Intelligent automation improves credit risk decisions

KPMG helped a large banking group with an IA strategy to automate parts of its global risk analysis process.

A large global banking group
Financial services
Risk sentiment analyzer
  • Client challenge
  • Benefits to client
  • Approach
  • KPMG insights

Client challenge

This firm is a leading bank in Europe with a strong presence in the Americas and Asia-Pacific. The company has nearly 200,000 employees and a presence in more than 70 nations. Its main activities are domestic markets and international financial services as well as corporate and institutional banking. The group strives to help its clients realize their projects through solutions that include financing, investment, savings, and protection insurance. 

Risk analysis is a highly manual, subjective, and costly process. Credit analysts have to read and analyze thousands of news articles and social media posts daily and then make quick credit decisions. With millions of sources for real-time news and social media, this company wanted a better way to keep constant watch for news that might have a negative impact on credit risk. Company leaders wanted to explore how technology could improve risk analysis consistency, lower costs, and improve credit analysts’ work experience across all locations. They also wanted to determine if they should build the capability in-house or buy an existing solution.

Benefits to client

KPMG worked with the client’s innovation team to develop a strategy and roadmap for using intelligent automation to identify meaningful news reporting patterns as one way to improve credit risk decisions globally.  The plan included using cognitive automation to constantly scan news and social media for important events and analyze sentiment for potential credit risk to businesses. The recommended automation strategy is intended to augment human credit analysts’ work so they can spend more time doing strategic credit risk analysis.

This international banking group now has a solid strategy and roadmap that sets the path for automating parts of its global risk analysis process. Additionally, the group has the potential for consistent, 24x7x365, automated external news and social media monitoring to augment credit analysts’ work and improve credit risk decisions. By analyzing news sentiment, the company can have the capability to avoid credit risk situations before they occur.


KPMG helped this client develop a strategy and roadmap for using risk-sensing artificial intelligence to augment credit analysts’ work and improve credit risk decisions globally. The plan included constant and automated news and social media scanning for important events and sentiment analysis to identify potential credit risk to businesses.  The strategy included using machine learning and cognitive automation to classify news as it relates to the credit risk  portfolio. It also outlined how to use text analytics to scan thousands of external news articles and social media. The technology would then ingest and analyze the data, mapping news to risk taxonomies that help analysts identify themes and interpret events.

The recommended automation strategy is intended to augment human credit analysts’ work so they have to spend less time on tedious, manual searching and more effort focusing on strategic credit risk analysis. It includes a hybrid build/buy solution that uses an existing news aggregation service with a customizable risk sentiment analyzer.

KPMG insights

The KPMG team listened to the client’s needs and responded with a strategy and roadmap that enabled the client’s team to easily solve a critical business issue. Then we combined deep risk analysis knowledge with skills and experience in developing and implementing cognitive automation to provide a strategy and roadmap that helps guide the firm on its intelligent automation journey. The KPMG team used extensive data analytics experience in the global financial services industry to assess and recommend the automated credit risk analysis strategy and roadmap. Based on the team’s experience, the recommended strategy included what parts of the credit risk analysis process to automate rather than 100 percent automation to avoid introducing new risks.

This client's automation plan includes constant news and social media scanning for important events and sentiment analysis to identify potential credit risks.