
OatFi raises $24m to build credit network for B2B payments
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White Star Capital led the round, with participation from existing investors Portage and QED backing OatFi's effort to tackle one of the main pain points in B2B commerce: payment terms.
In traditional B2B transactions, buyers and suppliers often operate on opposing cash flow incentives. Suppliers seek fast post-delivery payments to recover working capital, while buyers look to delay payments to preserve operating cash and liquidity.
By embedding its underwriting, origination, and funding capabilities directly into B2B payment platforms within their AP, AR, and commercial charge card workflows, OatFi's APIs enable platforms to facilitate B2B transactions with built-in financing at the point where it's needed most.
'B2B payments are not just a money movement challenge—they're a data and workflow challenge,' says Michael Barbosa, CEO, OatFi. 'That's why we've focused on deep API integrations that offer working capital solutions within the platforms that businesses already rely on to pay and get paid.'
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Reuters
10 minutes ago
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Sarepta to pause Elevidys gene therapy shipments in US
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Finextra
26 minutes ago
- Finextra
From advisors to algorithms: The shift in wealth guidance
From advisors to algorithms: The shift in wealth guidance 0 Editorial This content has been selected, created and edited by the Finextra editorial team based upon its relevance and interest to our community. For decades, wealth management was a relationship business. Clients used to meet their financial advisors in offices, discuss life goals, and trust human judgment to guide their financial futures. However, over the last decade, a quiet revolution has taken place: one that's replacing handshakes with algorithms and intuition with machine learning. Today, millions of people receive financial advice not from a person, but from a platform. Robo-advisors, AI-driven planning tools, and hyper-personalised investment apps are reshaping how we think about wealth, and who gets to build it. However, according to McKinsey, by 2034, 'at current advisor productivity levels, the advisor workforce will decline to the point where the industry faces a shortage of roughly 100,000 advisors.' McKinsey data shows that advice revenues have been the main economic driver for the US wealth management industry. Revenues 'generated from fee-based advisory relationships [...] have grown from approximately $150 billion in 2015 to $260 billion in 2024, and growth in the number of human-advised relationships has outpaced population growth by three times in the same period.' Amid the growing demand for advice, declining advisor head count and addressing the shortage with an advisor talent and productivity system, the quiet revolution has advocated for the recruitment of new-to-industry advisors. By improving the advisor career path for entry-level talent, there is also a clear path for new sources of talent by targeting career switchers. Recruitment will not be enough. This is where generative AI comes in. McKinsey's estimate reveals that even a '30-40% average advisor adoption of more wealth-management-specific gen-AI-enabled tools and processes across the value chain and across the full advisor population by 2034 can deliver 6-12% of time savings – and, in turn, increase advisor capacity.' From suits to software Addressing a 100,000-advisor capacity shortage will be no easy feat, especially with the first wave of robo-advisors having emerged in the early 2010s, offering low-cost, automated portfolio management. They were simple, rules-based systems, efficient, but impersonal. Fast forward to 2025, and the landscape has evolved dramatically. Modern AI-driven platforms don't just rebalance portfolios. They analyse behavioural patterns, anticipate life events, and tailor advice to individual goals, risk appetites, and even emotional states. What began as a cost-saving tool has become a sophisticated engine for personalised wealth guidance. As Sébastien Payette, director consulting expert, financial services, CGI, writes, robo-advisors are becoming an 'integral part of investment strategies for both retail and high-net-worth (HNW) and ultra-high-net-worth (UHNW) investors.' He points to three key trends: 'Hybrid advisory models – Traditional financial institutions are incorporating robo-advisors alongside human expertise, offering a blend of automated technology and personalised, face-to-face financial guidance. Hyper-personalisation – Advanced robo-advisors utilise AI-driven insights that integrate financial market data with investors' digital footprints, tailoring investment strategies to unique financial goals, risk appetite, and life