“The strong resiliency posture allows two out of three regions to fail without customer impact, but geo-optimized routing also improved our overall customer experience,” Beer said. “Our strong partnership with AWS ensures that the infrastructure stack is frequently and automatically refreshed to improve our risk and resiliency posture and meet our security controls.” The cloud advantage JPMorgan Chase’s cloud journey with AWS began in 2020, and the launch of its consumer bank Chase in the UK was “built from the ground up on AWS,” Beer said. In 2023, the company had nearly 1,000 applications running on AWS, including core services such as deposits and payments. JPMorgan has also made use of AWS Graviton processors as part of the tightknit partnership. The 225-year-old company runs about 50% of all e-commerce transactions in the US, playing a major role during Black Friday and Cyber Monday mega sales this past weekend. “This is why we have been reinventing the way we build global banking and payments infrastructure, and why we’ve pushed the element of the art of the possible in the cloud,” she said. source

When they refer to “bench strength” in sports, they’re talking about the ability of a less skilled player to step in and play a big role if a main performer is unavailable. For years, IT leaders have wanted bench strength. However, those leaders found that achieving bench strength has been an elusive goal in tight job markets.   Is there a way you can develop a bench? Yes, IT can develop bench strength.  The first step is to identify the talent shortfalls in IT, where most CIOs will find the following gaps:  Talent shortages in new technologies such as artificial intelligence (AI), automation, database architecture, information management, cloud management, and edge IT Shortages of talent in the bread-and-butter infrastructure stalwarts, such as network architecture and systems software In the infrastructure category, one cause of declining bench strength is baby boomer retirements. Computer skillsets have systematically been abstracted from newer IT workers, who now work through point and click GUIs (graphical user interfaces) to provision, monitor and manage infrastructure resources. Unfortunately, the more highly abstracted IT tools that newer workers use don’t always get to the bottom of a bug in system infrastructure software. That bug could bog down a hotel reservation system resulting in loss of hundreds of thousands of dollars in bookings per hour. For this, you need “down to the metal” skills, which boomers have excelled at.   Related:How to Build an Effective IT Mentoring Program The net result for IT managers and CIOs is that they find themselves short in new skill areas such as AI, but also in the older IT disciplines that their shops must continue to support, and that younger IT’ers aren’t exposed to.  Setting Your Bench Strength Targets  Since talent is likely to be short in new technology areas and in older tech areas that must still be supported, CIOs should consider a two-pronged approach that develops bench strength talent for new technologies while also ensuring that older infrastructure technologies have talent waiting in the wings.  Here are five talent development strategies that can strengthen your bench:  Partnering with schools that teach the skills you want. Companies that partner with universities and community colleges in their local areas have found a natural synergy with these institutions, which want to ensure that what they teach is relevant to the workplace.   This synergy consists of companies offering input for computer science and IT courses and also providing guest lecturers for classes. Those companies bring “real world” IT problems into student labs and offer internships for course credit that enable students to work in company IT departments with an IT staff mentor.  Related:Jumping the IT Talent Gap: Cyber, Cloud, and Software Devs The internships enable companies to audition student talent and to hire the best candidates. In this way, IT can sidestep a challenging job market and bring new skills in areas like AI and edge computing to the IT bench.   There are even universities that teach “down to the metal” skills at the behest of their corporate partners. The IBM Academic Initiative, which teaches students mainframe software skills, is one example.  Using internal mentors. I once hired a gentleman who was two years away from retirement because he 1) had invaluable infrastructure skills that we needed; and 2) he had expressed a desire to “give back” to younger IT employees he was willing to mentor. He assigned and supervised progressively more difficult “real world” projects to staff.  By the time he left, we had a  “bench” of three or four persons who could step in.  Not every company is this fortunate, but most have experienced personnel who are willing to do some mentoring. This can help build a bench.  