Imagine seeing your business future with crystal clarity. Today’s finance leaders are making this vision real. They put data-driven insights above all, using smart tools to turn numbers into bold moves. With the right visuals and security, they shape tomorrow before it arrives.

This year, managing cash and foreign exchange has jumped to the front. It’s a sign of the times — businesses want to stay steady when storms hit. They’re getting ready for higher costs and stockpiling, always a step ahead of change.

Efficiency is the new golden rule. Every dollar in working capital must work harder, giving companies the power to move fast and stay flexible.

The best minds are looking to next-gen AI. Nearly seven in ten see its promise: sharper inventory forecasts, smoother collections, and faster cash flow. Simple steps, big leaps.

More than half are exploring fresh ideas — integrated payments that fit right in, blockchain for trust, and treasury hubs close to home. Each idea brings more control over cash and currency.

Half are reworking their supply chains. They’re spreading risk and building factories that bounce back, no matter what.

Sustainability matters too. These leaders know that strong finances and a cleaner world go hand in hand.

The future belongs to those who see it coming — and act on it now.

1. Data-driven financial intelligence remains the #1 priority, with finance leaders emphasizing the importance of leveraging data visualization and security monitoring tools to strengthen decision-making over the next five years.

2. Liquidity and forex management has dramatically risen from 7th place last year to 2nd place this year. This shift reflects companies’ focus on strengthening financial stability amid higher upfront costs and potential inventory stockpiling due to increased market volatility.

3. Working capital efficiency ranks third, as companies seek to optimize financial returns while enhancing financial flexibility.

Key Solutions Being Explored

69% of respondents are considering Gen AI-powered solutions to optimize inventory forecasting, address prolonged receivables collection, and improve cash conversion cycles
Over 50% are exploring innovative solutions including integrated payments, blockchain capabilities, and regional treasury centers for better liquidity and forex risk management
50% continue prioritizing supply chain reconfiguration for risk diversification and manufacturing resilience

Sustainability Focus

Half of the surveyed finance leaders maintain sustainability as a priority, with:

77% favoring digital tools for enhanced ESG reporting
64% seeking partnerships with ESG ecosystem networks
63% preferring sustainable trade finance solutions

The survey, conducted across 800+ finance leaders in 14 markets and seven sectors, reveals how the business landscape’s increasing complexity is driving companies toward more agile, resilient, and technology-enabled financial strategies.

In-Depth Analysis: Finance Leaders’ Evolving Priorities and Long-Term Outlook

Executive Summary

The DBS survey reveals a fundamental paradigm shift in corporate finance strategy, moving from traditional cost optimization toward risk-aware, technology-enabled financial management. The dramatic rise of liquidity and forex management from 7th to 2nd priority signals a new era of financial volatility that demands proactive, sophisticated responses.

Deep Dive: The Three Strategic Pillars

1. Data-Driven Financial Intelligence: The Foundational Priority

Current State: Data-driven decision-making maintains its position as the top priority, but the context has evolved significantly. Finance leaders are no longer seeking basic analytics but sophisticated, real-time intelligence systems that can navigate unprecedented market complexity.

Deeper Implications:

Predictive Capabilities: The emphasis on data visualization and security monitoring tools suggests finance leaders are building systems for predictive, rather than reactive, financial management
Risk Integration: Data systems are becoming the central nervous system for integrated risk management across liquidity, forex, credit, and operational risks
Competitive Advantage: Organizations with superior financial intelligence will increasingly outperform those relying on traditional financial management approaches

Long-term Impact (5-10 years):

Finance functions will evolve into strategic intelligence centers driving business decisions
CFOs will become Chief Intelligence Officers, with finance teams operating more like internal consulting groups
Companies without sophisticated data capabilities will face increasing competitive disadvantage and potential survival challenges

2. Liquidity and Forex Management: The New Strategic Imperative

The Dramatic Shift: The leap from 7th to 2nd place represents one of the most significant priority shifts in corporate finance in recent decades. This isn’t merely a cyclical adjustment but a structural transformation.

