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AI in Insurance: How Insurers Are Using AI to Transform Claims and Risk Management

AI in Insurance: How Insurers Are Using AI to Transform Claims and Risk Management

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AI in Insurance is transforming an industry that has traditionally been highly information-driven. Insurers evaluate risk, calculate premiums, assess claims, understand customer behavior, detect potential fraud, and manage large amounts of documentation before making decisions that can have significant financial consequences. What is changing is not the importance of data, but the speed and sophistication with which insurers can analyze it. Artificial intelligence is increasingly becoming part of this transformation, enabling insurers to process information faster, identify patterns that may be difficult to detect manually, and automate repetitive parts of the insurance lifecycle. The use of AI in insurance is expanding across underwriting, pricing, claims management, fraud detection, customer service and risk assessment. The National Association of Insurance Commissioners notes that insurers are using AI in areas including underwriting, pricing, customer service, claims handling and fraud detection. EIOPA’s 2026 survey also found that nearly two-thirds of surveyed European insurance undertakings were already actively using generative AI, although many implementations remain at the proof-of-concept stage. Claims and risk control are specifically vital because they sit down close to the economic center of coverage. A claims branch have to decide whether or not a claim is valid, estimate its fee, perceive ability fraud, verify documentation and sooner or later authorize fee. At the same time, insurers need to continuously recognize changing risks throughout property, fitness, lifestyles, vehicle, commercial and area of expertise coverage. AI can assist join these strategies by turning huge volumes of based and unstructured statistics into usable insights. However, AI is not simply replacing insurance professionals with automated decision-making. In many practical implementations, the more realistic model is a combination of automated analysis and human oversight. AI can identify patterns, prioritize cases, summarize documents, estimate potential losses or flag unusual behavior, while trained professionals remain responsible for complex decisions and exceptions. This distinction is becoming increasingly important as regulators focus on responsible AI governance. EIOPA’s 2025 Opinion on AI governance and risk management emphasizes risk-based governance, fairness, explainability, data governance, documentation and clearly defined responsibilities for insurers using AI systems. The result is a new segment of insurance generation in which AI is turning into much less approximately experimentation and greater about improving specific business tactics. For insurers, the possibility isn’t always in reality to install the latest model. It is to determine in which AI can produce measurable upgrades with out compromising equity, transparency, safety, compliance or purchaser accept as true with. What Is AI in Insurance? AI in insurance means using computer tools to look at data, spot patterns do work automatically help people make choices and make talking with customers smoother all along the insurance journey. Traditional insurance systems already rely heavily on statistical models and structured data. AI expands those capabilities by allowing insurers to work with much larger and more diverse datasets, including documents, images, text, transaction histories, sensor information and customer interactions. Machine learning models can identify patterns from historical information. Natural language processing can extract useful information from documents and conversations. Computer vision can analyze images associated with automobile, property or other claims. Predictive analytics can estimate the probability of particular outcomes. Generative AI can summarize documents, create internal reports and support employees with information retrieval. These capabilities can be used independently or combined into larger insurance workflows. Insurance Function Traditional Approach AI-Enabled Approach Claims Manual document and claim review Automated document analysis and claim classification Fraud detection Rules and manual investigation Pattern recognition, anomaly detection and predictive scoring Underwriting Manual assessment of available information Predictive models and automated risk analysis Pricing Historical statistical models Advanced predictive and behavioral analytics Customer service Human-led support AI-assisted service and conversational systems Risk management Periodic analysis Continuous monitoring and predictive insights Document processing Manual data entry OCR, NLP and automated extraction Property assessment Physical inspection Image analysis and remote assessment Loss estimation Manual calculations Predictive loss and reserve models The important point is that AI does not represent one technology or one insurance application. It is an umbrella for multiple capabilities that can be integrated into different stages of the insurance lifecycle. Why Are Insurers Increasing Their Use of AI? The insurance industry operates under several pressures simultaneously. Customers increasingly expect faster digital experiences, claims departments need to process large volumes of information efficiently, fraud continues to create financial losses, and insurers must manage increasingly complex risks. At the same time, insurers have access to more data than ever before. Connected vehicles, mobile devices, property sensors, digital interactions, medical information, transaction records and external datasets can potentially provide additional signals about risk. The challenge is turning this information into useful decisions. AI can help insurers process information on a scale that would be very difficult for human teams to manage by hand. EIOPA has highlighted AI uses from pricing and underwriting to claims management and fraud detection. The NAIC also points to AI across underwriting, pricing, claims and other insurance functions. There is also a growing operational incentive. Insurance organizations handle enormous amounts of documentation. A single claim can involve forms, photographs, invoices, emails, medical records, repair estimates, police reports and other supporting materials. AI can help classify and extract information from these materials before a human adjuster reviews the case. This does not necessarily mean eliminating human involvement. Instead, it can reduce the amount of time employees spend searching for information and performing repetitive administrative tasks. How AI Is Transforming Insurance Claims Claims management is one of the most visible areas where AI can change the insurance experience. The traditional claims process can be slow because information often arrives through multiple channels and must be manually reviewed.An adjuster has to read documents look at pictures check policy details guess how damage there is, talk to the customer and get in touch, with repair shops. It’s a lot of work. It takes time. AI can connect many of these steps. A customer might submit a claim digitally, upload photographs and provide supporting documents. AI systems can classify the claim, extract relevant information, identify missing