Use consultants and learn from them. At times in my CIO career, I hired consultants who possessed specialized technology skills where we lacked experience. When my staff and I evaluated consultants for these assignments, we graded them on three parameters:   Related:Hiring Hi-Tech Talent by Kickin’ It Old School 1) Their depth and relevance of knowledge for the project we wanted done;   2) Their ability to document their work so that someone could take over when their work was complete; and   3) Their ability and willingness to train an IT staff member. Getting the project done was a foremost goal, but so was gaining bench strength.   Give people meaningful project experience. It’s great to send people to seminars and certification programs, but unless they immediately apply what they learned to an IT project, they’ll soon forget it.  Mindful of this, we immediately placed newly trained staff on actual IT projects so they could apply what they learned. Sometimes a more experienced staff member had to mentor them, but it was worth it. Confidence and competence built quickly.   Retain the employees you develop. CIOs lament about employees leaving a company after the company has invested in training them. In fact, the issue became so prominent at one company that the firm created a training “vesting plan” whereby the employee had to reimburse the company for a portion of training expenses if they left the company before a certain prescribed time.   A better way to retain employees is by regularly communicating with them, giving them a sense of belonging that makes them feel part of the team, assigning them to meaningful work, and rewarding them with paths to advancement and salary increases.  Companies (and employees) continuously change, and there is no guarantee that IT departments will always be able to retain their most competent performers. Consequently, it’s critical to develop employees, to actively and continuously engage with them, and to foster an open and pleasant working experience.  By doing so, CIOs can improve staff skill agilities in their organizations and be ready for the next tech breakthrough.  source

Bank executives and their teams face rising customer expectations, evolving needs and behaviors, and new competitive threats — and mobile experiences are at the center of it all. The share of consumers using mobile apps for banking has spiked (overtaking online banking). Competitive pressures, meanwhile, are on the rise as disruptors roll out new capabilities and new value propositions that threaten traditional banks’ relevance and future growth. To help banks respond to these trends, Forrester took a deep dive into a crucial question for banks: How will people’s mobile banking needs, expectations, and behaviors change in the near future? This research provides an overview of key trends, identifies 10 emerging must-have mobile features, and highlights 10 emerging differentiators in mobile banking experiences. We found that: Ten mobile banking offerings are quickly becoming table stakes. Digital leaders can no longer rightly assume that customers don’t want a full range of in-app functionality. Indeed, most US banking customers say that they should be able to accomplish any financial task through a mobile app. Our research identified 10 mobile banking offerings that are becoming must-haves. These include data aggregation, personal data management capabilities, credit builders, autonomous finance services, virtual cards, and subscription management tools (see an example from Capital One below). Another 10 are emerging as brand differentiators. For most banks, the goal is not just to compete but to differentiate. To drive breakthrough growth, digital leaders should explore the products, services, features, and experiences that go beyond what people expect. Our research identified 10 differentiators, including shared finance products, international remittances and multicurrency tools, in-app search, personalized advice, charitable giving tools, and more. Often, the purpose of a given capability is not broad appeal but rather a clear value proposition to a specific audience: BBVA, for example, offers carbon tracking to attract the niche subset of green-focused consumers (see below). Digital banking leaders and their teams will need to ideate and prioritize. Rather than roll out every potential differentiator, digital leaders should identify and prioritize the impact of emerging, must-have offerings by customer and business objectives. Forrester’s Digital Initiatives Prioritization Tool lets digital leaders and their teams rank the relative value of different options. Digital teams should collaborate with product, business, and technology teams to explore new opportunities through experimentation, zero-base builds, and learning. Knowing what’s likely to come next will help digital banking leaders experiment with and adapt to these new features and differentiators. Want To Know More? If you’re a Forrester client and want to go deeper into our new “What’s Next In Mobile Banking, 2024” research, you can read our three reports: What’s Next In Mobile Banking, 2024: An Overview lays out the premise of our research and includes key data insights from that research. What’s Next In Mobile Banking, 2024: 10 Must-Have Capabilities identifies the 10 must-have mobile banking offerings for any bank looking to compete on digital experiences — including examples from firms that already offer these features or products. What’s Next In Mobile Banking, 2024: 10 Emerging Differentiators lays out the 10 emerging differentiators in mobile banking experiences. It also includes examples from banking brands that currently provide these offerings to customers. If you want to discuss the full list of 10 emerging must-haves and 10 emerging differentiators, please reach out to us!   [Image 1: Capital One touts its new in-app subscription management tool for mobile banking customers]   [Image 2: BBVA calculates the customer’s carbon footprint and offers sustainable products]   source

LinkedIn Email Facebook Twitter WhatsApp The post 行業研究報告: 2025 年十大金融科技與支付趨勢 appeared first on VeriMedia. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More AWS announced more updates for Bedrock aimed to spot hallucinations and build smaller models faster as enterprises want more customization and accuracy from models.  AWS announced during re:Invent 2024 Amazon Bedrock Model Distillation and Automated Reasoning Checks on preview for enterprise customers interested in training smaller models and catching hallucinations. Amazon Bedrock Model Distillation will let users use a larger AI model to train a smaller model and offer enterprises access to a model they feel would work best with their workload.  Larger models, such as Llama 3.1 405B, have more knowledge but are slow and unwieldy. A smaller model responds faster but most often has limited knowledge. AWS said Bedrock Model Distillation would make the process of transferring a bigger model’s knowledge to a smaller one without sacrificing response time.  Users can select the heavier-weight model they want and find a small model within the same family, like Llama or Claude, which have a range of model sizes in the same family, and write out sample prompts. Bedrock will generate responses and fine-tune the smaller model and continue to make more sample data to finish distilling the larger model’s knowledge.  Right now, model distillation works with Anthropic, Amazon and Meta models. Bedrock Model Distillation is currently on preview.  Why enterprises are interested in model distillation For enterprises that want a faster response model — such as one that can quickly answer customer questions — there must be a balance between knowing a lot and responding quickly. While they can choose to use a smaller version of a large model, AWS is banking that more enterprises want more customization in the kinds of models — both the larger and smaller ones — that they want to use.  AWS, which does offer a choice of models in Bedrock’s model garden, hopes enterprises will want to choose any model family and train a smaller model for their needs.  Many organizations, mostly model providers, use model distillation to train smaller models. However, AWS said the process usually entails a lot of machine learning expertise and manual fine-tuning. Model providers such as Meta have used model distillation to bring a broader knowledge base to a smaller model. Nvidia leveraged distillation and pruning techniques to make Llama 3.1-Minitron 4B, a small language model it said performs better than similar-sized models. Model distillation is not new for Amazon, which has been working on model distillation methods since 2020.  Catching factual errors faster Hallucinations remain an issue for AI models, even though enterprises have created workarounds like fine-tuning and limiting what models will respond to. However, even the most fine-tuned model that only performs retrieval augmented generation (RAG) tasks with a data set can still make mistakes.  AWS solution is Automated Reasoning checks on Bedrock, which uses mathematical validation to prove that a response is correct.  “Automated Reasoning checks is the first and only generative AI safeguard that helps prevent factual errors due to hallucinations using logically accurate and verifiable reasoning,” AWS said. “By increasing the trust that customers can place in model responses, Automated Reasoning checks opens generative AI up to new use cases where accuracy is paramount.”  Customers can access Automated Reasoning checks from Amazon Bedrock Guardrails, the product that brings responsible AI and fine-tuning to models. Researchers and developers often use automated reasoning to deal with precise answers for complex issues with math.  Users have to upload their data and Bedrock will develop the rules for the model to follow and guide customers to ensure the model is tuned to them. Once it’s checked, Automated Reasoning checks on Bedrock will verify the responses from the model. If it returns something incorrectly, Bedrock will suggest a new answer. AWS CEO Matt Garman said during his keynote that automated checks ensure an enterprise’s data remains its differentiator, with their AI models reflecting that accurately.  source

