Cedar Podcasts

Business Process Re-engineering (BPR) is the only approach that can deliver rapid, dramatic, and lasting performance improvements, but success depends on strategic execution. Effective BPR starts with a holistic, customer-backward view of the enterprise, focusing on value rather than isolated departmental fixes. Organizations must target “addressable” costs, eliminate low-impact processes, prioritize high-value changes, and address root causes instead of symptoms. Breaking processes into granular steps, validating assumptions with data, and balancing impact against implementation effort are critical. Change must be sequenced carefully, avoiding excessive simultaneous disruption and over-reliance on technology that automates flawed processes. Success also hinges on strong ownership through participative change management to overcome resistance and sustain improvements. Finally, redesigned processes must be continuously reviewed and adapted to changing business contexts. BPR is not a one-time fix, but an ongoing discipline to remain competitive in a dynamic marketplace.
  

Artificial Intelligence is increasingly central to predictive and behavioral analytics, especially in risk management and compliance within financial institutions. By analyzing large volumes of financial and non-financial data, AI improves risk identification, measurement, and mitigation. It enables early warning and learning systems in lending, governance, cybersecurity, AML, and fraud prevention, often predicting issues before they materialize. AI in risk management relies on techniques such as machine learning, natural language processing, segmentation analytics, evolutionary programming, cognitive RPA, and visual analytics, with ML, NLP, and segmentation most widely used. Applications span credit scoring, portfolio optimization, behavioral modelling, enterprise risk management, and financial crime detection. AI tools enhance customer segmentation, creditworthiness assessment using alternative data, stress testing, fraud detection, alert prioritization, and case investigation through pattern recognition and graph analytics. Overall, AI significantly increases accuracy, speed, and effectiveness of risk and compliance processes while reducing manual effort and operational inefficiencies.

AI will fundamentally reshape financial institutions, their operating models, and market competition. Rather than a single technology, AI represents a set of capabilities predictive analytics, autonomous learning, and digital interaction enabled by ecosystems including cloud, blockchain, and quantum computing. Back offices will evolve into AI-driven “back-office-as-a-service” models, turning cost centers into revenue generators, as seen with platforms like BlackRock’s Aladdin and Ping An’s OneConnect. Customer experience will shift toward “self-driving finance,” with AI providing personalized advice, optimizing decisions, and improving fraud and AML controls through shared data and pattern recognition. Market dynamics will polarize: large incumbents will gain scale advantages and become AI service providers, while agile niche players target unmet needs; mid-sized firms risk being squeezed out due to limited investment capacity. Regulation, data portability, cybersecurity, partnerships, and talent strategies will be decisive in shaping competitive outcomes in an AI-driven financial ecosystem.

Variable pay is a powerful but sensitive tool that can motivate talent and shape organizational culture if designed well, or demotivate employees if handled poorly. Effective variable pay cannot be owned by HR alone; it requires leadership alignment to balance shareholder and employee interests. Four principles are critical: alignment with strategy and values, simplicity and transparency, meaningful payouts, and objective calculation. A sound framework defines a bonus pool based on company performance, typically approved by the Board, and may be supplemented with non-cash experiential rewards that often have greater emotional impact than cash. Deciding programme coverage involves cultural trade-offs between inclusivity and meritocracy, often guided by each grade’s share of the wage bill. Individual payouts should be performance-linked, salary-based multiples that clearly differentiate high performers. Ultimately, well-designed variable pay enhances employee experience, strengthens pay-for-performance culture, and provides a competitive advantage in attracting and retaining talent.

Enterprise architecture is evolving as banks adapt to digital disruption, regulatory pressure and tech-savvy customers, accelerated by Covid-19. Legacy-heavy traditional banks face greater challenges than digital-native ones, as rigid architectures hinder agility and innovation. While earlier enterprise architecture focused on translating strategy into IT, it often became a constraint for rapid digital initiatives. New-age enterprise architecture shifts focus from technology to business value, customers, FinTech ecosystems, digital capabilities and data. It is business-centred, customer-oriented, FinTech-friendly, data-driven and designed to enable end-to-end digital transformation.

Key design principles include evolutionary architecture with no fixed end state, Minimum Viable Architecture (MVA), modular and loosely coupled components, plug-and-play integration, cloud-native microservices and high agility through iterative development. Frameworks such as Open Agile Architecture (O-AA), TOGAF, SAFe, BIAN and DevOps support this shift, enabling enterprises to scale, innovate and respond quickly to change.