How Technology Is Changing Liquidity Management

Market Liquidity: Why It Matters for Traders and Financial Markets

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Market liquidity is one of the maximum essential however often misunderstood standards in financial markets. Traders might also attention on expenses, charts, buying and selling techniques and market direction, however the capability to sincerely buy or sell an asset effectively depends closely on liquidity. A marketplace can show an appealing rate whilst still being hard or pricey to trade if there are not enough shoppers and dealers available. At its best, market liquidity refers to how without difficulty an asset may be offered or bought with out causing a massive trade in its price. A particularly liquid marketplace generally has many energetic participants, common transactions, robust trading quantity and relatively narrow variations among buying and selling prices. A much less liquid market may have fewer individuals, wider bid-ask spreads, decrease buying and selling pastime and extra rate actions whilst exceedingly huge orders input the marketplace. Liquidity subjects because monetary markets are constructed around continuous trade. Investors need to enter positions, investors need to execute orders, institutions want to transport huge amounts of capital and market makers want to provide prices. When liquidity is powerful, these sports can take place extraordinarily correctly. When liquidity weakens, even everyday trades can come to be extra steeply-priced or tough to execute. The concept will become in particular critical at some point of periods of marketplace stress. A marketplace can appear pretty liquid all through normal conditions however behave very in a different way whilst volatility rises and contributors end up more cautious. Bid-ask spreads can widen, to be had market intensity can fall and expenses can pass sharply as investors try to execute orders against a smaller pool of to be had liquidity. For traders, liquidity affects execution charges, slippage, spreads and the potential to enter or exit positions. For institutional buyers, it affects portfolio control and the price of transferring massive amounts of capital. For exchanges and economic establishments, liquidity is intently connected to marketplace excellent and resilience. Understanding marketplace liquidity therefore requires searching beyond trading volume by myself. Volume is one critical indicator, but liquidity also includes market intensity, transaction fees, execution velocity, fee impact and the quantity of individuals inclined to change. As economic markets end up increasingly more electronic and globally connected, liquidity has also end up extra dynamic. The same asset can experience very exclusive liquidity situations at one-of-a-kind times of the buying and selling day or for the duration of distinct market environments. What Is Market Liquidity? Market liquidity describes the ease with which an asset can be bought or sold without producing a substantial change in its market price. If a trader can purchase a large quantity of an asset quickly at prices close to the current market price, that market is considered relatively liquid. If selling even a modest quantity causes the price to fall significantly, the market is less liquid. This idea applies across different asset classes. Stocks traded by large companies may have thousands or millions of shares changing hands regularly. Major government bonds can have deep institutional markets. Foreign exchange markets can support extremely large transaction volumes across global participants. On the other hand, smaller stocks, thinly traded bonds or certain alternative investments may have much less liquidity. Liquidity is therefore not a characteristic that is simply “present” or “absent.” It exists on a spectrum. Liquidity Level Typical Characteristics Trading Impact Very High Large participation, deep order books, narrow spreads Low execution friction High Strong volume and consistent buyers/sellers Generally efficient execution Moderate Reasonable activity but less market depth Larger orders may affect price Low Few participants and wider spreads Higher transaction costs Very Low Limited trading and shallow market depth Large price impact and difficult exits The important concept is price impact. Suppose an investor wants to buy shares worth $10,000. In a highly liquid market, that order may have little effect on the price because many sellers are available. Now imagine an investor wants to purchase $10 million worth of an asset with limited trading activity. There may not be enough sellers at the current price. The investor may need to accept progressively higher prices to complete the order. The trade itself can therefore move the market. That is one of the clearest ways to understand liquidity. Why Does Market Liquidity Matter? Liquidity matters because investors do not simply care about the quoted market price. They care about the price at which they can actually execute a transaction. A market price is an indication of where buyers and sellers are currently willing to transact. But the availability of additional buyers and sellers determines how much an investor can trade around that price. For traders, liquidity affects several important elements of execution. Factor How Liquidity Affects It Bid-ask spread More liquidity generally supports narrower spreads Slippage Lower liquidity can increase slippage Market impact Large orders have greater impact in thin markets Execution speed Liquid markets generally support faster execution Volatility Weak liquidity can amplify price movements Exit risk Illiquid assets may be harder to sell Transaction costs Lower liquidity can increase effective trading costs Portfolio flexibility Liquid assets are easier to rebalance This is why professional traders and institutional investors pay close attention to liquidity conditions before executing large trades. A trade that appears profitable based on the quoted price may become much less attractive after accounting for spread, market impact and slippage. The Four Main Dimensions of Market Liquidity Market liquidity is usually understood through several related dimensions rather than a single measurement. The most important dimensions are tightness, depth, immediacy and resilience. 1. Tightness Tightness refers to transaction costs, particularly the difference between the price a buyer is willing to pay and the price at which a seller is willing to sell. A narrow bid-ask spread generally indicates lower immediate transaction costs. 2. Depth Depth describes how much trading interest exists at different prices. A market with substantial orders near the current market price can absorb larger trades without significant price movement. 3. Immediacy Immediacy refers to how quickly