CRM continues to be a hot investment area for enterprises. Our data shows that close to 70% of organizations plan to increase their CRM investments over the next year. Why? Because CRM is at the heart of all customer operations that directly impact company revenue, and emerging technologies are boosting its power. These technologies provide deeper insights, automate tasks, and enhance personalization, making the front office vastly more effective. But adopting emerging tech is tricky. Often, the appetite for emerging tech outweighs its true, realized benefit. Enterprises also find real impact in small proofs of concept but then struggle to scale investments across organizations. Our report, The Top Emerging Technologies For CRM, 2024, explores the impact of emerging technologies within three benefit horizons: short-, medium-, and longer-term. Three technologies are poised to deliver ROI in the near term. Generative AI (genAI) for language, genAI for visual content, and TuringBots deliver real value right now. GenAI yields material benefits by empowering CRM users to reach new levels of productivity, to deliver a differentiated customer experience (CX), and to unlock new revenue streams. TuringBots speed the development of bespoke CRM applications, which accelerates innovation by allowing companies to iterate on user and customer experiences more quickly. Companies with advanced tech management strategies are already partway through a rollout or deep into pilots of these technologies. Even less technically mature companies are running pilots. You should be exploring or investing in them now. The promise of automating CRM actions dominates medium-term tech hopes. Medium-term (2–5 years out) emerging technologies focus on automating actions within CRM. AI agents automate customer-facing engagement such as sales prospecting or answering simple customer service inquiries. Explainable AI enables the front office to trust and act on “next best” recommendations, steps, and insights. It will be several years, however, before these technologies produce a significant benefit for most companies. Companies with advanced tech management strategies should be deep into piloting these technologies, while less mature firms should approach them with more caution. Two technologies present long-term potential for risk-takers. Web3 and extended reality have niche use cases today: Web3 CRM finds traction in industries such as nonfungible-token marketplaces and decentralized finance. Extended reality, used today for field worker training, onboarding, and field repairs, will have broader appeal for customer service and in select industries such as healthcare. Put these technologies on your watch list. Read our report on these technologies and let me know your thoughts. You can connect with me via inquiry or brief me about your technologies and customer success stories. source

LinkedIn Email Facebook Twitter WhatsApp The post 行業研究報告: 2025 年科技趨勢 (CBINSIGHTS) appeared first on VeriMedia. source

In today’s data-driven world, large enterprises are aware of the immense opportunities that data and analytics present. Yet, the true value of these initiatives is in their potential to revolutionize how data is managed and utilized across the enterprise. However, a significant challenge persists: harmonizing data systems to fully harness the power of AI. According to a recent Salesforce study, 62% of large enterprises are not well-positioned to achieve this harmony, with 80% grappling with data silos and 72% facing the complexities of overly interdependent systems. For chief information officers (CIOs), the lack of a unified, enterprise-wide data source poses a significant barrier to operational efficiency and informed decision-making. To overcome this, many CIOs originally adopted enterprise data platforms (EDPs)—centralized cloud solutions that delivered insights quickly, securely, and reliably across various business units and geographies. Now, EDPs are transforming into what can be termed as modern data distilleries. These distilleries refine raw data while also embedding pre-built and custom components directly into the data lifecycle. This shift streamlines operations, enhances business insights, and unlocks the full potential of data. Why data distilleries are a game-changer: Insights from the insurance industry Traditionally, managing data in sectors like insurance relied on fragmented systems and manual processes. For instance, in claims management, insurers would assess claims based on incomplete, poorly cleaned data, leading to inaccuracies in evaluating claims. Take, for example, a recent case with one of our clients. They had an AI model in place intended to improve fraud detection. However, the model underperformed, and its outputs showed discrepancies compared to manual validations. An analysis uncovered that the root cause was incomplete and inadequately cleaned source data, leading to gaps in crucial information about claimants. This issue resulted in incorrect risk assessments, where high-risk claims were mistakenly approved, and legitimate claims were wrongly flagged as fraudulent. Today, the introduction of data