Underlying Drivers:

Geopolitical Fragmentation: Trade wars, sanctions, and regional conflicts are creating persistent currency volatility
Supply Chain Restructuring: Diversification strategies require managing multiple currencies and liquidity pools across regions
Interest Rate Uncertainty: Central bank policy divergence globally creates complex hedging requirements
Inventory Strategy Changes: Stockpiling due to supply chain risks ties up significant working capital

Strategic Innovations Emerging:

Regional Treasury Centers: Companies are establishing regional hubs to optimize liquidity management across time zones and regulatory environments
Blockchain Integration: Distributed ledger technology for real-time, transparent cross-border liquidity management
Integrated Payment Systems: Unified platforms that combine treasury management, payments, and forex hedging

Long-term Structural Changes:

Permanent Complexity: The survey timing (before and after US tariff announcements) suggests these aren’t temporary concerns but permanent features of the new business landscape
Competitive Differentiation: Superior liquidity and forex management will become key competitive advantages, particularly for multinational corporations
Regulatory Evolution: Expect increased regulatory focus on liquidity risk management, potentially requiring more sophisticated reporting and compliance

3. Working Capital Efficiency: The Optimization Imperative

Beyond Traditional Management: Working capital optimization is evolving from basic cash cycle management to sophisticated, AI-driven financial engineering.

AI-Powered Transformation: The 69% of respondents exploring Gen AI solutions represents a watershed moment:

Inventory Forecasting: AI will enable dynamic, demand-sensing inventory management that significantly reduces working capital requirements
Receivables Management: Predictive analytics for customer payment behavior will minimize collection periods
Cash Conversion Cycles: Real-time optimization across the entire cash cycle, potentially reducing working capital needs by 20-30%

Broader Strategic Themes

The Technology-Finance Convergence

Current Trajectory: Finance functions are becoming technology organizations. The integration of AI, blockchain, and advanced analytics isn’t supplementary but foundational to competitive finance operations.

Long-term Evolution:

Finance-as-a-Service: Internal finance teams will operate more like fintech companies, providing sophisticated services to business units
Embedded Finance: Financial management will become seamlessly integrated into operational processes
Autonomous Finance: Within 10 years, routine financial decisions will be largely automated, with human oversight focused on strategic and exceptional situations

Supply Chain Finance Integration

Current Reality: 50% prioritizing supply chain reconfiguration indicates finance and operations are becoming inseparable strategic functions.

Future Implications:

Financial Supply Chain Design: Companies will design supply chains as much for financial optimization as operational efficiency
Risk-Adjusted Sourcing: Every sourcing decision will incorporate sophisticated financial risk modeling
Dynamic Capital Allocation: Real-time adjustment of capital allocation based on supply chain performance and risks

Sustainability as Financial Strategy

Beyond Compliance: The focus on ESG reporting and sustainable finance solutions represents sustainability becoming integral to financial strategy rather than a compliance add-on.

Long-term Financial Integration:

Sustainable Finance Premium: Companies with superior ESG performance will access capital at preferential rates
Risk Mitigation: Environmental and social risks will become key components of financial risk management
Value Creation: Sustainability initiatives will be measured and managed as profit centers rather than cost centers

Regional and Sectoral Implications

Asia-Pacific Leadership

Given DBS’s regional focus, the survey likely reflects Asia-Pacific perspectives, suggesting:

Regional Innovation Hub: Asia-Pacific may become the global laboratory for advanced financial management practices
Cross-border Complexity: The region’s diversity in currencies, regulations, and market maturity creates unique challenges driving innovation
Growth Market Dynamics: Rapidly growing markets require more sophisticated financial management than mature economies

Risk Assessment and Mitigation Strategies

Emerging Risks

Technological Risks:

AI Bias and Errors: As AI becomes central to financial decision-making, algorithmic errors could have catastrophic impacts
Cybersecurity Vulnerabilities: Increased digitization creates expanded attack surfaces
Technology Dependency: Over-reliance on technology platforms could create single points of failure

Strategic Risks:

Competitive Intelligence: Advanced financial intelligence systems could become targets for corporate espionage
Regulatory Lag: Technology adoption may outpace regulatory frameworks, creating compliance uncertainties
Talent Gap: The convergence of finance and technology requires new skill sets that may be in short supply

Mitigation Approaches

Diversification Strategies:

Multi-vendor Technology Stacks: Avoid single-vendor dependency for critical financial systems
Hybrid Human-AI Decision Making: Maintain human oversight for strategic and high-risk decisions
Continuous Learning Systems: Implement adaptive systems that improve from market feedback