Real-Time Payments: How Instant Payments Are Changing Banking and Financial Services

Real-Time Payments: How Instant Payments Are Changing Banking and Financial Services

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For decades, moving money through the financial system often meant accepting a delay between sending a payment and receiving usable funds. Traditional payment systems were built around processing windows, batch settlement, banking hours and multiple intermediaries. That model worked well when businesses and consumers were accustomed to waiting, but expectations have changed dramatically as digital services have become faster and increasingly available around the clock. Today, customers expect money to move almost as quickly as information. Now customers expect money to move as quickly as information does. A person can send a message to someone on the side of the world in seconds. They can order something online. Get confirmation quickly. Real-time notifications are instant. So it makes sense to anyone when they send money and have to wait days for it to arrive. This is where real-time payments are changing banking and financial services. Real-time payment systems allow funds to be transferred and made available to recipients within seconds, often operating continuously rather than only during traditional banking hours. The concept is no longer limited to faster peer-to-peer transfers. It is increasingly being integrated into business payments, payroll, treasury management, ecommerce, account funding, insurance disbursements, lending, cross-border transactions and embedded financial services. The scale of this transformation is becoming increasingly visible. McKinsey estimates that instant-payment value flows across the 15 largest economies with adopted instant-payment rails reached nearly $22 trillion in 2024 and expects annual growth of roughly 15% to 18% over the next five years. J.P. Morgan’s 2026 fintech research describes real-time payments as becoming “table stakes” for financial institutions and reports substantial growth in U.S. RTP activity and participation. But faster payments also create new challenges. A transaction that settles instantly can be harder to reverse. Fraudsters can act fast. They use engineering, steal credentials and move money before a bank or customer realizes something is wrong. De Nederlandsche Bank found that payment fraud, in the Netherlands increased in 2025. It pointed out that fraudsters often target international payments because those transactions are harder to stop or undo.This creates an important paradox. The faster money moves, the faster financial institutions need to detect risk. As a result, real-time payments are not simply a technology upgrade to existing payment systems. They are forcing banks, fintechs and financial institutions to rethink fraud management, liquidity, treasury operations, customer experience, payment infrastructure and even the economics of financial services. What Are Real-Time Payments? Real-time payments are electronic payment transactions in which funds are transferred and made available to the recipient almost immediately, generally within seconds and with payment infrastructure operating continuously or close to continuously. Unlike traditional batch-based payment systems, real-time payment rails are designed around immediate processing and settlement. The exact characteristics vary between countries and payment systems. Some systems settle directly through central-bank infrastructure, while others operate through commercial payment networks or interconnected systems. The important distinction is the availability of funds and speed of processing. Traditional Payment Model Real-Time Payment Model Processing may occur in batches Processing occurs continuously Settlement can take hours or days Funds can become available within seconds Banking-hour limitations may apply Designed for 24/7 availability Information may arrive separately Payment and data can move together Reconciliation may be delayed Faster reconciliation is possible Fraud controls can operate before or after settlement Fraud decisions must increasingly happen before or during payment Real-time payments should therefore not be understood simply as “faster bank transfers.” They represent a different operating model in which speed, availability, data and risk management are closely connected. Real-Time Payments vs. Traditional Payments The difference becomes clearer when comparing the payment journey. A traditional bank transfer may involve payment initiation, validation, batch processing, clearing, settlement and eventual crediting of the recipient’s account. With a real-time payment, these stages are compressed into a much shorter timeframe. That compression creates a better customer experience but also reduces the time available for financial institutions to identify suspicious transactions. Feature Traditional Payments Real-Time Payments Processing Batch or scheduled Immediate Availability Often limited by processing cycles Typically 24/7 Settlement May take longer Near-immediate Customer experience Delayed confirmation Immediate confirmation Liquidity Can remain in transit Available quickly Fraud response time More time may be available Decisions need to happen rapidly Reconciliation Can be delayed Can happen closer to transaction time Business use cases Established Expanding rapidly This is why real-time payments require more than faster infrastructure. Banks need systems capable of making decisions in real time as well. Why Are Real-Time Payments Growing? The growth of real-time payments is being driven by several forces rather than one technological development. The first is changing customer expectations. Consumers increasingly expect instant access to money, instant transaction confirmation and digital experiences that do not depend on traditional banking hours. Businesses have similar expectations. A company waiting days for a payment can face unnecessary cash-flow pressure. Faster settlement can improve working-capital visibility and help businesses understand their actual cash position more quickly. he second driver is digital commerce. Ecommerce platforms, marketplaces and digital services increasingly operate continuously. A payment system that operates only during limited processing windows creates friction in an always-on digital economy. The third driver is financial infrastructure modernization. Banks are upgrading payment systems, adopting richer financial messaging standards and connecting to new payment rails. KPMG’s 2026 banking trends research identifies payments modernization as a major priority, with instant cross-border payments, open banking, payments AI and embedded finance among the areas attracting significant attention. The fourth driver is the growth of new business models. Embedded finance, digital wallets, marketplaces, payroll platforms and fintech applications can integrate payment functionality directly into customer workflows. The result is a broader shift: Payments are becoming part of digital infrastructure rather than a separate financial activity. Major Real-Time Payment Systems Around the World Real-time payments are not based on one global network. Different countries and regions have developed their own payment systems and infrastructures. Some of the most widely known examples include India’s UPI, Brazil’s Pix, the UK’s Faster Payments, the European Union’s instant-payment infrastructure,

