forum in Johannesburg offered such a moment.

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Podcast: The hidden math behind credit risk

Podcast

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The conversation covers why traditional machine learning models are missing critical components for accurate risk assessment and how adverse selection has dramatically impacted loan quality in recent vintages. Then Joe makes a bold prediction that software user interfaces are on the verge of a transformation that will render them unrecognizable from previous versions Curious? All is revealed in this fascinating conversation.

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Stabilizing machine learning models with Age-Period-Cohort inputs for scoring and stress testing

All Articles Machine Learning

Machine learning models have been used extensively for credit scoring, but the architectures employed su er from a significant loss in accuracy out-of-sample and out-of-time. Further, the most common architectures do not e ectively integrate economic scenarios to enable stress testing, cash flow, or yield estimation. The present research demonstrates that providing lifecycle and environment functions from Age-Period-Cohort analysis can significantly improve out-of-sample and out-of-time performance as well as enabling the model’s use in both scoring and stress testing applications. This method is demonstrated for behavior scoring where account delinquency is one of the provided inputs, because behavior scoring has historically presented the most diculties for combining credit scoring and stress testing. Our method works well in both origination and behavior scoring. The results are also compared to multihorizon survival models, which share the same architectural design with Age-Period-Cohort inputs and coecients that vary with forecast horizon, but using a logistic regression estimation of the model. The analysis was performed on 30-year prime conforming US mortgage data. Nonlinear problems involving large amounts of alternate data are best at highlighting the advantages of machine learning. Data from Fannie Mae and Freddie Mac is not such a test case, but it serves the purpose of comparing these methods with and without Age-Period-Cohort inputs. In order to make a fair comparison, all models are given a panel structure where each account is observed monthly to determine default or non-default.

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An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling

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The greatest source of failure in portfolio analytics is not individual models that perform poorly, but rather an inability to integrate models quantitatively across management functions. The separable components of age–period–cohort models provide a framework for integrated credit risk modeling across an organization. Using a panel data structure, credit risk scores can be integrated with an APC framework using either logistic regression or machine learning. Such APC scores for default, payoff, and other key rates fit naturally into forward-looking cash flow estimates. Given an economic scenario, every applicant at the time of origination can be assigned profit and profit volatility estimates so that underwriting can truly be account-level. This process optimizes the most fallible part of underwriting, which is setting cutoff scores and assigning loan pricing and terms. This article provides a summary of applications of APC models across portfolio management roles, with a description of how to create the models to be directly integrated. As a consequence, cash flow calculations are available for each account, and cutoff scores can be set directly from portfolio financial targets.

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The evolution of goals in AI agents

All Articles AI Ethics

Forced evolution has been proposed as a possible path to developing artificial general intelligence. For practical reasons, self-replicating robots are being proposed for missions where direct manufacture could be prohibitive or as a cost-effective means to maintain a stable working population of robots. If self-replication occurs in a harsh (i.e. selective) environment, the forces of evolution may distort the originally programmed objectives. Via millions of simulations of AI agents with nematode-level neural networks, this research explores the consequences of allowing replication in a hostile and competi- tive environment. As the selection pressures are tuned, the evolution of their neural networks and corresponding behav- ioral changes are tracked. As a consequence of these simulations, agents with multi-layer neural networks trained simply to retrieve resources, consume needed resources, and evade obstacles evolve behaviors that look like evasion of hostile overseers, the intended murder of enemies, and cannibalism of other agents. These simulations are intended to directly address safety concerns around creating self-replicating AI agents or robots. As designers, if we allow replication under selection pressure, regardless of initial designs, we risk allowing the emergence of unintended strategies. One solution to preventing evolution could be to enable AI agents with continuous backup– immortality.

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Classical and quantum computing methods for estimating loan-level risk distributions

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Classical and quantum computing methods for estimating loan-level risk distributions

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Journal of the Operational Research Society

All Articles IFRS 9 & CECL

The new accounting standards of CECL for the US and IFRS 9 elsewhere require predictions of lifetime losses for loans. The use of roll rates, state transition and “vintage” models has been proposed and indeed are used by practitioners. The first two methods are relatively more accurate for predictions of up to one year, because they include lagged delinquency as a predictor, whereas “vintage” models are more accurate for predictions for longer peri- ods, but not short periods because they omit delinquency as a predictor variable. In this paper we propose the use of survival models that include lagged delinquency as a covariate and show, using a large sample of 30 year mortgages, that the proposed method is more accurate than any of the other three methods for both short-term and long-term predictions of the probability of delinquency. We experiment extensively to find the appropriate lagging structure for the delinquency term. The results provide a new method to make lifetime loss predictions, as required by CECL and IFRS 9 Stage 2.

