Spike in bad loans raises scrutiny of P2P credit models
Jump in delinquencies at some lenders prompts questions over modelling practices, but firms stand by their approach
Machine learning techniques, big data management, advanced fraud detection measures: online lenders certainly talk a good game on why they’re able to make better decisions than traditional lenders about who to loan money to. But a recent spike in delinquencies and charge-offs for online consumer loans in the US is raising questions about the effectiveness of these techniques for credit risk modelling.
Certainly, the past 12 months have been humbling for some online lenders. Lending Club and
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact info@risk.net to find out more.
You are currently unable to copy this content. Please contact info@risk.net to find out more.
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Printing this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@risk.net
More on Credit risk
Revolutionising liquidity management: harnessing operational intelligence for real‑time insights and risk mitigation
Pierre Gaudin, head of business development at ActiveViam, explains the importance of fast, in-memory data analysis functions in allowing firms to consistently provide senior decision-makers with actionable insights
Sec-lending haircuts and indemnification pricing
A pricing method for borrowed securities that includes haircut and indemnification is introduced
XVAs and counterparty credit risk for energy markets: addressing the challenges and unravelling complexity
In this webinar, a panel of quantitative researchers and risk practitioners from banks, energy firms and a software vendor discuss practical challenges in the modelling and risk management of XVAs and CCR in the energy markets, and how to overcome them.
Credit risk & modelling – Special report 2021
This Risk special report provides an insight on the challenges facing banks in measuring and mitigating credit risk in the current environment, and the strategies they are deploying to adapt to a more stringent regulatory approach.
The wild world of credit models
The Covid-19 pandemic has induced a kind of schizophrenia in loan-loss models. When the pandemic hit, banks overprovisioned for credit losses on the assumption that the economy would head south. But when government stimulus packages put wads of cash in…
Driving greater value in credit risk and modelling
A forum of industry leaders discusses the challenges facing banks in measuring and mitigating credit risk in the current environment, and strategies to adapt to a more stringent regulatory framework in the future
Accelerating the evolution of credit decisioning and modelling
Anthony Mancuso, director, global head of risk modelling and decisioning at SAS, explains the importance of developing a fully capable credit modelling lifecycle to empower non-specialist personnel, and offers insight into its own solutions to this end,…
NLP and transformer models for credit risk
News feeds are factored into models to predict credit events