Digital Banking Trends: How AI, Payments and Open Banking Are Changing Finance

Digital Banking Trends: How AI, Payments and Open Banking Are Changing Finance

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Banking has changed from an industry where customers visited branches and waited for transactions to an environment where financial services can be accessed almost instantly from a mobile device. Checking an account balance, transferring money, paying a bill, opening an account, applying for a financial product or receiving transaction alerts can now happen without a customer entering a branch. This shift has transformed digital banking from an additional banking channel into one of the most important ways financial institutions interact with customers. The transformation is also accelerating. Digital banking is no longer simply about putting traditional banking services on a website or mobile application. Banks are now redesigning the way financial services are delivered, supported and embedded into customers’ everyday digital activities. Artificial intelligence is being used for personalization, fraud detection, customer service and operational automation. Payment systems are becoming faster and more connected. Open banking is creating new ways to share financial information and initiate payments. Mobile applications are becoming more sophisticated, while APIs are allowing banking services to connect with fintech platforms and other digital ecosystems. Current industry research reflects this broader transformation. KPMG’s 2026 banking research highlights digital channels, AI, payments modernization, open banking, instant cross-border payments and embedded finance as important areas of banking investment. Capgemini similarly identifies personalization, seamless checkout, payment orchestration, cybersecurity and new payment methods as major banking trends. The scale of digital adoption is also visible in mobile banking. Sensor Tower’s 2026 digital banking research reported that banking app downloads remained above half a billion per quarter during early 2026, while sessions grew faster than downloads, showing that customers are increasingly using mobile banking as part of everyday financial activity. For banks, this creates both opportunity and pressure. Customers are no longer comparing one bank only with another bank. They are comparing their banking experience with the simplicity of ecommerce, digital wallets, online marketplaces and other technology platforms. A slow banking application, complicated onboarding process or confusing payment experience can therefore become a competitive disadvantage. At the same time, financial institutions cannot innovate without considering security, privacy, regulation and trust. The future of digital banking will therefore not be determined by technology alone. It will be shaped by how effectively banks combine technology, data, payments, security, regulation and customer experience. This article examines the major digital banking trends changing finance and explains how AI, payments, open banking, mobile experiences, embedded finance and modern banking infrastructure are changing the financial services industry. What Is Digital Banking? Digital banking means offering banking products and services using tools and technology. It does not depend on physical bank branches for transactions. This includes things like banking, mobile banking, opening accounts digitally making electronic payments getting loans through digital systems managing wealth online providing customer service through digital channels and using APIs to connect financial services. However, modern digital banking is broader than simply using a banking application. Traditional digital banking focused heavily on moving existing banking processes online. Modern digital banking is increasingly focused on rebuilding the underlying customer journey. Traditional Banking Modern Digital Banking Branch-centered Mobile and digital-first Manual processes