Cedar Podcasts

Large technology transformations like core banking programs often fail when readiness is judged only on system or application status. To enable effective milestone decisions, Cedar recommends the RAPID framework which is a holistic readiness assessment tool based on experience from 40+ core banking implementations. RAPID evaluates five critical dimensions: Resources, Application, Process, Infrastructure and Data.

The framework helps governing bodies assess readiness at board, steering committee and PMO levels through a three-step approach. First, strategic questions define readiness objectives for an upcoming milestone, such as User Acceptance Testing. Second, each RAPID dimension is broken into measurable operational parameters with clear criteria. Third, parameters are assessed using ownership, timelines and RAG (Red-Amber-Green) status to drive accountability.

RAPID ensures structured, transparent decision-making, improves execution discipline and reduces risk. It can be applied at any stage of a transformation, including testing, data migration, business simulation and go-live.

Robo-advisors are becoming a permanent feature of wealth management, driven by millennials and the mass-affluent seeking affordable, transparent investment advice. With global AUM projected at $100 trillion and robo-advisory expected to capture about 5%, the opportunity is significant. Millennials alone are set to inherit nearly $30 trillion over the next decade. Robo-advisors use self-learning algorithms to assess risk and preferences, delivering advice at roughly 30% of traditional fees and catering to smaller ticket sizes.

Banks and wealth managers must decide whether to adopt pure robo models, hybrid offerings, or integrated platforms. Successful examples like Vanguard and Charles Schwab show the power of combining human advice with digital tools. Operationally, success depends on seamless onboarding, compliance-aware integration, automated portfolio management, and superior digital experience. Organizationally, firms must blend traditional advisory expertise with strong technology, analytics and digital content skills. Ultimately, embracing robo-advisory is essential for banks to remain relevant and competitive.

The global financial services sector is undergoing rapid technological and competitive change, putting pressure on margins and increasing regulatory focus on operational resilience. Since 2016, regulators worldwide have emphasized risks linked to cloud adoption, data privacy, cybersecurity, climate risk and growing reliance on third-party providers. While outsourcing can reduce firm-level risk, concentration among service providers may create systemic vulnerabilities.

Traditional point-in-time third-party due diligence is proving ineffective. A shift toward data-driven, continuous risk monitoring is needed, supported by automation and AI to identify, predict and mitigate emerging risks. As operational resilience depends on timely risk detection, automation is no longer optional.

To strengthen resilience, institutions must adopt new operating models, manage change effectively, and diversify technology dependencies through hybrid and multi-cloud strategies. Strong data protection, cybersecurity and privacy technologies are essential. Ultimately, technology can enhance risk strategies only if combined with operating-model innovation, AI adoption and robust governance.

China and India present highly attractive telecommunications investment opportunities, driven by strong global industry growth and rapid wireless adoption. While voice services still account for over 70% of revenues, wireless is overtaking fixed-line due to expanding coverage and falling prices. Asia is the fastest-growing telecom region, led primarily by China’s scale and India’s growth potential.

China’s telecom expansion has been propelled by strong state direction, evolving from a monopoly to a state-controlled oligopoly, with gradual liberalization following WTO accession. Despite massive subscriber growth and high tele-density, foreign participation remains restricted and regulatory independence is still evolving.

India’s growth, by contrast, has been driven by liberalization, deregulation and an independent regulatory framework. Rising FDI limits, strong competition, and rapid subscriber growth make India especially attractive to global investors, despite challenges such as lower ARPUs and higher churn. Overall, China offers unmatched scale, while India offers superior growth and openness to investment.
  

Turbulence in U.S. credit markets is expected to reduce the size and number of private equity (PE) deals in developed economies, as debt for leveraged buyouts becomes scarcer. Despite this, PE continues to face a surplus of capital chasing fewer attractive deals, driven by fewer undervalued companies and limited growth opportunities in mature markets. While Asia and other emerging regions account for a small share of global PE activity, their large economies and higher growth rates present long-term potential. However, expansion into these markets is constrained by limited reliable information, weak regulatory oversight, scarcity of experienced local management talent, and underdeveloped exit mechanisms. Overcoming these barriers requires deeper local knowledge, primary data-driven due diligence, access to strong local management networks, and carefully planned exit strategies that account for regulatory volatility. Going forward, PE activity is expected to shift toward smaller, niche international deals, with firms adapting their due diligence models to better manage emerging-market risks.