stages. Diversified asset offerings – Robo-advisors are expanding beyond equities and fixed-income products to include alternative investments such as derivatives, real estate, private equity, and cryptocurrencies, broadening the scope of automated wealth management.' What AI does better Payette also explains that smarter AI and predictive analytics through robo-advisors 'will leverage even more advanced AI capabilities to predict market trends and mitigate risks, leading to increasingly optimised portfolios and investment strategies.' AI's appeal lies in its speed, scale, and objectivity. It can: Monitor markets and portfolios in real time. Optimise tax strategies across multiple jurisdictions. Detect behavioural biases and nudge users toward better decisions. Offer 24/7 access to insights, no appointment necessary. For younger investors and the mass affluent, these tools offer something traditional advisors often couldn't: accessibility. With lower fees and intuitive interfaces, AI has opened the door to wealth planning for millions previously priced out of the conversation. As the World Economic Forum states, 'large language models (LLMs) are still evolving. They are expanding beyond robo-advisors, progressing from chatbots to assistants to agents, reducing the advice gap for retail investors. 'The essential question is can AI systems deliver the level of emotional intelligence and empathy required by investors to share information and needs in ways they do with their human advisors, i.e., could machines ever replace human advisors?' But what's lost? Despite its advantages, AI lacks something essential: human empathy. Financial decisions are rarely just about numbers. They're about fear, ambition, family, and identity. A human advisor can read between the lines, sense hesitation, and offer reassurance. An algorithm, no matter how advanced, can't replicate that, at least not yet. There's also the question of accountability. When an AI-driven platform makes a poor recommendation, who's responsible? The developer? The firm? The user? As AI takes on more advisory functions, these questions become more urgent — and more complex. Can AI wealth models be credible? Yes, by processing vast datasets with speed and precision. Agentic AI can also integrate collaboration with human experts, which strengthens the credibility of the systems. Can AI wealth models be reliable? Yes, AI is consistent and free from human error, but there are transparency concerns. LLMs can offer guidance and with modules such as retrieval augmented generation (RAG), context is enriched. Can AI wealth models be intimate? Yes, AI can recognise sentiment, but cannot offer the lived experience and cultural nuances that humans can. Can AI wealth models be self-oriented? Yes, AI can operate without personal biases, but the data its trained on or how it's deployed by financial firms may introduce conflicts of interest. Regulatory oversight may be needed to ensure AI acts in the best interests of the clients, and recommendations are not biased. The rise of hybrid models Rather than replacing human advisors, many firms are now blending the best of both worlds. This transformation or phase of the evolution requires a balanced approach that capitalises on AI systems but preserves human touch. This is how trust-based relationships can be built in wealth. These advisors can use AI to handle data-heavy tasks, while humans focus on relationship-building and strategic guidance. It's a model that promises both efficiency and empathy, and it's gaining traction fast. In conversation with Finextra in 2023, Renato Miraglia, head of wealth management and private banking, Italy, at UniCredit, says that human beings will continue to be essential in the relationship with private and wealth clients into the future, but the use of data and technology will change the analytical tools with which client materials are produced. He cites the value of generative AI to create highly personalised and detailed real-time reports and analyses on the peculiarities of each client's position. 'The wealth manager of the future will use new and sophisticated tools to analyse risks and investment opportunities – and this will improve the accuracy of financial planning. The next generation of tools consider key elements in an integrated way, taking a holistic view of factors such as the interaction with real estate or business assets, changes in lifestyle, and elements of risk that can be secured through various forms of insurance,' Miraglia elaborates. The next evolution The future of wealth guidance may not be about choosing between humans and machines, but about redefining the role of each. As generative AI becomes more conversational and emotionally intelligent, it could take on more nuanced advisory roles. Meanwhile, human advisors may evolve into financial coaches, helping clients navigate not just markets, but meaning.

