# banking Published articles for banking. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Fake parcel delivery messages steal your card and bank details DevFeed: [Fake parcel delivery messages steal your card and bank details]() Original publisher: [Read original article]() Author: Mieke Verburgh Published: 2026-09-18T07:44:22Z Content type: article Language: en Sources: [Malwarebytes]() Topics: [Security](), [online privacy]() Tags: [banking](), [card](), [delivery](), [email](), [financial](), [personal](), [phishing](), [scams](), [security](), [threat-intel]() ### AI overview The article describes parcel-delivery phishing campaigns that impersonate postal and courier services. A Belgian bpost campaign uses a small unpaid-customs-fee claim to direct victims to a fake website that collects personal, card, and banking information. ### Source excerpt Parcel delivery phishing messages impersonate familiar couriers and use small fees or promised refunds to steal personal and financial information. ## B2B Cross-Border Payments: FX, Rails, and Tax DevFeed: [B2B Cross-Border Payments: FX, Rails, and Tax]() Original publisher: [Read original article]() Author: Deepak Jangir Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Finance]() Tags: [b2b](), [banking](), [cross-border](), [cross-border-payments](), [fees](), [payments](), [reconciliation](), [saas](), [tax]() ### AI overview This tutorial explains how B2B cross-border payments are affected by foreign-exchange spreads, correspondent-bank fees, settlement paths, compliance screening, payment rails, and jurisdiction-specific tax treatment. It highlights why international receivables can arrive late or short and become difficult to reconcile. ### Source excerpt How B2B cross-border payments actually work: FX spread and settlement timelines, correspondent banking, regional rails, why international transfers fail, and the tax and entity rules that catch sellers out. ## Five AI Questions We're Hearing from Financial Services Leaders DevFeed: [Five AI Questions We're Hearing from Financial Services Leaders]() Original publisher: [Read original article]() Author: Junta Nakai; Erin Butler; Roshni Joshi; Antoine Amend; Jennifer Miller; Andrea DeSosa; Rajaram Suresh; Kim Hatton; Naeem Rehman; Spencer Cook Published: 2026-09-09T18:09:29Z Content type: article Language: en Sources: [Databricks]() Topics: [Artificial Intelligence](), [databricks](), [audit trail](), [tokenization]() Tags: [2026](), [agentic-ai](), [ai](), [audit-trail](), [banking](), [compliance](), [data](), [databricks](), [financial](), [financial-services](), [governance](), [reconciliation](), [regulatory]() ### AI overview The article presents five questions that financial-services leaders are asking about trustworthy AI. It highlights governance, compliance, auditability, governed data, liquidity, reconciliation, tokenized settlement, financial-crime controls, and human oversight, with Databricks describing examples for banking operations. ### Source excerpt Last year at Sibos Frankfurt, the question was whether AI works. This year: can your... ## Agentic treasury runs on searchable, observable context DevFeed: [Agentic treasury runs on searchable, observable context]() Original publisher: [Read original article]() Author: Karen Mcdermott Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Artificial Intelligence](), [observability](), [data](), [Latency](), [dashboards](), [Transactions](), [systems]() Tags: [agentic](), [agentic-ai](), [ai](), [banking](), [dashboards](), [data](), [elasticsearch-logs-metrics](), [erp](), [finance](), [financial-services](), [geospatial-log-analytics-metrics](), [latency](), [observability](), [payment](), [systems](), [transactions]() ### AI overview This article argues that agentic treasury depends on searchable context, observable systems, and trustworthy data rather than AI alone. It explains that treasury teams face decision latency because balances, transactions, forecasts, payment activity, liquidity positions, counterparty information, and market signals are fragmented across enterprise systems, platforms, dashboards, reports, and spreadsheets. ### Source excerpt Agentic treasury requires more than AI. It needs searchable context, observable systems, and trusted data to reduce decision latency and support confident, governed action. ## New currency capabilities for global businesses to cut FX costs DevFeed: [New currency capabilities for