Five-Year Outlook: The Transformed Finance Function

2025-2030 Transformation Trajectory

Year 1-2 (2025-2026): Foundation Building

Implementation of AI-powered working capital optimization
Establishment of regional treasury centers
Integration of ESG metrics into financial planning

Year 3-4 (2027-2028): Competitive Differentiation

Advanced predictive financial modeling becomes standard
Real-time, integrated risk management across all financial dimensions
Sustainability becomes measurable value driver

Year 5+ (2029-2030): Market Leadership Definition

Autonomous financial decision-making for routine operations
Financial functions drive strategic business decisions
Companies with inferior financial intelligence face potential market exit

Competitive Landscape Evolution

Winners:

Organizations that successfully integrate technology, finance, and operations
Companies with superior data and AI capabilities
Firms that balance efficiency with resilience

Challenged:

Traditional, cost-focused finance organizations
Companies slow to adopt technological solutions
Firms without sophisticated risk management capabilities

Strategic Recommendations

Immediate Actions (Next 12 Months)

Audit Current Capabilities: Comprehensive assessment of data, technology, and risk management capabilities
Talent Strategy: Begin recruiting and training for finance-technology hybrid roles
Technology Infrastructure: Invest in scalable, integrated financial management platforms
Pilot Programs: Launch AI-powered working capital optimization pilots

Medium-term Strategy (2-3 Years)

Regional Expansion: Establish or enhance regional treasury capabilities
Partnership Development: Form strategic alliances with fintech and technology providers
Process Reengineering: Redesign financial processes around AI and automation capabilities
Risk Framework Evolution: Develop integrated risk management across all financial dimensions

Long-term Positioning (5+ Years)

Finance Transformation: Complete evolution to intelligence-driven finance function
Competitive Moat: Establish superior financial management as sustainable competitive advantage
Market Leadership: Become industry benchmark for integrated finance-operations-technology management

Conclusion

The DBS survey reveals finance leaders navigating a fundamental transformation from traditional financial management to sophisticated, technology-enabled strategic intelligence. The dramatic priority shifts reflect not cyclical adjustments but structural changes in the global business environment.

Success in this new paradigm requires viewing finance not as a support function but as a strategic driver of competitive advantage. Organizations that master the integration of data intelligence, risk management, and operational efficiency will define market leadership in the next decade.

The companies that treat these survey findings as tactical adjustments rather than strategic imperatives risk not just underperformance but potential market obsolescence in an increasingly complex, fast-moving business environment.

The Metamorphosis of Maya Chen: A Finance Leader’s Journey Through Transformation

Chapter 1: The Awakening (March 2025)

Maya Chen stared at the DBS survey results on her laptop screen, the Singapore skyline glittering beyond her 42nd-floor office window. As CFO of AsiaLink Manufacturing, a mid-sized industrial conglomerate with operations across Southeast Asia, she felt the familiar knot in her stomach that came with paradigm shifts.

“Liquidity and forex management jumped from seventh to second priority,” she murmured to herself, highlighting the key finding. The timing couldn’t be worse—or perhaps, she realized, it couldn’t be better.

AsiaLink had been coasting on traditional financial management for decades. Monthly closes, quarterly reviews, annual budgets. Their ERP system was a patchwork of legacy modules, their risk management was siloed across departments, and their “data analytics” consisted of Excel pivot tables and PowerBI dashboards that nobody really trusted.

But Maya was different from her predecessor. At 38, she had an engineering background before her MBA, and she’d been quietly observing the transformation happening around them. Companies like their competitor, TechFlow Industries, had somehow managed to reduce their cash conversion cycle by 40% in just two years. Their regional treasury operations were rumored to be so sophisticated that they were actually making money on currency arbitrage while hedging their operational exposure.

“We’re not just behind,” Maya whispered. “We’re becoming obsolete.”

Her assistant knocked. “Ms. Chen? The board wants to discuss the Q1 results tomorrow. They’re particularly interested in why our working capital requirements have increased while revenues stayed flat.”

Maya nodded, but her mind was elsewhere. Tomorrow’s board meeting would be more than a quarterly review. It would be her declaration of transformation.

Chapter 2: Building Adaptive Capabilities (April 2025)

The boardroom fell silent as Maya finished her presentation. Instead of the usual financial review, she had presented a complete reimagining of AsiaLink’s finance function.