Finextra
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The Modernization Imperative: Why Financial Services Cannot Afford Legacy System Inertia: By Sergiy Fitsak
The financial services sector has reached an inflection point where legacy system modernization has shifted from strategic advantage to business imperative. Institutions operating on decades-old infrastructure face mounting pressure from regulatory evolution, competitive disruption, and customer expectations that legacy architectures simply cannot meet. While modernization carries inherent risks in a heavily regulated environment, the cost of inaction has become demonstrably higher. Financial institutions that delay transformation risk regulatory non-compliance, competitive disadvantage, and operational inefficiencies that compound exponentially over time. The Convergence of Modernization Drivers Regulatory Complexity and Compliance Burden Modern financial regulations demand system agility that legacy platforms cannot deliver. Open banking mandates, real-time payment requirements, and evolving data privacy regulations require architectures built for adaptability, not just stability. Legacy systems with hardcoded logic and manual processes struggle to accommodate rapid regulatory changes while maintaining audit readiness. The compliance burden extends beyond implementation to documentation and reporting. Modern regulations require granular transaction tracking, real-time risk monitoring, and comprehensive audit trails—capabilities that legacy architectures often lack without significant workarounds. Competitive Pressure from Digital-Native Challengers Fintech startups and embedded finance providers built on cloud-native infrastructure consistently outpace traditional institutions in product innovation cycles. These challengers deploy new features weekly rather than quarterly, offer personalized services through advanced analytics, and provide seamless digital experiences that legacy systems cannot match without extensive customization. The threat extends beyond direct competition. Big Tech companies entering financial services with platform-based approaches force traditional institutions to reconsider their technology foundations to remain relevant in evolving ecosystems. Evolving Customer Expectations Digital-first customers expect instant account opening, real-time payment processing, and personalized financial insights. Legacy systems designed for back-office efficiency rather than customer experience struggle to deliver these capabilities without introducing significant latency or operational complexity. The gap between customer expectations and legacy system capabilities continues widening as digital experiences in other sectors set new standards for responsiveness and personalization. Strategic Modernization Approaches for Financial Services Incremental Architecture Evolution Financial institutions typically cannot afford the operational risk of wholesale system replacement. Successful modernization strategies focus on incremental transformation that preserves business continuity while enabling progressive capability enhancement. Core system wrapping with API layers represents the most common initial approach. This strategy enables modern applications and partner integrations to interface with existing systems without requiring core logic changes. The approach provides immediate value while establishing the foundation for more comprehensive modernization efforts. Cloud-First Infrastructure Strategy Hybrid cloud adoption allows financial institutions to balance regulatory requirements with operational efficiency. Non-critical workloads such as analytics, customer portals, and development environments migrate first, while sensitive transaction processing remains in controlled environments until regulatory frameworks evolve. Cloud-native services enable advanced capabilities—real-time fraud detection, predictive analytics, and automated compliance monitoring—that would require significant investment to develop internally on legacy infrastructure. Data Architecture Modernization Modern financial services require real-time data processing for fraud prevention, regulatory reporting, and customer personalization. Legacy ETL processes and batch-oriented data flows cannot support the analytical requirements of contemporary financial products. Event-driven architectures using modern streaming platforms enable real-time transaction monitoring, instant fraud detection, and immediate regulatory reporting while reducing system complexity compared to traditional batch processing approaches. Implementation Considerations and Risk Mitigation Regulatory Compliance in Modernized Environments Modernization efforts must maintain regulatory compliance throughout transformation phases. This requires careful planning around data residency, audit logging, access controls, and disaster recovery capabilities. Modern architectures actually enhance compliance capabilities through improved observability, automated monitoring, and granular access controls. Documentation and change management become critical during modernization to satisfy regulatory requirements for system understanding and control procedures. Operational Risk Management Financial institutions require comprehensive testing strategies that validate not just functionality but also performance under stress conditions. Modern infrastructure enables more sophisticated testing approaches including chaos engineering and automated load testing that improve system resilience. Rollback capabilities and feature flagging ensure that modernization efforts can be reversed quickly if issues emerge, reducing the operational risk of transformation initiatives. Skills Development and Team Alignment Successful modernization requires workforce development alongside technology transformation. Teams accustomed to legacy systems need training in cloud-native development, API design, and modern DevOps practices. Organizations often supplement internal teams with specialized expertise during transition periods to accelerate knowledge transfer and reduce implementation risks. Measurable Business Impact Operational Efficiency Gains Modernized financial institutions typically achieve significant cost reductions through automated processes, reduced manual interventions, and improved system reliability. Cloud-native infrastructure provides variable cost structures that align technology spending with business growth rather than fixed capacity planning. Enhanced Customer Experience Modern architectures enable real-time personalization, instant transaction processing, and seamless omnichannel experiences that drive customer satisfaction and retention. The ability to launch new products rapidly allows institutions to respond quickly to market opportunities. Regulatory Responsiveness Modern systems adapt to regulatory changes more efficiently through configuration rather than code changes. Automated compliance monitoring and reporting reduce manual effort while improving accuracy and auditability. The Path Forward Financial services modernization represents a strategic imperative that cannot be indefinitely deferred. Institutions that approach transformation systematically—balancing innovation with risk management—position themselves for sustainable competitive advantage in an increasingly digital financial ecosystem. The question facing financial leaders is not whether to modernize, but how to execute transformation that enables growth while preserving institutional stability and regulatory compliance.