Automated workflows Limited operating hours 24/7 availability Product-focused Customer-journey focused Periodic transactions Real-time financial activity Separate systems Connected ecosystems Generic communication Personalized experiences Reactive service Predictive and proactive service Closed infrastructure API-enabled infrastructure This distinction is important because the next phase of digital banking is not simply about replacing branches with apps. It is about making financial services more connected, personalized and available wherever customers need them. Why Digital Banking Is Changing So Quickly Several forces are driving the transformation of banking simultaneously. Customer expectations are one of the strongest drivers. Consumers increasingly expect financial services to be fast, intuitive and available through mobile devices. Businesses similarly expect banking functionality to connect directly with accounting platforms, enterprise software and payment systems. Technology is another major driver. Cloud infrastructure, APIs, artificial intelligence, data platforms and modern payment rails are giving banks capabilities that were difficult to deliver through older architectures. Competition is also changing. Banks increasingly compete with fintech companies, digital wallets, payment platforms and technology companies that can introduce financial features without operating traditional branch networks. Regulation is another important factor. Open banking frameworks, payment modernization, digital identity requirements, cybersecurity expectations and data-protection rules are influencing how financial institutions build digital services. The result is a banking environment where modernization is becoming less optional. KPMG’s 2026 Banking Technology Survey found that banking executives are prioritizing modernization across AI, cybersecurity, payments and data, while changing customer expectations and legacy systems remain important drivers of payments modernization. Major Digital Banking Trends Changing Finance The digital banking landscape is being shaped by several interconnected trends rather than one technology. Digital Banking Trend What It Is Changing Artificial intelligence Customer service, fraud detection, personalization and operations Open banking Data sharing and financial connectivity Real-time payments Speed of money movement Mobile banking Everyday customer interaction Embedded finance Where financial services are delivered API banking Connectivity between banks and digital platforms Digital onboarding Account opening and customer acquisition Personalization Financial products and customer engagement Cybersecurity Protection of digital financial services Cloud modernization Banking infrastructure and scalability Digital identity Authentication and onboarding Payment orchestration Management of multiple payment methods Data-driven banking Decision-making and financial insights These trends are connected. Open banking creates data connectivity. AI can analyze that data. Real-time payments provide faster movement of money. APIs connect these capabilities with applications. Mobile banking gives customers access to the resulting services. This interconnected model is what makes modern digital banking different from earlier forms of online banking. 1. AI Is Becoming a Core Layer of Digital Banking Artificial intelligence has become one of the most important technologies shaping digital banking. Banks are using AI across customer service, fraud prevention, document processing, risk management, compliance, personalization and internal operations. The focus is also changing. Earlier banking AI projects often focused on individual use cases such as chatbots or fraud models. Financial institutions are increasingly looking at AI as a broader capability that

Embedded Finance in Banking: How Banks Are Moving Financial Services Into Digital Platforms

Embedded Finance in Banking: How Banks Are Moving Financial Services Into Digital Platforms

