Across Southeast Asia and Hong Kong, finance teams are transitioning from ledger-keepers to strategic drivers of business resilience. The imperative for real-time cash visibility clashes with fragmented markets, forcing a move beyond digitisation. Technologies like automation and AI are viewed as essential, with 95% of regional tax and finance leaders prioritising data and AI tools to support innovation and predictive analytics. 

Yet adoption is tempered by concerns over data integrity and the escalating threat of AI-enabled fraud. Singapore lost S$913 million to scams in 2025, recovering only S$140.5 million. 

In the first half of 2026, Hong Kong recorded 20,613 overall deception and fraud cases with total financial losses reaching HK$3.5 billion with investment scams costing victims roughly HK$1.65 billion, nearly half of all monetary damage from fraud during this period.

CFOs are prioritising robust verification controls, with 58% giving equal priority to payment speed and security, yet only 43% rate their ability to deliver both as strong. 

In this episode of PodChats for FutureCFO, Karthik Manimozhi, Global President at Eftsure shares his views on how to drive cash resilience with visibility, velocity, verification.

1.       How can CFOs establish a centralised data architecture and governance framework that ensures data integrity, underpins successful AI deployment, and mitigates the risk of information leakage to third-party vendors? 

2.       In an environment of high economic and environmental volatility, how can CFOs mature their cash flow visibility from a near-real-time snapshot to a predictive, AI-driven forecast that actively models for uncertainty? 

3.       Amidst divergent monetary policies and tariff uncertainty, how can CFOs/finance team leverage AI and automation to strengthen their working capital velocity and accelerate receivables, ensuring liquidity is not trapped or eroded? 

4.       To what extent does current technology infrastructure and partnership with banks enable the "always-on" treasury, essential for managing trapped cash and intra-day liquidity across different time zones? 

5.       As fraud tactics grow more sophisticated, including the rise of deepfakes, how can CFOs move beyond reliance on traditional human verification to establish cryptographic, AI-powered controls that verify identities and payments before they are authorised?

6.       For finance teams operating across multiple jurisdictions in the region, how can they balance the drive for automation with the reality of fragmented local payment rails and complex, evolving regulatory landscapes? 

7.       As the focus of sustainability shifts from branding to bottom-line impact, how should CFOs integrate real-time energy cost data and supply chain carbon exposures into their core treasury and cash flow models? (repeated due to signal problem)

8.       With many firms increasing AI budgets but few reaching advanced capability, what do you recommend as a strategic framework for upskilling finance talent to oversee autonomous AI systems?