global businesses to cut FX costs]() Original publisher: [Read original article]() Author: Bart Heideman Published: 2026-08-17T00:00:00Z Content type: release Language: en Sources: [Stripe Blog]() Topics: [stripe](), [.NET Conf]() Tags: [australia](), [banking](), [cross-border](), [errors](), [external](), [fees](), [global](), [growth](), [payment](), [payments](), [product](), [stripe]() ### AI overview Stripe announces expanded multicurrency settlement and instant currency conversion to help global businesses reduce foreign exchange costs and manage funds in more markets and currencies. ### Source excerpt Two product upgrades make it easy for global businesses to manage FX entirely on Stripe. We're expanding multicurrency settlement to more markets and currencies, and we're introducing the ability to convert currencies instantly--all on Stripe. ## Why digital accessibility is a strategic priority for HSBC DevFeed: [Why digital accessibility is a strategic priority for HSBC]() Original publisher: [Read original article]() Author: Deque Systems Published: 2026-07-28T11:03:51Z Content type: article Language: en Sources: [Deque]() Topics: [digital accessibility](), [Accessibility]() Tags: [accessibility](), [accessibility-best-practices](), [banking](), [business](), [compliance](), [customer](), [customer-trust](), [design](), [digital-accessibility](), [digital-experiences](), [digital-products](), [financial-services](), [strategy](), [web-accessibility]() ### AI overview This article explains why digital accessibility is a strategic priority for HSBC and financial-services organizations. It connects accessible online banking with customer trust, stronger relationships, more inclusive digital experiences, and long-term business value. ### Source excerpt Mali Fernando, Group Head of Digital Experience and Accessibility at HSBC, discusses the role of digital accessibility in creating better banking experiences, strengthening customer relationships, and supporting long-term business success. The post Why digital accessibility is a strategic priority for HSBC appeared first on Deque. ## David Vélez and Robin Vince join the boards of the OpenAI Foundation and OpenAI Group PBC DevFeed: [David Vélez and Robin Vince join the boards of the OpenAI Foundation and OpenAI Group PBC]() Original publisher: [Read original article]() Published: 2026-07-21T00:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [Artificial Intelligence]() Tags: [ai](), [banking](), [brazil](), [company](), [finance](), [leadership](), [openai](), [technology]() ### AI overview OpenAI announces that David Vélez, founder and CEO of Nubank, and Robin Vince, Chairman and CEO of BNY, will join the boards of the OpenAI Foundation and OpenAI Group PBC. The announcement highlights their experience in finance, technology, governance, innovation, and expanding access and opportunity. ### Source excerpt David Vélez and Robin Vince join the boards of the OpenAI Foundation and OpenAI Group PBC, bringing global leadership in finance, technology, and governance. ## MUFG aims to become AI-native with OpenAI DevFeed: [MUFG aims to become AI-native with OpenAI]() Original publisher: [Read original article]() Published: 2026-07-07T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [OpenAI](), [Artificial Intelligence](), [AI Strategy](), [AI Chat](), [Security]() Tags: [ai](), [artificial-intelligence](), [banking](), [chatgpt](), [customer-service](), [efficiency](), [enablement](), [enterprise](), [financial-services](), [innovation](), [japan](), [openai](), [operations](), [partner](), [scale](), [security](), [systems](), [technology](), [workflows]() ### AI overview MUFG is working with OpenAI to become an AI-native financial group. It has begun rolling out ChatGPT Enterprise to approximately 35,000 Mitsubishi UFJ Bank employees, combining secure enterprise use, workflow transformation, employee enablement, and new AI-powered customer experiences. ### Source excerpt MUFG uses ChatGPT Enterprise to build an AI-native organization, improve workflows, and deliver new AI-powered financial services at scale. ## Forward Deployed Engineering Faces Rapid Expansion and Talent Constraints DevFeed: [Forward Deployed Engineering Faces Rapid Expansion and Talent Constraints]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-07-06T14:28:59Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [Artificial Intelligence](), [Microsoft](), [Amazon Web