
Model risk chiefs warn on machine learning bias
ML model outputs open to “potential bias sitting in your datasets”, says RBS model risk head

Banks’ rapid adoption of machine learning techniques to augment the modelling of everything from credit card approvals to suspicious transactions has left model managers scrambling to make sure their risk frameworks can accommodate them, senior executives are warning.
Banks hope models that make use of machine learning (ML) – a subset of artificial intelligence that relies on automation to create accurate predictions from large, dense datasets – can dramatically speed up manually intensive
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 Risk management
Can CRE credit risk models cope with hybrid working?
As US office use changes, modellers deploy judgement overlays and alternative data to keep up
Strategies for effective real-time data capture and robust risk management
Risk management systems, processes and real-time data aggregation techniques are rapidly evolving across financial institutions against a backdrop of high market volatility and rapid technological development
Repo clearing could spur CDM adoption – Barclays
UK bank’s tech group believes repo market is primed for new data standards
Banking ALM outlook 2024
Video Q&A with Andrew Aziz, chief strategy officer and head of product, SS&C Algorithmics
Climate risk overlays unnerve model-validation teams
Risk Live: Model risk managers fear they lack the data or skills to properly test expert judgement
Clearing members combing rule books after LME lawsuit win
Industry debates whether other CCPs and exchanges would cancel trades if faced with similar crisis
Interest rate and liquidity risk special report 2023
This special report explores the ongoing impact of higher interest rates on bank capital and liquidity, and the steps they are taking to shore up their liquidity risk management practices in the current environment.
How higher interest rates are affecting bank liquidity
A panel of industry experts discusses the challenges posed to banks’ capital and liquidity by a persistently higher interest rate environment. They also share insights on adapting their liquidity risk management strategies, tools and technologies for a…