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The bank is not limited to branch, bank website, or even traditional mobile banking application. Financial services are a growing number of virtual products that people already use every day. A business owner can access capital transfers through an accounting platform, a consumer can get financing from buying goods online, a freelancer can get invoices through an enterprise platform, and a marketplace can offer checking accounts or card games without asking customers to leave their environment Think about how banking, distribution, manufacturing sharing, buyer sales contact and. This evolution is commonly described as embedded finance. At its simplest, embedded finance means integrating financial products and services directly into non-financial digital experiences. Payments, banking accounts, cards, lending, insurance, investment products, and other financial capabilities can become part of software platforms, marketplaces, ecommerce applications, enterprise systems, and consumer applications. Instead of requiring customers to visit a separate financial institution, the financial service appears at the moment and place where it is useful. For banks, this represents a significant change in strategy. Traditionally, banks controlled much of the customer journey. Customers visited branches, logged into banking portals, or opened dedicated banking applications to access financial products. Digital platforms are changing that model by becoming the place where financial decisions happen. As a result, banks increasingly have an opportunity to provide the regulated financial infrastructure behind those experiences while digital platforms control the customer-facing interface. The opportunity is already becoming substantial. McKinsey estimates that embedded finance revenue in Europe could exceed €100 billion by the end of the decade, with embedded-finance channels potentially accounting for 20% to 25% of retail and SME lending by 2030. Embedded finance is more than just placing a payment button inside an app. It is a transformation in how financial services are shared. Banks must decide which services should be embedded, which platforms they should work with how APIs and cloud systems should connect services how responsibilities should be split and how compliance and customer safety can be protected when financial services run through third-party interfaces. This is why embedded finance in banking has become an important strategic conversation for financial institutions. The future may not be about banks disappearing from the customer journey. Instead, banks may become more deeply integrated into the digital journeys customers already use. What Is Embedded Finance in Banking? Embedded finance in banking means putting services right inside the digital tools people already use. Of going to a separate bank website or app users can access things like loans, payments or insurance while staying on their favorite platform. The underlying financial service can still be provided by a regulated bank or financial institution. What changes is the distribution model. For example, imagine a small retailer using an accounting platform. Historically, the retailer might use the accounting software for invoices and financial reporting, then separately visit a bank to apply for a business loan. With embedded finance, the accounting platform could analyze relevant business information and present a financing option directly within the software. The retailer can discover, apply for, and potentially receive financing without leaving the platform. The same principle can apply to payments, accounts, cards, insurance, foreign exchange, and other services. Traditional Banking Model Embedded Finance Model Customer visits bank Financial service appears inside an existing platform Bank owns most of the customer interface Platform may own the customer experience Products are accessed separately Products are integrated into workflows Banking relationship is destination-based Banking becomes experience-based Manual or multi-step processes More contextual and automated journeys Bank application or branch SaaS, ecommerce, marketplace, or app Product-first distribution Customer-journey-first distribution The important distinction is that embedded finance does not necessarily mean the digital platform becomes a bank. In many models, regulated institutions continue to provide accounts, payment infrastructure, lending capabilities, compliance functions, safeguarding, and other regulated services while the platform provides the digital interface and customer relationship. This creates an ecosystem rather than a simple replacement of banks. Why Banks Are Moving Financial Services Into Digital Platforms The rise of embedded finance is tied to a shift in what customers and businesses expect. People now want experiences that are quick, relevant and linked together. When customers are already using a platform to run a business buy a product manage staff or talk to customers moving them to a separate financial application can make things harder. Consider an ecommerce marketplace. A seller may need to receive payments, manage cash flow, access working capital, issue invoices, and monitor expenses. If every financial activity requires a different provider, the seller must move between multiple systems. A platform that integrates several of these capabilities can become much more valuable because it connects financial services directly to the workflow. The same logic applies to consumers. Someone purchasing a high-value product may need financing at the exact moment they decide to buy. Offering financing during checkout can be more convenient than asking the customer to leave the store, search for a lender, complete a separate application, and return to the purchase. The underlying principle is simple: financial services become more useful when they are available at the point of need. FIS describes APIs as a key foundation for banks extending products into third-party platforms, while also highlighting the strategic issues around security, compliance, customer ownership, and differentiation. The Shift From Banking as a Destination to Banking as a Layer For decades, banking was treated as a destination. Customers knew where they were going when they wanted financial services: a bank branch, an ATM, a banking website, or a mobile banking app. Embedded finance changes that mental model. Banking increasingly becomes a layer underneath other digital experiences. Customers may not think about the bank providing a particular service because their immediate interaction happens through the platform they already trust. This can be compared to the evolution of internet infrastructure. Users do not normally think about the servers, databases, content delivery networks, or cloud infrastructure supporting a website. They simply interact with the application. In a similar way, embedded finance aims to