“You’re talking about replacing our entire financial infrastructure,” said Chairman Lim, his voice carefully neutral. “The cost would be enormous.”

“The cost of not transforming would be existential,” Maya replied, advancing to her next slide. “Let me show you three scenarios, and what happens to companies like us in each one.”

She walked them through the analysis: the stable multipolar world where optimization became competitive advantage, the fragmented competition where regulatory agility meant survival, and the technology disruption where traditional finance roles would be automated away.

“In every scenario,” she concluded, “our current approach fails. But if we build adaptive capabilities now, we can thrive in any of them.”

Board member Sarah Wong, the former tech executive, leaned forward. “Talk to me about this ‘flexible technology platform’ concept. Are you talking about another massive ERP implementation?”

Maya smiled. “Actually, the opposite. Instead of one monolithic system, we build a modular, API-first architecture. Think of it like financial Legos—we can reconfigure our capabilities as conditions change.”

Over the next hour, she outlined her vision: cloud-native modules for treasury management, AI-powered working capital optimization, real-time risk monitoring, and predictive analytics—all connected through open APIs that could integrate new capabilities as they emerged.

“The beauty is scalability and adaptability,” Maya explained. “We start with core modules and add capabilities as we need them. When new technologies emerge—quantum computing, advanced AI, even financial applications we can’t imagine yet—we can integrate them without rebuilding everything.”

Chairman Lim was scribbling notes. “What about the human element? Our finance team has been with us for years.”

“Which brings me to the second pillar,” Maya said. “Hybrid talent development.”

Chapter 3: Developing Hybrid Talent (May-August 2025)

Maya stood before her finance team of 28 professionals, seeing a mixture of curiosity, anxiety, and skepticism in their faces. She had called this all-hands meeting to launch what she privately called “Project Renaissance.”

“I need to be honest with you,” she began. “The traditional finance roles as we know them are disappearing. But that doesn’t mean finance professionals are disappearing. We’re evolving.”

She had spent the previous month designing a comprehensive talent transformation program. Her inspiration came from an unlikely source—her daughter’s music lessons. Just as musicians learned to blend classical technique with digital composition, finance professionals needed to master both traditional financial acumen and emerging technological capabilities.

“We’re partnering with the National University of Singapore’s FinTech program to create custom certification tracks,” Maya announced. “Every team member will develop dual competencies—your core finance expertise plus a technology specialization.”

The specialization tracks reflected the new reality:

Data Science Finance: Advanced analytics, machine learning, predictive modeling
Technology Integration: API development, system architecture, automation design
Risk Intelligence: Geopolitical analysis, multi-dimensional risk modeling, scenario planning
Strategic Intelligence: Competitive analysis, market prediction, strategic advisory

Tommy Ng, her senior financial analyst and a 15-year veteran, raised his hand. “What if we can’t adapt? What if we’re too old to learn all this new stuff?”

Maya had anticipated this question. “Tommy, you’ve been managing our Southeast Asian currency hedging for five years. You understand market volatility, political risk, and economic cycles better than most economists. The AI tools we’re implementing will amplify your expertise, not replace it.”

She clicked to her next slide, showing a demonstration of their new AI-powered forex analytics platform. “Instead of spending hours pulling data from different sources and building spreadsheets, the AI gives you real-time analysis of currency trends, political risk factors, and optimal hedging strategies. You focus on interpretation, strategy, and decision-making—the high-value work.”

By August, the transformation was visible. Tommy had become the team’s unofficial “Risk Intelligence Champion,” combining his market knowledge with predictive analytics. Linda Tan, their junior accountant, discovered a talent for data visualization and was creating dashboards that made complex financial data accessible to operations teams.

But the real breakthrough came from an unexpected source.

Chapter 4: Strategic Partnerships (September 2025)

Maya was reviewing the Q2 results when her phone buzzed with an encrypted message from David Park, CFO of TechFlow Industries—their main competitor.

“Coffee tomorrow? Something you need to see.”

The next day, sitting in a quiet corner of the Raffles Hotel lobby, David leaned forward conspiratorially. “Maya, I’m about to tell you something that will sound insane, but hear me out. What if our companies collaborated on financial intelligence?”

