Latest news with #financialinstitutions
Finextra
17 hours ago
- Business
- Finextra
GFT unveils AI coding assistant
Global digital transformation company GFT has announced its work to drive U.S. and Canadian financial institutions' cloud and AI digital transformation projects with Wynxx, its new AI-powered software development product. 0 This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author. As financial institutions modernize their legacy systems, including transitioning to the cloud and deploying new AI use cases across the organization, they are looking for ways to maintain quality while reducing costs. GFT's Wynxx makes this possible, reducing the amount of time cloud and AI capabilities take to launch by up to 95%. The proprietary AI tool has already enabled a global tier-1 investment bank to reduce the time a cloud deployment development task took from over one month to just one day. Financial institutions want to take advantage of AI driven efficiencies – but less than 40% have even taken the first step: going all in on cloud environments. This is largely because for most financial institutions, digital transformation projects, like the move to the cloud, are a complex undertaking that require significant developer resources. As a result, these financial institutions are facing challenges as they look to harness the power of AI – which typically relies on centralized cloud ecosystems – to bring a competitive edge by reducing operating costs while enhancing the customer experience. It's against this backdrop that GFT is bringing Wynxx to the U.S. and Canada, allowing financial institutions to decrease the developer time and resources required for large scale digital transformation projects. The new product allows financial institutions to significantly drive efficiency in key digital transformation projects, including: The move from decentralized, legacy systems to a comprehensive cloud environment. GFT partners with leading hyperscalers – including AWS and Google – to deliver a cloud environment that fits each financial institutions' unique needs. Since cloud deployments take months, or even years to complete in some cases, institutions typically choose to move to the cloud slowly and incrementally. Now, with Wynxx, GFT is able to deliver the same leading cloud transformation services it's grown a reputation for over 20 years faster, so organizations can spend more time innovating new AI solutions that leverage the cloud infrastructure. Staying up to date with the latest advancements in automation. Most financial institutions have harnessed some form of automation in workflows – such as fraud prevention, anti money laundering and more in recent years, but these technologies continue to improve over time. Updating older automation tools, though, presents a challenge, especially if there are gaps in code documentation (which explains how the code works). With Wynxx, GFT can now reduce the time required for documentation by over 90%, while ensuring the utmost quality, to reduce complexity in new updates. Injecting AI into manual, error prone tasks to scale offerings and increase revenue. In recent years, financial offerings like private credit have rapidly increased in demand. But most firms cannot scale their manual credit risk analysis at the rate needed to meet this demand without risking accuracy, and subsequently revenue, in lending decisions. At the same time, building a proprietary AI-credit analysis system, with all of the firm's internal risk controls and parameters, is often counted out because of the developer hours required. Now, Wynxx enables GFT to build custom software solutions that interact with the financial firm's existing systems, while taking into account its internal risk preferences and controls – quickly. 'Over the past 35+ years, GFT has built and maintained a reputation for delivering the effective, custom configured technology financial services need to build their business, offer an enhanced customer experience and streamline operations,' said Christopher Ortiz, Regional CEO North America, UK and APAC at GFT. 'Now, with Wynxx, we are able to provide the same quality of service our clients have come to expect from us, in a shorter span of time. We look forward to working alongside our clients to solve their most pressing business challenges, now on a timeline that wasn't previously possible, to foster new avenues for innovation.' The launch of Wynxx is the latest step in GFT's plan to become a fully AI-centric company by 2025, where AI will be central to not only the company's internal operations, but also the services it provides clients.