Agentic Payments in Banking: How AI Agents Will Change the Way We Pay

Agentic Payments in Banking: How AI Agents Will Change the Way We Pay

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For decades, digital payments have become increasingly faster and easier, but one thing has remained largely unchanged: people still have to tell the payment system what to do. A customer searches for a product, compares prices, chooses a merchant, enters payment information, confirms the transaction, and waits for the payment to be processed. Even mobile wallets and one-click checkout have mainly improved the speed of a process that still depends on a human making the final decision. Artificial intelligence is beginning to change that model. The emergence of AI agents introduces a different approach to commerce and financial services. Instead of simply recommending a product, answering a question, or displaying a payment button, an AI agent can potentially understand a customer’s objective, search for an appropriate option, evaluate alternatives, follow predefined rules, and initiate a transaction on the customer’s behalf. This is the foundation of agentic payments in banking. The concept is moving beyond experimentation. The IMF’s 2026 analysis of agentic AI and payments describes a shift from human-initiated instructions toward agent-mediated decisions and highlights authorization, settlement, compliance, liquidity, resilience, cybersecurity, traceability, and legal uncertainty as important considerations. Payment networks are also actively developing this infrastructure. Visa announced live agentic commerce transactions in Europe in July 2026, with AI agents browsing products, selecting items, and initiating purchases within customer-defined parameters. Mastercard has similarly developed Agent Pay and reported live end-to-end agentic payment activity with European banking partners. This means the conversation is no longer simply about whether AI can recommend what people should buy. The bigger question is: What happens when AI can decide when, where, and how to spend money within rules established by the customer? That is where agentic payments become important for banks, fintech companies, payment providers, merchants, regulators, and consumers. What Are Agentic Payments in Banking? Agentic payments are payment transactions in which an AI agent acts on behalf of a customer, business, or another authorized entity to initiate or facilitate a financial transaction. Unlike traditional payment automation, an AI agent may not simply execute a fixed instruction such as “pay this bill every month.” An agent can potentially interpret a broader objective and determine the steps required to accomplish it. An agentic system could potentially: The payment itself is only one part of the process. The important change is that decision-making and transaction execution become connected. Agentic Payments vs Traditional Payments Feature Traditional Payments Agentic Payments Transaction initiation Human Human or AI agent Product discovery Human Human or AI agent Price comparison Usually human AI-assisted or autonomous Payment decision Human Agent within defined permissions Authorization User authentication Delegated authorization + controls Payment timing User-selected Potentially agent-selected Personalization Limited Highly contextual Automation Rule-based Goal-oriented Risk management Predefined systems Dynamic + predefined controls Customer interaction Checkout-focused Objective-focused Example User buys a product Agent finds and buys the product within rules The distinction is important because agentic payments are not simply another version of recurring payments or automated billing. The defining characteristic is that the system can make decisions and take actions within a delegated scope. How Do Agentic Payments Work? The architecture behind agentic payments can vary considerably, but a useful way to understand the model is to separate it into several stages. 1. User Intent Everything begins with the customer’s objective. The customer might say: The AI agent translates this natural-language objective into a structured task. 2. Agent Planning The agent determines what needs to happen. For a purchase, it might search merchants, compare products, evaluate prices, check delivery terms, and determine whether the transaction meets the customer’s rules. For a business payment, it might check invoices, verify vendors, examine payment limits, and determine whether approval is necessary. 3. Authorization This is one of the most important parts of agentic payments. An AI agent should not receive unlimited access to a customer’s bank account simply because the customer has asked it to perform a task. Instead, the system needs to establish: 4. Authentication and Risk Checks Before the payment is executed, the payment ecosystem can apply authentication, fraud detection, identity verification, transaction monitoring, and other risk controls. 5. Payment Execution The agent initiates the transaction through an approved payment method or payment network. 6. Settlement and Confirmation The transaction is processed and settled through the underlying financial infrastructure. 7. Audit and Reporting The system should maintain records showing: The IMF’s framework is useful here because it separates the problem into intent, authorization, and settlement, emphasizing that agentic capabilities need to coexist with the deterministic requirements of payment systems. The Key Difference Between AI Assistants and AI Payment Agents Not every AI assistant is an agentic payment system. That difference may look small from the customer’s perspective, but technically it is enormous. AI Capability AI Assistant AI Payment Agent Answer questions ✓ ✓ Provide recommendations ✓ ✓ Search products ✓ ✓ Compare prices ✓ ✓ Make decisions Limited ✓ Initiate payments Usually no ✓ Operate under financial permissions Limited ✓ Execute multi-step tasks Limited ✓ Monitor transaction outcomes Limited ✓ Act autonomously Limited ✓ The financial industry therefore needs to treat payment agents as more than conversational software. They are becoming participants in the transaction process. Why Agentic Payments Matter for Banking Agentic payments are payment transactions in which an AI agent acts on behalf of a customer, business, or another authorized entity to initiate or facilitate a financial transaction. Agentic commerce introduces a new layer between the customer and the financial institution. Instead of: Customer → Merchant → Payment Network → Bank the future could increasingly look like: Customer → AI Agent → Merchant/Service → Payment Infrastructure → Bank That additional layer creates opportunities and challenges. Banks could become the trusted financial control layer that gives AI agents permission to transact while maintaining customer protection, compliance, and visibility. This could create a major opportunity for banks that build agent-ready payment infrastructure early. 7 Major Benefits of Agentic Payments in Banking 1. Faster and More Convenient Payments The most obvious benefit is convenience. Customers could delegate

