Transforming Performance with Future-Ready Business Models: A Banking Perspective
– By Abhijit Pyne, TCS
Explore how banks can drive sustainable operations by transforming business models from a traditional one-sided platform structure to banking-as-a-service on a multi-sided platform.
Banks have been going through difficult times since the global financial crisis of 2008. Scarce economic progress has decelerated their growth prospects, with investors and shareholders witnessing low return on equity (ROE). Banks have been striving to cut costs, and improve efficiencies to address challenges of the current market environment.
What the banks have been unable to adequately complete so far, is a reassessment of their existing business model. This is necessary to help them understand how the business model and associated operating models could be modified to respond to emerging opportunities and threats in the marketplace. Banks need to look into all parts of their business model canvas—revenue and cost structure, partner ecosystem, customer segments, value proposition, and resources channel—and must source from the best providers to usher transformational changes in the business model.
This paper discusses on how banks can drive sustainable operations by transforming business models from a traditional one-sided platform structure to banking-as-a-service on a multi-sided platform.
Bolstering Anti Money Laundering Programs with Intelligent Data Management and Analytics
– By Vishal Sudan & Gopesh Trivedi, TCS
Fighting financial crimes and ensuring anti money laundering (AML) compliance have become major focus areas for the financial industry. Thus, it is paramount to create a fool-proof AML program with the support of smart data management and analytics tools.
With money laundering transactions accounting for 2% to 5% of the global GDP in 2016, fighting financial crimes has emerged as a top priority for the financial industry. While regulators are tightening up legislations to tackle this scenario, banks and financial institutions are struggling to ensure anti money laundering (AML) compliance.
One way around this issue is for banking and financial companies to leverage the rising volumes of transactional and analytical data to devise robust AML programs. This paper highlights the promise data management and analytics holds in the areas of AML and financial crimes compliance. In particular, it touches upon big data technologies, machine learning and data modeling techniques, and artificial intelligence tools.