Maya nearly choked on her latte. “You want to collaborate with our biggest competitor?”

“Not on business strategy,” David clarified. “On financial infrastructure. Look, we’re both facing the same transformation challenges. The technology investments are massive, the talent requirements are specialized, and the learning curve is steep. What if we could share some of the development costs while maintaining competitive advantages?”

He pulled out his tablet, showing her a proposal for the “Southeast Asian Financial Intelligence Consortium.” The concept was elegant: competing companies would jointly fund the development of core financial technologies—data infrastructure, regulatory compliance systems, risk management platforms—while maintaining separate, proprietary applications for competitive strategy.

“Think of it like airports,” David explained. “Competing airlines share the same runway infrastructure but offer different services and routes.”

Maya was intrigued but skeptical. “How do we ensure our competitive information stays protected?”

“Smart contracts and zero-knowledge proofs,” David replied. “We can share pattern recognition and risk intelligence without sharing actual data. For example, our AI systems could collaborate on detecting emerging market risks or regulatory changes without revealing our specific positions or strategies.”

Over the following weeks, Maya discovered that this wasn’t David’s original idea. Similar consortiums were forming globally—the European Financial Intelligence Alliance, the North American Finance Technology Cooperative, even a broader Asia-Pacific partnership including companies from Japan, Australia, and India.

“The individual company approach to financial transformation is becoming obsolete,” explained Dr. Amanda Foster, the MIT finance professor who was consulting on the consortium design. “The complexity and capital requirements are too high. But more importantly, financial intelligence is becoming a collective good—the more companies contribute to pattern recognition and risk identification, the better the systems become for everyone.”

Maya presented the consortium opportunity to her board in October, framing it as the third pillar of their adaptive strategy.

Chapter 5: Scenario Planning Processes (October 2025 – January 2026)

The consortium’s first joint project was ambitious: developing a shared scenario planning platform that could help member companies navigate the uncertain global environment.

Maya found herself in Singapore’s Marina Bay financial district every Tuesday morning, joining CFOs from twelve companies in what they called “Future Finance Forums.” Each session focused on a different potential scenario and its implications for financial management.

The October session examined the “Stable Multipolar World” scenario. Maya’s team had prepared analysis showing how AsiaLink could optimize their treasury operations across six countries, potentially reducing financing costs by 30% through better liquidity management and currency hedging.

But it was the November session on “Fragmented Competition” that proved most valuable. The geopolitical tensions between the US and China were affecting everyone’s supply chains, but the consortium’s collective intelligence revealed patterns none of them could see individually.

“Look at this correlation,” said Jennifer Liu from Pacific Electronics, highlighting a chart on the shared screen. “When US tariff announcements spike social media sentiment in certain regions, our suppliers’ credit ratings change within 48 hours. But the traditional credit agencies don’t pick this up for weeks.”

Maya realized they were witnessing the birth of something unprecedented: collective financial intelligence. The consortium’s AI systems were analyzing thousands of data sources—news feeds, social media sentiment, satellite imagery of manufacturing facilities, shipping traffic patterns, regulatory filings—and identifying market patterns faster than any individual company could.

“We’re not just sharing costs,” Maya reflected during the December session. “We’re creating superhuman financial intelligence.”

The January session focused on implementation. Each company would maintain their own strategic AI systems, but they would share a common “Early Warning Network” that could identify emerging risks and opportunities in real-time.

AsiaLink’s role in the network leveraged their manufacturing footprint across Southeast Asia. Their facilities became sensor nodes, providing real-time economic intelligence from six countries. In return, they gained access to intelligence from consortium members’ operations across thirty countries.

Chapter 6: The Test (February 2026)

The crisis came without warning, as crises do.

Maya was reviewing the month-end results when alerts started cascading across her dashboard. A major shipping container accident had blocked the Suez Canal, a cyber attack had disrupted European banking systems, and political tensions in South Asia were spiking commodity prices.

Two years earlier, AsiaLink would have learned about these events from news reports, days after they began affecting their operations. Financial impact assessments would have taken weeks. Response strategies would have been reactive and incomplete.

But Maya’s transformed finance function operated differently now.

Within two hours of the first event, their AI systems had identified potential impacts across their supply chain, calculated currency exposure changes, and recommended hedging adjustments. Their scenario planning systems immediately activated “Crisis Mode,” pulling up pre-developed response strategies for multiple disruption scenarios.

