Banking Credit Risk Management has evolved over time, more so after 2008 and a number of Regulations from Basel III to CCAR and IFRS 9 have accentuated the need for a renewed focus. This has also been the time when there have been advancements in technology which has ushered in a new era of Digital revolution and has presented banks with many possibilities to manage their Credit Risk in a more efficient, cost effective and effective manner. Though some of the Digital levers such as Machine learning, Robotics Process automation (RPA) and Natural Language processing (NLP) have been adopted for customer centric processes, we are now seeing an appetite and focus in the Industry to explore its applicability for the Risk Management domain.
In this paper authored by us, we are presenting our view on
- How Credit Risk Management has evolved,
- What are the current focus areas
- What are the Challenges the Industry is facing in Banking Credit Risk Management
- How Digital levers (specifically Machine learning, RPA & NLP) can help
- We present a matrix of Key Credit Risk processes across the Digital Levers which identifies some of the exploratory Use cases we see some of the Banks adopting. This is the heart of the whitepaper which identifies specific credit risk USE cases for Digital adoption. This has been gathered based on our years of experience in the Credit Risk Domain.
Read more here: Credit Risk in the Digital Age_V4
About the Authors
MANOJ REDDY (BFS Risk Practice Head – TATA CONSULTANCY SERVICES, North America)
AJAY KATARA (BFS Risk Practice – Regulatory & Robotics Process Automation Lead – TCS)