Services]() Tags: [ai](), [aws](), [banking](), [engineering](), [enterprise](), [healthcare](), [microsoft]() ### AI overview The article examines the rapid growth of Forward Deployed Engineering (FDE) organizations as Microsoft and AWS invest heavily in the model. It argues that AI vendors need FDEs to help enterprises apply AI beyond chat, combining technical expertise with knowledge of domains such as healthcare and banking. The author predicts the role may eventually resemble systems integrators or contractors and become less distinct as adoption expands. ### Source excerpt But AI vendors need enterprise adoption at any cost ## How Razorpay Built a Customer Data Platform for Queryable Segments at Scale DevFeed: [How Razorpay Built a Customer Data Platform for Queryable Segments at Scale]() Original publisher: [Read original article]() Author: Varun Meka Published: 2026-06-26T08:06:33Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [data](), [Transactions](), [App](), [systems](), [Apache Spark]() Tags: [app](), [banking](), [card](), [customer](), [data](), [devices](), [engineering](), [payments](), [platform](), [razorpay](), [saas](), [scale](), [shopping](), [transactions]() ### AI overview Razorpay describes building an in-house Customer Data Platform to unify fragmented customer and transaction data into queryable audience segments. The supplied excerpt says the platform serves segments across more than 500 million user profiles in under 30 milliseconds while keeping personally identifiable information isolated in source systems. ### Source excerpt Turning Scattered Data Into Queryable Segments at Scale: How Razorpay Built Its Customer Data Platform A consent-native CDP that serves audience segments across 500M+ user profiles in under 30ms, with PII isolated to the source systems. The Problem We Were Solving A customer opens her favourite online shopping app, adds a few items to her cart, and pays INR 1,200 via UPI, powered invisibly by Razorpay. A week later she returns and pays using a saved Visa card from her laptop. Later that month, she places a larger INR 8,500 order through net banking from work. Three transactions. Three different payment instruments. Three different devices. To the merchant's engineering team, and to Razorpay's data systems, these could look like three completely different people, unless you've done the hard work of figuring out they're all the same customer. Now suppose this is a D2C fashion brand approaching their Diwali sale. The merchant's growth team has a clear plan: "Identify customers who have transacted at least once in the last 30 days, have spent more than INR 5,000 cumulatively this quarter, and haven't enrolled in our loyalty programme. Send them an early-access nudge with a personalized discount 48 hours before the public sale opens." A year ago, answering that question at Razorpay meant filing a cross-team data request, waiting for an analyst to write a custom Spark job, and getting an answer in 2-3 days. By the time the merchant had the segment, the Diwali sale was already live. The early-access window had closed. The campaign got sent to a broader, less-targeted audience, wasting spend on customers who would have bought anyway and leaving cold customers untouched. Now multiply that pain by millions of merchants. Razorpay powers payments and growth for over 12 million merchants. From D2C fashion brands and SaaS startups to subscription platforms, ed-tech companies, and the 2 million+ local merchants accepting QR payments every day. Together, they process billions of transaction ## BBVA puts AI at the core of banking with OpenAI DevFeed: [BBVA puts AI at the core of banking with OpenAI]() Original publisher: [Read original article]() Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [ChatGPT](), [OpenAI](), [AI Strategy](), [software-development](), [code productivity]() Tags: [ai](), [banking](), [chatgpt](), [enterprise](), [financial-services](), [global](), [openai](), [productivity](), [software-development]() ### AI overview BBVA describes a strategic alliance with OpenAI to scale ChatGPT Enterprise and integrate artificial intelligence across banking, including customer experiences, operations, risk analysis, software development, and employee productivity. The article reports 100,000 employees using ChatGPT Enterprise globally, increased monthly active usage, time savings, and efficiency gains in selected workflows. ### Source excerpt