National Post
20 hours ago
- Business
- National Post
New AI-Powered Experian Assistant for Model Risk Management Streamlines and Accelerates Governance Processes
Article content Newest addition to Experian Assistant product family allows financial institutions to document, validate and monitor models with speed, transparency and audit-readiness Article content COSTA MESA, Calif. — Experian today announced the launch of Experian Assistant for Model Risk Management, a first-of-its-kind solution to help financial institutions govern and manage models more efficiently across the entire model‑development lifecycle. Fully integrated into the Experian Ascend Platform™ and powered by ValidMind technology, this solution helps accelerate model validation, improve auditability and transparency, and may aid financial institutions in reducing regulatory and reputational risk. This launch follows last October's introduction of the award-winning Experian Assistant and its AI-enabled model-lifecycle features. Article content Article content 'Manual documentation, siloed validations and limited performance model monitoring can increase risk and slow down model deployment,' said Vijay Mehta, EVP, Global Solutions and Analytics, Experian Software Solutions. 'Adhering to model-risk-management guidelines can be a tremendous strategic advantage for financial institutions when they can create, review and validate documentation quickly and at scale, and this new solution offers these capabilities.' Article content As financial institutions accelerate innovation, they must balance the move toward GenAI-enabled capabilities with compliance to global model-risk-management guidelines such as SR 11-7 (US) and SS1/23 (UK). Experian Assistant for Model Risk Management offers financial institutions with customizable, pre-defined templates, centralized model governance repositories, and transparent internal workflow approvals—empowering them to meet regulatory guidelines with confidence and efficiency. Article content 'Our partnership with Experian represents a major step forward in operationalizing AI for governance and model risk management,' said ValidMind CEO Jonas Jacobi. 'By embedding ValidMind's automation and governance capabilities into the Experian Assistant for Model Risk Management, we're helping financial institutions move faster and satisfy regulator expectations.' Article content 'The combination of Experian's commercial expertise and presence with ValidMind's technology provides the foundation for scalable and explainable AI across the credit and risk lifecycle,' said Sid Dash, Chief Researcher at Chartis. 'This partnership addresses a growing industry imperative – the need to establish proper AI governance that aligns with an evolving technology and regulatory environment and provides a framework for institutions to modernize their model risk practices.' Article content Why It Matters Article content Accelerates time-to-market by streamlining model documentation and approvals, reducing internal approval time by up to 70% and enabling financial institutions to deploy models more quickly. Replacing manual processes with automation speeds up the creation, maintenance and validation of complex documents for data collection and model development. Streamlines a validation team's efforts by quickly accessing and creating consistent reports. Provides the ability to monitor models with reporting insights to ensure confidence in a model's performance and value. This launch further strengthens the award-winning Experian Assistant product family, extending trusted automation and GenAI capabilities from model development into model governance. Article content Why Choose Experian Assistant for Model Risk Management Article content : Article content Model-Risk-Management Excellence – Simplify model documentation efforts via automation, guided workflows and seamless tool integration. Reduced Risk – Enhance consistency to better align with evolving regulatory guidelines and help mitigate the risk of compliance failures and fines. Enhanced Connectivity – Access to Experian analytics experts and Ascend Ops ™ for model registration and deployment, model monitoring, and scenario planning, ensures robust oversight and operational efficiency. Article content To learn more about Experian Assistant for Model Risk Management or schedule a demo, visit: Article content About Experian Article content Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help to redefine lending practices, uncover and prevent fraud, simplify healthcare, deliver digital marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics and software. We also assist millions of people to realize their financial goals and help them to save time and money. Article content We operate across a range of markets, from financial services to healthcare, automotive, agrifinance, insurance, and many more industry segments. Article content We invest in talented people and new advanced technologies to unlock the power of data and innovate. As a FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 25,200 people across 32 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at Article content Article content Article content Article content Contacts
Forbes
21 hours ago
- Business
- Forbes
Why AI Fraud Prevention Must Be Built For The Real World