AI is transforming unsecured lending, with rapid market growth driven by instant credit scoring, faster underwriting, and improved customer experience. Traditional loan processes that took weeks are being replaced by AI-based risk assessment models that enable same-day or even instant lending decisions. Banks such as Santander and Scotiabank use AI engines like Kabbage to assess creditworthiness quickly, even for new customers, while reducing manual intervention. AI is also improving document processing accuracy, as seen in JP Morgan’s COIN program and Franklin American Mortgage, which dramatically cut review time and errors. Beyond efficiency, AI enables personalized lending, with banks like RBC offering tailored repayment strategies based on customer behavior. However, challenges remain around data privacy, IT compatibility, and workforce disruption as automation increases. Looking ahead, AI will redefine lending through superior customer experience, widespread automation, and the rise of a data- and algorithm-driven economy. Early adopters stand to gain new revenue streams, cost efficiencies, and stronger customer relationships.

Rising regulatory scrutiny has made compliance costs a major challenge for banks, with global fines exceeding €211 billion since the financial crisis and regulations continuing to expand. Banks must balance shareholder expectations with stricter compliance, often while facing competition from less-regulated players. To manage costs proactively, three imperatives stand out. First, compliance should be embedded into day-to-day processes rather than treated as a siloed function, reducing duplication and staffing overheads. Second, banks must effectively manage and leverage data: robust data architecture, granular reporting, and RegTech tools enable faster, more accurate compliance, while data masking protects privacy and reputation. Third, advanced use of data, AI, and analytics can transform compliance into a competitive advantage by automating low-value tasks, improving governance, and freeing resources for meaningful risk mitigation. Banks that adopt a proactive, technology-enabled compliance approach can reduce surprises, strengthen resilience, and ultimately enhance enterprise value.

CIOs and CTOs are prioritizing the ability to turn enterprise data into actionable insights, as fragmented, siloed systems have limited effective decision-making for years. Many organizations suffer from inconsistent data, weak analytics, duplicated MIS efforts, and data integrity risks due to poor data design. Advances in storage and processing have renewed interest in data warehouses (DWHs), but success depends on sound design choices. Key decisions span data models, warehouse platforms, analytics approaches, and implementation partners. Businesses may adopt industry-standard data models, partner-provided models, or custom designs depending on the uniqueness of reporting needs. DWH platforms range from standard databases for lower-volume analytics to appliance-based solutions for real-time and big-data use cases. Analytics can be embedded within the DWH or delivered through stand-alone applications, depending on complexity and change frequency. Selecting a capable implementation partner is critical, as they own data integration, quality, performance, reporting, and change management. A clear DWH strategy is essential to build a reliable foundation for data-driven decisions.

Corporate banks face shrinking margins from global slowdown, trade tensions, and rising risk costs, making digital transaction banking a key growth lever. Digitizing corporate transactions reduces cost per transaction while improving speed, accuracy, and client experience. Cash management, trade finance, and supply chain services already dominate global transaction banking revenues and continue to grow. Banks are responding by investing in modern GTB platforms, choosing between lean portals, persona-based GTB portals, or fully integrated end-to-end platforms depending on scale and strategy. Key trends include persona-based experience design, personalized dashboards, and customer journey optimization to drive digital adoption. Banks are also building smart, segment-specific digital onboarding solutions and modernizing architecture using microservices, APIs, and app-based models to enable omnichannel delivery and new revenue streams. A major shift is toward self-service and self-administration models, where corporates manage users, entitlements, and workflows themselves—cutting operational costs by up to 25–30% while improving efficiency and scalability.

Open Banking, driven by PSD2, is transforming banking from a closed, protected model into an open, platform-based ecosystem where customer data is shared securely with third-party providers via APIs. This shift creates both disruption and opportunity. Banks must move beyond compliance and rethink revenue models, customer segmentation, product strategies, and technology architecture. Revenue growth will depend on API monetization and service adoption. Customers will be served through highly personalised micro-segments, enabled by modular, API-driven offerings. Products and services will increasingly be co-created with fintech partners, while some banks may also act as aggregators or third-party providers themselves. Technologically, banks need API platforms, developer portals, sandboxes, and secure, partner-friendly architectures that balance openness with security. Open Banking is expected to create digital marketplaces that improve agility, innovation, and time to market. Ultimately, success in Open Banking hinges on delivering superior customer experience in a collaborative ecosystem.