Learn how BBVA scaled ChatGPT Enterprise to 100,000 employees and partnered with OpenAI to accelerate AI-powered banking transformation worldwide. ## Plaid vs Stripe: Why They Solve Different Problems (Not Competitors) DevFeed: [Plaid vs Stripe: Why They Solve Different Problems (Not Competitors)]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-06-11T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [stripe](), [App](), [data]() Tags: [ach](), [bank-transfers](), [banking](), [card](), [comparisons](), [developer-tools](), [fintech](), [integration](), [payments](), [stripe]() ### AI overview This guide compares Plaid and Stripe, explaining that Plaid connects bank accounts and provides account data and verification, while Stripe primarily processes card payments and offers related financial products. It identifies ACH transfers as their main area of overlap and explains how the platforms can be used together. ### Source excerpt Plaid vs Stripe compared. Why they are not really competitors, where each one fits, and how they can be used together for ACH, banking, and card payments. ## What is Direct Debit? How It Works for SaaS Subscriptions (2026) DevFeed: [What is Direct Debit? How It Works for SaaS Subscriptions (2026)]() Original publisher: [Read original article]() Author: Aarthi Poonia Published: 2026-06-05T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [API]() Tags: [2026](), [ach](), [banking](), [cost](), [glossary](), [guide](), [payments](), [saas](), [subscriptions]() ### AI overview A guide to direct debit for SaaS subscriptions, covering mandate authorization, payment initiation and settlement, regional bank rails such as ACH, SEPA Direct Debit and BACS, failure handling, and trade-offs compared with card payments. ### Source excerpt Direct debit explained for SaaS founders. How ACH, SEPA Direct Debit, BACS, and Faster Payments work, when to use direct debit over cards, and the trade-offs. ## Your Prototype Is Not Being Honest With Your Users (And Here's How To Fix It) DevFeed: [Your Prototype Is Not Being Honest With Your Users (And Here's How To Fix It)]() Original publisher: [Read original article]() Author: hello@smashingmagazine.com (Eric Joseph L.) Published: 2026-05-25T12:00:00Z Content type: tutorial Language: en Sources: [Articles on Smashing Magazine -- For Web Designers And Developers]() Topics: [Usability](), [User interface design](), [Figma](), [Authentication](), [passwords](), [No-code](), [iOS]() Tags: [article](), [authentication](), [banking](), [design](), [figma](), [how-to](), [ios](), [mobile](), [no-code](), [prototypes](), [tutorial](), [ui](), [usability](), [user-experience](), [user-interfaces](), [validation]() ### AI overview This tutorial explains how to build a realistic mobile banking login prototype in ProtoPie. It covers importing a login design from Figma, creating functional text and masked-password inputs, validating credentials, displaying live errors, and adding a timed Face ID animation without code. ### Source excerpt There's a moment in almost every usability session where a participant pauses at the login screen, types something, and glances up: checking whether they're "doing it right." That pause is a clear sign. They've already clocked that this isn't a real app, and every data point collected after that moment is filtered through that awareness. ## Embedded Finance Companies in 2026: Who Powers the Stack DevFeed: [Embedded Finance Companies in 2026: Who Powers the Stack]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-04T00:00:00Z Content type: comparison Language: en Sources: [Dodo Payments Blog]() Topics: [Finance](), [Software as a service]() Tags: [2026](), [apis](), [banking](), [comparison](), [embedded](), [embedded-finance](), [infrastructure](), [merchant-of-record](), [payments](), [saas](), [subscription](), [tax]() ### AI overview A 2026 comparison of 11 embedded finance companies for SaaS platforms, covering payments, payouts, treasury, lending, and cards. It evaluates providers by strengths, costs, and use cases, and discusses partner diligence using a former category leader's collapse as a cautionary example. ### Source excerpt The embedded finance companies that matter in 2026, ranked by use case. Banking-as-a-service, payments infrastructure, lending APIs, the SaaS-friendly providers, and one high-profile collapse worth learning from. ## Android Theft Protection Updates Add Authentication Controls and