The consortium’s Early Warning Network proved invaluable. While AsiaLink’s systems identified impacts to their direct operations, the network’s collective intelligence revealed second and third-order effects that no individual company could have anticipated.

“Maya,” her Risk Intelligence specialist Tommy called out, “the network is showing unusual credit stress patterns in three of our Tier 2 suppliers. Not from the current crisis, but from cascading effects we wouldn’t normally see for another two weeks.”

This early warning allowed AsiaLink to secure alternative suppliers and adjust their working capital forecasts before their competitors even realized there was a problem.

But the real test came when Maya’s board held an emergency session to review the company’s crisis response.

“In the past,” Chairman Lim observed, “events like this would have meant weeks of uncertainty, reactive adjustments, and significant financial impact. This time, you had updated forecasts and response strategies within hours. How?”

Maya smiled, thinking back to her transformation journey. “We built capabilities that adapt to changing conditions, developed people who understand both finance and technology, created processes that anticipate rather than react, and established partnerships that multiply our intelligence. We didn’t just upgrade our systems—we reimagined what finance could be.”

Chapter 7: The New Normal (December 2026

Eighteen months after beginning her transformation journey, Maya surveyed the changed landscape of her finance function. The numbers told the story: working capital requirements down 35%, forecasting accuracy up 80%, crisis response time reduced from weeks to hours.

But the real transformation was deeper than metrics.

Her finance team no longer spent their days on routine processing and reporting. Those tasks had been automated. Instead, they focused on strategic intelligence, competitive analysis, and forward-looking decision support. Tommy had become the company’s chief risk intelligence officer, his deep market knowledge amplified by AI systems that could process thousands of risk variables in real-time.

Linda, the former junior accountant, now led their “Financial Innovation Lab,” constantly testing new AI applications and integration possibilities. She had recently pilot-tested a quantum computing application for complex optimization problems, working directly with researchers at A*STAR.

The technology platform Maya had envisioned was now reality. AsiaLink’s financial systems could integrate new capabilities within days rather than years. When Central Bank Digital Currencies launched in three Southeast Asian countries, their treasury systems were ready within a week.

The consortium had grown to include 47 companies across 15 countries, creating a financial intelligence network that was becoming the nervous system of the global economy. Governments and central banks were beginning to use consortium data for policy decisions.

“You know what’s interesting,” Maya reflected during her year-end board presentation, “we started this transformation to catch up with our competitors. Now we’re helping define what the next generation of finance looks like.”

Chairman Lim nodded approvingly. “What’s next?”

Maya smiled, thinking of the quantum computing trials, the AI ethics frameworks they were developing, and the early discussions about financial applications of augmented reality. “The transformation never ends. That’s the point. We built a finance function that evolves continuously, just like the world around us.”

Epilogue: The Butterfly Effect (January 2027)

Six months later, Maya received an invitation that brought her journey full circle. The DBS Institute was hosting the first “Global Finance Transformation Summit,” and they wanted her to keynote on “From Traditional to Strategic: A Practitioner’s Guide to Finance Evolution.”

As she prepared her presentation, Maya reflected on how the DBS survey that had awakened her to the transformation had been just the beginning. The real insight wasn’t in the specific priorities—liquidity management, data intelligence, working capital optimization—but in recognizing that finance itself was fundamentally changing.

Standing before an audience of 500 finance leaders from around the world, Maya shared her core message: “The transformation we’re experiencing isn’t about upgrading our tools or improving our processes. It’s about reimagining what finance means in a world where change is the only constant.”

She clicked to her final slide, showing a butterfly emerging from its chrysalis—a metaphor she had grown fond of.

“We began as traditional finance functions, focused on control and reporting. We’re becoming strategic intelligence centers, focused on adaptation and value creation. But like the butterfly, this isn’t just growth—it’s metamorphosis. We’re not better versions of what we were. We’re something entirely new.”

In the audience, she spotted dozens of finance leaders frantically taking notes, seeing their own transformation journeys reflected in her story. The awakening was spreading.

The butterfly effect had begun.

Three years later, Maya’s approach to adaptive finance capabilities had been documented as the “Singapore Model” and was being taught in business schools worldwide. But that’s another story.

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Top Priorities for DBS Finance Leaders