The Core Banking System (CBS) life cycle has two phases: implementation and post-implementation, each requiring a distinct Centre of Excellence (CoE) operating model. Implementation is a major transformation, involving customization, process redesign and governance. Key challenges include prioritizing business requirements, ensuring service continuity, planning scalability and deciding on specialist skills. Best practices include CXO-level sponsorship, clear roles, dedicated teams, defined target operating models, agreed prioritization, strong system integration, robust data modelling and alignment with the vendor product roadmap.

Post-implementation, the CoE must manage ongoing enhancements, support and cost efficiency without losing agility. Best practices include capturing lessons learned, retaining 20–40% of the implementation team, managing vendor transitions, structured release planning, global service delivery models and transparent cost allocation. Overall, an effective CBS CoE depends on bank size, complexity and strategy, supported by a structured assessment of IT capabilities and transformation goals.

Artificial intelligence, big data analytics and cognitive computing are reshaping the traditionally slow-to-adapt insurance industry. As customer needs shift toward on-demand, micro-duration products and real-time risk assessment, technology is enabling insurers to move beyond one-size-fits-all models. AI supports personalized, pay-as-you-go insurance through real-time pricing, chatbots and automated underwriting, while exploding volumes of data from IoT devices, wearables and digital ecosystems improve risk accuracy. Big data analytics expands the data surface for better decision-making, though data quality and governance become critical. Cognitive analytics further enhances prediction, product design and personalized premiums. Together, these technologies reduce reliance on intermediaries, enable direct customer engagement and lower costs. The result is a shift toward micro-products, self-service, automated decisions and more cost-effective insurance, marking a fundamental paradigm change in how insurance is designed, priced and delivered.

Machine learning (ML) is transforming financial services by enabling systems to learn from data and make predictions, improving accuracy, efficiency and customer experience. Banks are already using ML in areas such as fraud detection, cybersecurity, smart loan origination, collections, virtual assistants and portfolio management, with benefits including lower costs and reductions of over 35% in bad-debt provisions.

Effective ML deployment depends on three core pillars. First, managing Big Data through robust frameworks that ingest, clean and integrate structured and unstructured data into central repositories. Second, continuous discovery, where ML models are iteratively trained, validated and refined to improve accuracy while minimizing false positives. Third, real-time execution, ensuring ML insights trigger immediate actions through tightly integrated system architectures.

Beyond finance, ML applications extend to cybersecurity, HR screening, legal contract review and regulatory compliance. However, greater sophistication also raises the need for strong data governance and security. As ML advances, machines may increasingly outperform humans in tasks like behavioral biometrics, reshaping decision-making across industries.

Sanjiv Anand, Chairman, Cedar Management Consulting International

The article challenges Gordon Chang’s view that China under Xi Jinping seeks to dismantle the Westphalian system of sovereign states and that Trump’s “America First” stance is a principled defense of sovereignty. It argues China does not aim to export ideology or replace the global order, but pragmatically pursues its interests using “sharp power” to extract value and influence, especially from smaller neighbors. While Beijing applies pressure regionally, this is consistent with historical behavior of great powers, not a novel systemic threat. Trump’s policies may rightly confront China on trade and market access, but they stem from populist instincts and domestic politics rather than strategic vision. The article contends that disengagement weakens U.S. influence and accelerates China’s regional pull. Effective engagement with China requires strong alliances, reciprocity in trade and investment, leadership in multilateral institutions, and renewed confidence in democratic values and soft power.

Petroleum retailing in Asia and the Middle East faces margin pressure from deregulation, low differentiation and rising competition. In India, pricing deregulation and new entrants are expanding station numbers, reducing throughput and fuel margins. In the UAE and Singapore, regulated prices, high crude costs, limited geography and stagnant demand constrain profitability. As fuel becomes a commodity, retailers must shift from a product-led to a customer-centric, service-oriented model. Growth depends on differentiated value propositions, deeper customer insight and higher non-fuel revenues, which offer superior margins. Opportunities include convenience stores, auto care and ancillary services that increase dwell time and wallet share, supported by attractive site design and upgrades. Scientific site selection, rationalization and security are critical to sustain network profitability. Loyalty programs and data-driven segmentation can strengthen customer retention and tailor offerings. Overall, success requires treating petroleum retailing as both a product and a service business, with non-fuel differentiation at its core.