Chris Brown, President at VASS Intelygenz, drives AI and deep tech innovation and implementation across industries, delivering tangible ROI. Artificial intelligence (AI) is transforming fraud prevention, but only when it moves beyond theory into practice. Financial institutions are under pressure to stop sophisticated fraud in real time, with minimal false positives and without adding friction for customers. While commercial solutions offer a starting point, they often fall short at scale or fail to adapt to evolving threats. What businesses really need is fraud prevention powered by AI that is accurate, explainable and deployable at enterprise speed. Fraud is no longer sporadic. It's adaptive, continuous and increasingly aided by AI itself. Financial institutions and digital-native businesses face not just more fraud, but smarter fraud that requires smarter defenses. Why AI Is Uniquely Suited To Fraud Fraud is dynamic, data-heavy and high-volume, which makes it an ideal match for machine learning (ML). AI systems can learn patterns across millions of transactions, detect anomalies invisible to rule-based systems and flag suspicious behavior in milliseconds. But building a high-performing fraud model is only half the equation. To deliver real-world value, these models must be tightly integrated with existing systems, comply with strict regulations and continuously learn from new data. A Case Study In Real-Time Detection Consider the case of a major U.S. fintech company battling significant debit card fraud despite already using a commercial system. We partnered with them to develop a custom ML-based solution that scored over three million transactions per day, handled peak loads of 100 requests per second and delivered fraud decisions in under 300 milliseconds. The model aligned with business metrics like detection rate and false positive ratio and explained every decision at both the model-wide and transaction level. This initiative wasn't simply about outperforming a previous system. The AI engine we implemented was designed for high throughput. The system used a diverse set of features, combining transactional data with historical, temporal and behavioral insights across multiple entities. Explainability was embedded at both macro (model-wide) and micro (per-transaction) levels to meet compliance and internal audit standards. Over four months of shadow testing, our system improved true positive detection by 93% while reducing false declines by over $110 million. These results weren't just model-driven. They came from tight infrastructure planning, domain-specific feature engineering and a full MLOps lifecycle for continuous improvement. Key Ingredients For Success Real-world fraud prevention demands far more than just a clever algorithm; it requires a strong, integrated foundation. Machine learning models can only be as effective as the data they're trained on. This means building high-quality, well-governed and consistently updated data pipelines that ensure reliable inputs. While AI can handle the bulk of transaction triage, human-in-the-loop systems remain essential for managing complex edge cases and for supplying ongoing feedback that refines model performance. Equally important is explainability and fairness. In regulated environments, organizations must be able to clearly articulate why a decision was made, necessitating systems that are transparent, bias-aware and fully auditable. Compliance must be baked in from the start, with models that align with current regulations, support independent validation and generate traceable records of decisions. Finally, none of this works without infrastructure that can scale. Real-time fraud detection hinges on low-latency systems capable of serving precomputed features instantly, providing an architectural advantage that makes sub-second decision-making not just possible but standard. Governance, Not Guesswork: Compliance From Day One Effective fraud prevention systems require more than just accurate models. They demand trust, transparency and ongoing oversight. From day one, these systems should be built with fairness in mind, excluding sensitive variables, documenting decision logic and maintaining traceable logs for auditability. A human-in-the-loop approach ensures nuanced, edge-case decisioning and continuous model improvement driven by analyst feedback. By embedding governance into the foundation, organizations build solutions that scale with both sophistication and integrity. The Road Ahead: Smarter And More Flexible AI The next frontier combines traditional ML with large language models and graph-based techniques. These hybrid AI stacks will be key in tackling unstructured data from KYC and compliance workflows, while still supporting structured transaction analysis at scale. No matter the tech stack, AI fraud prevention must be built not just for accuracy, but for explainability, speed and integration. To fight fraud effectively, companies need solutions that are not only smart but also production-ready. That means deploying AI with the right mix of data, governance, infrastructure and human insight. Fraud moves fast, and your defenses must move faster. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
21 hours ago
- Business
- Forbes
How AI Agents Are Helping Financial Institutions Become Truly Customer-Centric