Recovery Safeguards DevFeed: [Android Theft Protection Updates Add Authentication Controls and Recovery Safeguards]() Original publisher: [Read original article]() Author: Edward Fernandez (noreply@blogger.com) Published: 2026-01-27T16:59:00Z Content type: release Language: en Sources: [Google Online Security]() Topics: [Android Security](), [LineageOS](), [Security](), [Authentication](), [passwords](), [Google]() Tags: [2025](), [android](), [android-security](), [authentication](), [banking](), [brazil](), [browser](), [feature](), [none](), [password](), [recovery](), [security](), [time](), [updates]() ### AI overview Google announces Android theft protection updates including more control over Failed Authentication Lock, expanded Identity Check coverage, longer lockout times after failed screen-lock attempts, and an optional security challenge for Remote Lock. ### Source excerpt Posted by Nataliya Stanetsky, Fabricio Ferracioli, Elliot Sisteron, Irene Ang of the Android Security Team Phone theft is more than just losing a device; it's a form of financial fraud that can leave you suddenly vulnerable to personal data and financial theft. That's why we're committed to providing multi-layered defenses that help protect you before, during, and after a theft attempt. Today, we're announcing a powerful set of theft protection feature updates that build on our existing protections, designed to give you greater peace of mind by making your device a much harder target for criminals. Stronger Authentication Safeguards We've expanded our security to protect you against an even wider range of threats. These updates are now available for Android devices running Android 16+. More User Control for Failed Authentications: In Android 15, we launched Failed Authentication Lock, a feature that automatically locks the device's screen after excessive failed authentication attempts. This feature is now getting a new dedicated enable/disable toggle in settings, giving you more granular control over your device's security. Expanding Identity Check to cover more: Early in 2025, we enabled Identity Check for Android 15+, which requires the user to utilize biometrics when performing certain actions outside of trusted places. Later in the year, we extended this safeguard to cover all features and apps that use the Android Biometric Prompt. This means that critical tools that utilize Biometric Prompt, like third-party banking apps and Google Password Manager, now automatically benefit from the additional security of Identity Check. Stronger Protection Against Screen Lock Guessing: We're making it much harder for a thief to guess your PIN, pattern, or password by increasing the lockout time after failed attempts. To ensure you aren't locked out by mistake (by a curious child, for instance), identical incorrect guesses no longer count toward your retry limit. Enhanced Rec ## BBVA and OpenAI collaborate to transform global banking DevFeed: [BBVA and OpenAI collaborate to transform global banking]() Original publisher: [Read original article]() Published: 2025-12-12T00:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [OpenAI](), [AI Strategy](), [Generative AI](), [AI Platforms/Deployment](), [Security](), [software-development]() Tags: [ai](), [banking](), [chatgpt](), [deployment](), [enterprise](), [financial-services](), [generative-ai](), [global-affairs](), [openai](), [privacy]() ### AI overview BBVA and OpenAI are expanding a multi-year AI transformation program that will roll out ChatGPT Enterprise to all 120,000 BBVA employees across 25 countries. The collaboration will develop AI solutions for customer interactions, risk analysis, internal processes, employee productivity, and a more personalized banking experience, with security, privacy, training, and adoption controls for the regulated financial-services environment. ### Source excerpt BBVA is expanding its work with OpenAI through a multi-year AI transformation program, rolling out ChatGPT Enterprise to all 120,000 employees. Together, the companies will develop AI solutions that enhance customer interactions, streamline operations, and help build an AI-native banking experience. ## Tazapay Review 2026: Pricing, Features and Limits DevFeed: [Tazapay Review 2026: Pricing, Features and Limits]() Original publisher: [Read original article]() Author: Joshua D'Costa Published: 2025-11-12T00:00:00Z Content type: comparison Language: en Sources: [Dodo Payments