Webinars Archives - BCT Digital

3 mins read

This roundtable discussion hosted by Chartis in collaboration with BCT Digital delves into the primary challenges within current credit operations landscape, especially considering the growing shift of credit services to non-banking financial institutions. It examines the pivotal issues emerging in the transformation of the credit lifecycle and evaluates how technology can offer viable solutions to these challenges. Jaya Vaidhyanathan, CEO of BCT Digital, joins industry experts from CubeLogic, SAS Institute and Afghanistan International Bank to discuss the evolving challenges and strategies in credit management.

Key points of discussion

Future transformation in the credit lifecycle
Emerging risks and challenges in credit risk operations
Experts’ insights into the significance of technology, data, and analytics
Impact of climate risk on credit risk
Importance of early warning systems and data-driven exercises
Data challenges in the credit industry

Speakers

Jaya Vaidhyanathan
CEO,
BCT Digital

Karl Sees
Global Head of Product
Strategy and Pre-Sales, CubeLogic

Stas Melnikov
Head of risk portfolio,
Fraud and compliance,
SAS Institute

Feroz Noorani
Chief Risk Officer,
Kuwait International
Bank

Tony Fenianos
Chief Risk Officer,
Afghanistan International Bank

Sidhartha Dash
Chief Researcher,
Chartis

< 1 minute

In a webinar hosted by Techgig, Kasthuri Rangan Bhaskar, Vice-President – Products, BCT Digital, shared his insights on the extensive use of AI/ML models in the financial industry. They are used for customer sourcing, credit scoring, campaign management, fraud risk management, anti-money laundering, risk management among others. ML models have enabled and accelerated tasks that otherwise would have been cumbersome, manually time-consuming or even impossible. For example, in fraud risk management, ML models can identify patterns of suspicious transactions from huge volumes of transactional data, which would not have been evident through casual observation. The use of ML models can also bring about tremendous process and cost efficiencies.

However, there is a flipside to the usage of ML models. These models could suffer from serious flaws such as instability, bias, and explainability, if they are not constructed, used, and governed properly.