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Development of optimised causal AI and its application in retail banking
Journal of Digital Banking,
9
(2),
110-117
(2024)
Abstract
Causal artificial intelligence (AI) has emerged as a promising approach in machine learning (ML), as it considers not only correlations but also cause-and-effect relationships in data, resulting in more human-like decision making. The pivotal stages within causal AI involve causal discovery and inferencing, each playing a crucial role in extracting meaningful insights from the data. In the realm of causal discovery, various algorithms have been developed to uncover the underlying cause-and-effect structures within data sets. A notable limitation, however, surfaces when attempting to apply these algorithms to data sets characterised by binary variables. This constraint prompts a crucial examination of the current methodologies and calls for innovative solutions that can seamlessly navigate the complexities of binary variable data sets. This paper proposes an optimised causal discovery algorithm that is integrated with the causal inference method based on the estimation of conditional average treatment effects (CATE) scores. The results present the potential of causal AI in terms of incremental impact on the predictive capability of AI/ML models. And the incremental impact is elucidated by comparing conventional propensity-based modelling and causal AI-based modelling by means of a use case in the field of retail banking.
Keywords: causal AI; causal discovery; causal inference; CATE
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Author's Biography
Sai Chaitanya Molabanti is an associate data scientist working for Data Chapter team at DBS Tech, India. He is experienced in artificial intelligence and machine learning (AIML) model development and implemented causal AI, explainable AI solutions. Chaitanya has developed a few python reusable assets such as model monitoring engine for AIML model governance framework and synthetic data generation using Generative Adversarial Networks (GANs).
Lakshmi Kiran Kanchi is Senior Vice President at DBS Bank, Singapore, where his focus is on at scale and responsible AI governance framework for the development and deployment of artificial intelligence/ machine learning and GenAI-based applications to support consumer banking group and wealth management business. Furthermore, his scientific contributions in artificial intelligence/machine learning have been well recognised across the globe in the form of international patents, journal and conference publications and government grants.
Venkateshwarlu Sonathi is Senior Vice President at DBS Tech, India. He heads the Data Chapter at DBS Tech, and, in his role, he heads a team of data scientists and data analysts who deliver solutions for both the consumer banking and non-consumer banking businesses.
Rajanikanth Annam is a seasoned professional with over two decades of experience in artificial intelligence, data science and analytics. He currently serves as the Head of Data Science and Analytics for the Consumer Banking and Wealth Management department at DBS Bank. Rajanikanth’s expertise lies primarily in developing innovative data science solutions for digital marketing, customer engagement, algorithmic lending and fraud detection. He has actively contributed to the field through presentations at major international conferences and publications in journals.