Blog]() Topics: [Transactions](), [Software as a service]() Tags: [2026](), [alternatives](), [banking](), [comparison](), [cross-border](), [cross-border-payments](), [gateway](), [global](), [merchant-of-record](), [payment-gateway](), [payments](), [pricing](), [review](), [saas]() ### AI overview A review of Tazapay, a Singapore-based cross-border payments and escrow provider. It examines the platform's pricing, collections, virtual accounts, escrow, payouts and foreign-exchange model, and explains why it may be less suitable for SaaS businesses needing a merchant of record. ### Source excerpt What is Tazapay? A Singapore-based cross-border payments and escrow provider. This review covers its pricing structure, features, payout and FX model, and where it falls short for SaaS teams that need a merchant of record. ## Ensuring Compliance and Governance in Kubernetes for Banking Workloads DevFeed: [Ensuring Compliance and Governance in Kubernetes for Banking Workloads]() Original publisher: [Read original article]() Author: Urolime Technologies Published: 2025-11-04T12:50:33Z Content type: article Language: en Sources: [Kubernetes Archives - Urolime Blogs]() Topics: [Kubernetes](), [Security](), [Orchestration](), [Containers](), [Cloud Native Ecosystem](), [Scalability](), [Availability]() Tags: [banking](), [cloud-native](), [compliance](), [containers](), [gdpr](), [governance](), [kubernete](), [kubernetes](), [orchestration](), [pci-dss](), [security](), [soc-2](), [zero-trust]() ### AI overview This article examines how Kubernetes can support compliance and governance for banking workloads. It describes benefits such as scalability, high availability, and fault tolerance, while identifying governance and security challenges involving complex configurations, dynamic workloads, regulatory requirements, and audit visibility. It highlights Kubernetes consulting services and zero-trust network policies as relevant practices. ### Source excerpt In the fast-paced banking era, data security and compliance is not a choice, but a necessity. Here Kubernetes has emerged as an adaptable platform to govern the containerized workloads. Its scalable, fault-tolerant application orchestration feature has proven it to be the ideal choice for the industry. In this blog we will analyze on how Kubernetes [...] ## How banks are cutting development time in half while avoiding million-dollar outages with Temporal Cloud DevFeed: [How banks are cutting development time in half while avoiding million-dollar outages with Temporal Cloud]() Original publisher: [Read original article]() Author: Hannah Short Published: 2025-09-09T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Cloud](), [Development](), [reliability](), [distributed-systems]() Tags: [announcements](), [banking](), [cloud](), [distributed-systems](), [downtime](), [financial-services](), [productivity](), [reconciliation](), [reliability]() ### AI overview A Forrester Total Economic Impact study of Temporal Cloud describes banking organizations replacing homegrown workflow systems, with reported improvements including $14.3 million in preserved revenue over three years and feature delivery in half the time. ### Source excerpt Forrester's TEI study reveals Temporal Cloud delivers $14.3M in preserved revenue and 50% faster feature delivery for banking institutions. ## Feedzai TrustScore: Enabling Network Intelligence to Fight Financial Crime DevFeed: [Feedzai TrustScore: Enabling Network Intelligence to Fight Financial Crime]() Original publisher: [Read original article]() Author: Sofia Guerreiro Published: 2025-07-25T11:59:49Z Content type: article Language: en Sources: [Feedzai]() Topics: [Network](), [Artificial Intelligence](), [AI Models]() Tags: [ai](), [ai-models](), [banking](), [feedzai](), [financial](), [financial-fraud](), [fraud](), [fraud-detection](), [genai](), [machine-learning](), [network](), [network-intelligence](), [report](), [research](), [scams]() ### AI overview This blog post introduces Feedzai TrustScore, part of the Feedzai IQ solution, as an AI-based approach to financial fraud detection. It describes how network intelligence and aggregated knowledge from Feedzai's processed events are intended to help detect threats earlier and adapt to changing fraud patterns. ### Source excerpt By Sofia Guerreiro, Ricardo Ribeiro Pereira, Iker Perez, Jacopo Bono Detecting financial fraud is like finding a moving needle in a shifting