distilleries represents a significant departure from this conventional approach. These distilleries streamline data management by providing a unified, high-quality source of enterprise data. By integrating and refining data through these modern solutions, insurers can enhance the accuracy of risk assessments, reduce claims payout time by over 50%, and boost operational efficiency by more than 30%. To understand the impact of data distilleries, it’s essential to first recognize traditional data management challenges. Historically, insurers struggled with fragmented data sources, leading to inefficient data aggregation and analysis. Integrating advanced technologies like genAI often requires extensively reengineering existing systems. This approach consumed considerable time and resources and delayed deriving actionable insights from data. Today, data distilleries revolutionize this process by providing a centralized platform that streamlines data aggregation, facilitates access to genAI modules, and supports self-service data consumption in the cloud. Instead of overhauling entire systems, insurers can assess their API infrastructure to ensure efficient data flow, identify critical data types, and define clear schemas for structured and unstructured data. Incorporating custom knowledge graphs, enriched with domain expertise, further optimizes data consolidation. This shift allows for enhanced context learning, prompt augmentation, and self-service data insights through conversational business intelligence tools, as well as detailed analysis via charts. By focusing on these key factors, insurers can leverage the benefits of data distilleries, transforming raw data into valuable business intelligence and modernizing their operations. Driving operational efficiency and competitive advantage with data distilleries As organizations increasingly adopt cloud-based data distillery solutions, they unlock significant benefits that enhance operational efficiency and provide a competitive edge. Consolidating data and improving accessibility through tenanted access controls can typically deliver a 25-30% reduction in data storage expenses while driving more informed decisions. For insurers, actuarial teams can cut data preparation time from 8-10 days to just one. Claims processing can be reduced from 35-40 days to about a week. Selecting the right data distillery requires consideration. When evaluating options, prioritize platforms that facilitate data democratization through low-code or no-code architectures. These tools empower users with sector-specific expertise to manage data without extensive programming knowledge. Features such as synthetic data creation can further enhance your data strategy. Effective data governance and quality controls are crucial for ensuring data ownership, reliability, and compliance across the organization. A robust data distillery should integrate governance, modeling, architecture, and warehousing capabilities while providing comprehensive oversight aligning with industry standards and regulations. This approach minimizes risks, maintains customer trust, and ensures the delivery of reliable insights. From an implementation standpoint, choose a cloud-based distillery that integrates with your existing cloud infrastructure. The ideal solution should be scalable and flexible, capable of evolving alongside your organization’s needs. Opt for platforms that can be deployed within a few months, with easily integrated AI and machine learning capabilities. This ensures your organization effectively utilizes data, scales effortlessly, and stays agile and adaptable. Visit EXL’s website for more information on transforming processes with data. Ankur Jain, vice president of data, analytics & AI at EXL, a leading data analytics and digital operations and solutions company. source

4. Pizza algorithm competition  Pizza parties are particularly popular with HR departments in the USA when it comes to supposedly great benefits. To make this more interesting for local techies, developers could combine their passion for algorithms with their love of pizza once a month. The pizza algorithm competition is all about developing the perfect algorithm that determines the ultimate pizza — based on calories, toppings, delivery time and, of course, personal taste. The winner is rewarded with a 30-day pizza flat rate.  5. Official BRB clause  Sometimes you just need a break — and you need it now. With the official “BRB Clause,” developers would have the right to simply say “BRB” (Be Right Back) to critical requests or spontaneous meetings and withdraw for an indefinite period. No manager is allowed to ask where they are or what they are doing. An ideal solution to avoid the next slack bomb.  6. Headhunter defense spray  The job market has been developing in favor of employees for some time now and developers in particular are in high demand. This has the unpleasant side effect that headhunters are regularly on the mat for developers. What if there was a spray that protected developers from the constant onslaught of headhunters?  source