Zehra Cataltepe is the CEO of providing explainable growth solutions for marketing teams. She has over 100 AI papers and patents. RIAs, bank and credit union executives are tasked with providing value to their clients and members. For these organizations, whether the client/member (i.e., customer) receives value or not, and how much, determines whether the relationship continues or not. In this article, I'll outline the usage of AI and AI agents to truly be customer-centric by understanding what customers really need based on customer feedback and transactions. At TAZI AI, we use customer transactions and what customers (and competition) say to determine what customers do and say to understand customers' context and needs. We then use AI agents to connect these pieces of AI-generated insights to create the most relevant, hyper-personalized offers for the customers. Instead of a product-centric approach, this customer-centric approach allows the FI (financial institution) to truly understand the evolving customer needs and be there with the product offers the customer needs, as opposed to product offers that the FI wants to push. Providing high value to customers when they need it connects the customer to the FI and helps build long-term relationships. Customer Transactions: Determine What Customers Need As humans, our spending behavior signals a lot about what we need. Where we spend or don't spend our money, how frequently, changes in our spending patterns and more tell a lot. If you are visiting the car repair shop too often, maybe it is time for you to buy a new car. If you are spending a lot of time on home repairs, maybe you should search for some discounts on insurance for installing new monitoring products around your home or getting a new roof. If you have stopped paying college tuition, you may need help with a new auto loan. If you have stopped receiving your salary or rent payments, you need to do something about your future non-discretionary payments. If you are making more money than you usually do, it could be the right time to increase your investment in wealth management products with the best tax advantages. Using AI, it is possible for an FI to determine transaction types based on basic transaction information and also the sequence of transactions a customer has had. In addition, it is also possible to analyze the transactions of all customers and determine what they need and how they'll behave based on external dynamic market conditions. Understand What Customer Feedback Really Means Customers give feedback all the time and use a variety of mediums (app, web, social media) to do so. Your competitors' customers also leave feedback. You can use AI to predict the sentiment and complaints in communications. You can also quite easily determine the topics and subtopics of communications. Carefully selected subtopics allow a true understanding of the needs of the customer and also understanding the strengths and weaknesses of your FI and those of your competition. Utilizing Feedback To Determine The Best Products And Transactions To Offer The Most Relevant AI can predict positive and negative subtopics in your customer feedback. This can allow you to continuously evaluate your products to determine if they are relevant to your customers. It also allows you to remove the irrelevant ones to reduce the noise for your customers and the workload for your teams. AI can help you understand your customers' transactions and what they need at this time in their lives. AI agents can help you get these two pieces of important and dynamic information and create hyper-personalized offers for your customers based on their own current needs and what all the other customers in the market say. Value It's an unbelievable time to be a customer, where you see your FI—instead of pushing products that you don't need, through channels that you don't want to look at—offering you a product that can make a change in how much you save. And as an FI, it's possible to do this—even if you don't have 10 years of data or a bunch of data scientists with Ph.D.s. This is possible thanks to the new advancements in AI and agents. What took an extensive natural language processing project now only takes the right prompt with the right LLM (preferably on-prem, so that your data doesn't train someone else's LLM). What once required a lot of software now takes a lot less with new AI-based software and agent creation tools. This is a great opportunity to support your customers and help them have a better experience and trust in their FI. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Bloomberg
a day ago
- Business
- Bloomberg
Odd Lots: Circle's CEO on the Booming Business of Stablecoins
Stablecoins are emerging as one of the most active areas of cryptocurrencies. The idea of using blockchain rails to transmit money has captured the attention of legacy financial institutions as well as policymakers, as evidenced by the recent passage of the GENIUS Act, which builds out a regulatory framework for that business. But what are the opportunities. And how do stablecoin providers actually make money? On this episode, we speak with Jeremy Allaire, the co-founder and CEO of Circle, which is the company that backs USDC, the second biggest stablecoin on the market. We discuss the company's business model, concerns about financial stability, and the prospects for stablecoins to open up entirely new avenues of payments and commerce.