haystack. Fraud accounts for a tiny fraction of financial transactions, often less than 0.1%. At the same time, fraudsters are constantly adapting their tactics to evade detection. And this happens within a live and dynamic environment, where financial behaviors and technologies are changing over time. In short, this is an exceptionally difficult problem for financial institutions. With the rise of digital banking and new technologies like GenAI, this problem is becoming even more challenging. In fact, fraud schemes are spreading faster, growing more sophisticated, and becoming increasingly coordinated across geographies. Meanwhile, many financial institutions still rely on detection systems that are either too rigid (e.g., relying on handcrafted rules) or too narrow (e.g., dependent on custom AI models), and neither option can keep up with the pace of change. At Feedzai, we've developed a new approach. In this blog post, we introduce Feedzai TrustScore, part of the Feedzai IQ™ solution. This is a state-of-the-art AI solution that combines the aggregated knowledge from our network of $8.02T yearly processed events to detect threats earlier, adapt continuously, and safeguard financial systems with speed and precision. Traditional Fraud Detection: Catching a Moving Target With a Fixed Net Every year, millions of people fall victim to financial fraud or scams, with a recent report revealing an average loss of over $2,000 per victim. To protect their customers, banks and other financial institutions have made great efforts towards detecting these suspicious transactions, sometimes temporarily blocking them, conducting internal reviews, and contacting the account owner to validate the activity. However, this is an inherently adversarial task, as malicious actors adapt to whatever detection system the banks have in place. These playe ## Как McKinsey предлагает банкам извлекать выгоду из AI DevFeed: [Как McKinsey предлагает банкам извлекать выгоду из AI]() Original publisher: [Read original article]() Author: redmadrobot (red\_mad\_robot) Published: 2025-03-21T16:46:10Z Content type: opinion Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [Artificial Intelligence](), [genai]() Tags: [ai](), [ai-agent](), [bank](), [banking](), [genai](), [red-mad-robot](), [tag-4e7487fdecca](), [tag-8e9a901cca08](), [tag-fa773991fb10]() ### AI overview The article summarizes McKinsey research on using GenAI in banking and presents four principles for building an AI-first bank: defining a value-focused vision, transforming entire business domains and processes, creating an integrated AI stack, and scaling transformation through enabling teams and governance. ### Source excerpt Аналитический центр red_mad_robot перевёл исследование McKinsey о применении GenAI для банковского сектора. Помимо нового уровня автоматизации, AI поможет сделать банки более интеллектуальными, эффективными и финансово устойчивыми. Собрали для вас основные принципы построения AI-First банка, в конце вас ждёт пара полезных артефактов -- ставьте лайки, если материал пригодится. Читать далее ## Reinventing Risk at Revolut DevFeed: [Reinventing Risk at Revolut]() Original publisher: [Read original article]() Author: Konstantin Vasilev Published: 2024-12-10T07:46:23Z Content type: opinion Language: en Sources: [Revolut Engineering]() Topics: [Finance](), [Artificial Intelligence](), [Software]() Tags: [ai](), [banking](), [fake-accounts](), [finance](), [financial](), [fraud](), [knowledge-graph](), [operations](), [outage](), [revolut](), [risk-management](), [services]() ### AI overview Revolut describes how its Risk function approaches financial and operational risk in a financial-services business. The article explains that risks can overlap, such as fraud involving fake accounts leading to unpaid loans, and discusses the limitations of segregating risks into separate departments. ### Source excerpt Using probability, reasoning and AI Revolut is known for its innovation in delivering "super-app" financial services to its customers. The success is evident not only through the rocketing user growth, but also via the ripple effects caused across the banking industry -- traditional finance has to reinvent and evolve, or remain part of history, just like the internet search replaced the paper phonebook. Behind this success stands a complex and impressive machinery, which is still piloted by the same founders, who also engineered the very first components