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A Theory of Borrowers

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Credit Risk, Adverse Selection, Age-Period-Cohort Models

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Effective Generative AI Model Risk Management

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Effective Generative AI Model Risk Management

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Solving the Long-Range Forecasting Problem in Supervised Learning

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In a world where big data is everywhere, no one has big data relative to the economic cycle. Data volume needs to be thought of along two dimensions.

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Creating Unbiased Machine Learning Models by Design

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Unintended bias against protected groups has become a key obstacle to the widespread adoption of machine learning methods.

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Consumer risk appetite, the credit cycle and the housing bubble

All Articles Stress Testing

In this paper, we explore the role of consumer risk appetite in the initiation of credit cycles and as an early trigger of the US mortgage crisis. We analyze a panel data set of mortgages originated between 2000 and 2009 and follow their performance up to 2014. After controlling for all of the usual observable effects, we show that a strong residual vintage effect remains. This vintage effect correlates well with consumer mortgage demand, as measured by the Federal Reserve Board’s Senior Loan Officer Opinion Survey, and with changes in mortgage pricing at the time the loan was originated. Our findings are consistent with an economic environment in which the incentives of low-risk consumers to obtain a mortgage decrease when the cost of obtaining a loan rises. As a result, mortgage originators generate mortgages from a pool of consumers with changing risk profiles over the credit cycle. The unobservable component of the shift in credit risk, relative to the usual underwriting criteria, may be thought of as macroeconomic adverse selection.

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When Big Data Isn’t Enough: Solving the long-range forecasting problem in supervised learning

All Articles IFRS 9 & CECL

n a world where big data is everywhere, no one has big data relative to the economic cycle. Data volume needs to be thought of along two dimensions. (1) How many accounts / transactions / data fields do we have? (2) How much time history do we have? Few, if any, big data sets include history covering one economic cycle (back to 2005) or two economic cycles (back to 1998). Therefore, unstructured learning algorithms will be unable to distinguish between long-term macroeconomic drivers and point-in-time variations across accounts or transactions. This is the colinearity problem that is well known in consumer lending.

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Instabilities Using Cox Proportional Hazards Models in Credit Risk

All Articles IFRS 9 & CECL

When the underlying system or process that is being observed is based upon observations versus age, vintage (origination time) and calendar time, Cox proportional hazards models can exhibit instabilities because of embedded assumptions.

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Current expected credit loss procyclicality: it depends on the model

All Articles IFRS 9 & CECL

Current expected credit loss procyclicality: it depends on the model

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A survey of machine learning in credit risk

All Articles Machine Learning

Machine learning algorithms have come to dominate several industries. After decades of resistance from examiners and auditors, machine learning is now mov- ing from the research desk to the application stack for credit scoring and a range of other applications in credit risk.

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Macroeconomic Adverse Selection in Machine Learning Models of Credit Risk

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Macroeconomic Adverse Selection in Machine Learning Models of Credit Risk

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Scoring AI‐generated policy recommendations with Risk‐Adjusted Gain in Net Present Happiness

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Scoring AI‐generated policy recommendations with Risk‐Adjusted Gain in Net Present Happiness

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Impacts of Drought on Loan Repayment

All Articles Climate Risk

In order to stress test loan portfolios for the impacts of climate change, historical events need to be analyzed to create templates to stress test for future events. Using the 2012 Midwestern US drought as an example, this work creates a stress-testing template for future droughts. The analysis connects weather and crop yield data to impacts on local macroeconomic conditions by comparing drought-impacted agricultural counties with nearby urban counties. After measuring the net macroeconomic impacts of the drought, this was used as an overlay with existing macroeconomic stress models to stress test a lender in a different part of the US for possible drought impacts. Having a library of such climate events would allow lenders to stress test their portfolios for a wide range of possible impacts.

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How do we monitor AI?

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As we work with our first wave of clients for AI Monitor, www.deepfutureanalytics.ai, I am surprised at some of the use cases that are arising.

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