and set the Revolut rocketship in motion. This machine is mostly known for its software excellence, however, with any financial services comes the responsibility of navigating a complex net of regulations and risks. True to the company values, the Risk function at Revolut also innovates, leveraging the decades of banking experience of its leadership and adding the challenger way of thinking with the latest technology. What is Risk? One can find many definitions of risk, ranging from explanations suitable for children, "The possibility of something bad happening", to advanced statistical definitions used in finance: Uncertainty of the return on investment, characterised by standard deviation, asymmetry and tails of the return distribution and the generic ISO 31000 definition: "The effect of uncertainty on objectives" In addition, both graduate textbooks and banking regulations define two general types of risk: Financial: related to financial positions, such as: user deposits, lending, and investments in market instruments. Operational: related to failure of operations, such as: system outage, fraud and natural disasters. Some of you might already sense the inevitable challenges in distinguishing the above definitions and risks. Events seldom happen in isolation and independently: a natural disaster can lead to system outages and market crashes, similarly fraudsters might use fake accounts to withdraw loans which would ## The GANfather: Using Malicious GenAI Agents to Combat Money Laundering DevFeed: [The GANfather: Using Malicious GenAI Agents to Combat Money Laundering]() Original publisher: [Read original article]() Author: Ricardo Ribeiro Pereira Published: 2024-10-04T13:49:42Z Content type: article Language: en Sources: [Feedzai]() Topics: [Generative AI](), [synthetic-data](), [Machine learning](), [AI Development](), [data]() Tags: [ai](), [banking](), [data](), [feedzai](), [financial-sector](), [gans](), [genai](), [generative](), [generative-ai](), [legacy](), [machine-learning](), [money-laundering](), [research](), [synthetic](), [synthetic-data](), [transactions]() ### AI overview Feedzai describes a method that uses Generative AI to create synthetic data simulating realistic money-laundering activity. The generated examples are intended to support machine-learning approaches to detection and help identify vulnerabilities in banks' defenses, addressing the limited availability of labeled data. ### Source excerpt Digital systems have become deeply integrated into many aspects of modern life, particularly within the financial sector. While digital banking simplifies day-to-day operations for clients, it also creates new opportunities for malicious actors to exploit these systems. As a result, money laundering has grown particularly prevalent due to this digital expansion. Banks are required to monitor for money laundering activities and issue alerts when suspicious transactions are detected. Typically, monitoring is performed by rules-based legacy systems. A better approach would be to use Machine Learning models, but these usually require labeled data to train, which are mostly unavailable in this use case. To tackle this problem, we employ advanced Generative AI (GenAI) techniques to generate synthetic data that simulates realistic money laundering activities. These synthetic examples help us identify vulnerabilities and strengthen the defense mechanisms used by banks and other financial institutions In this blog post, we will explore the method developed by Feedzai, which leverages GenAI to tackle the challenges of detecting and preventing money laundering in today's digital landscape. This blog post is the first of a series dedicated to the work done on GenAI by Feedzai Research in the last few years. Problem Statement First, let's briefly introduce the concepts behind money laundering and the difficulties that banks face when trying to prevent it. Money laundering is the process of concealing the origins of illegally obtained funds. Criminals cannot directly spend "dirty" money without risking exposure of their illegal activities. Therefore, they want to disguise the origins of funds before using them. Money laundering typically involves three stages: Placement: the money is introduced into the financial system, often in small amounts spread across various banks. Layering: the money launderer moves the funds through a series of transactions, typically across multiple fin [Next page]()