AI Fraud Prevention in Banking: How Banks Are Fighting Smarter Scams

AI Fraud Prevention in Banking: How Banks Are Fighting Smarter Scams

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Banking fraud has always changed as financial technology changed. When banks went from paper records to credit cards to online banking, mobile apps, instant payments and digital wallets criminals found new ways to take advantage of each change. Today, the problem is becoming even more complicated because artificial intelligence is changing both sides of the fraud equation. Banks can use AI to identify suspicious activity faster, understand unusual transaction patterns, and strengthen fraud prevention, but criminals can also use AI to create more convincing phishing messages, deepfake identities, synthetic voices, and highly personalized scams. This creates a new reality for financial institutions. Traditional fraud prevention systems that depend heavily on fixed rules and historical patterns may struggle when fraudulent behavior changes quickly. A transaction can look normal on its own while becoming suspicious when combined with account behavior, device information, payment history, location, merchant activity, and other signals. This is where AI fraud prevention in banking becomes increasingly important. Financial authorities are paying close attention to this shift. In 2026, the Bank for International Settlements highlighted that AI can strengthen financial defenses while simultaneously increasing the speed, scale, and complexity of cyberattacks. The BIS has also pointed to deepfakes, synthetic identities, and AI-enabled deception as emerging risks for financial stability. The challenge for banks is therefore not simply to “use AI.” The real challenge is to use artificial intelligence responsibly to understand suspicious behavior earlier, reduce false alarms, protect legitimate customers, and respond to new forms of financial crime without creating an opaque system that nobody can properly explain. The future of banking security will increasingly depend on that balance. What Is AI Fraud Prevention in Banking? AI fraud prevention in banking refers to the use of artificial intelligence, machine intelligence, behavioral analytics, automation, and related technologies to identify, prevent, detect, and respond to potentially fraudulent financial activity . Traditional fraud systems typically operate through predetermined guidelines. A financial institution can create a rule that flags a transaction above a positive amount, a price out of unusual territory, or multiple transactions that take place within a quick time frame are still useful because rules can be true and predictable, but they can conflict when scammers change their pace. AI introduces a more adaptive approach. Instead of looking at the single easiest transaction, an AI-powered fraud detection engine can analyze the relationships between multiple signals. It can analyze based on behavior patterns and detect behaviors that seem out of sync with the customer’s daily interests. This does not mean that the AI model will robotically know if a transaction is fraudulent. Rather, it may provide an opportunity or threat assessment to determine whether additional verification or investigation is important. This distinction is important because banking decisions can have serious consequences. A legitimate transaction incorrectly blocked by a fraud system can create frustration for customers, while a fraudulent transaction that goes undetected can cause financial loss and damage trust. The strongest approach is therefore not simply automation. It is a combination of AI detection, risk-based controls, human oversight, and continuous monitoring. Why Banking Fraud Is Becoming Harder to Detect The biggest change in modern financial fraud is not simply that criminals have more tools. It is that fraud can now be personalized and scaled much more easily. A traditional phishing email is likely to contain obvious spelling mistakes or a message that looks suspicious. AI could make fraudulent conversations much extra convincing. A scammer may possibly give messages that suit the chosen victim’s language, tone, and context. Voice cloning can make a mobile name appear to come from a dependent man or woman, while a deepfake video can create a convincing impersonation. The Bank for International Settlements has warned that AI-enabled scams can make phishing more personalized and persuasive at scale, including through deepfake impersonation and customized scam messages. This matters for banks because many traditional fraud controls assume that identity signals are relatively difficult to imitate. If a criminal can create convincing synthetic identities or manipulate voice and visual information, financial institutions need more than a single authentication signal. A modern fraud prevention system therefore needs to understand behavior, not just identity. Traditional Fraud Detection vs AI Fraud Detection Area Traditional Fraud Detection AI Fraud Detection Primary approach Rules and thresholds Machine learning and behavioral analysis Data analysis Often transaction-focused Multi-signal analysis Adaptability Requires rule updates Can identify changing patterns Fraud patterns Known scenarios Known and emerging patterns Real-time analysis Possible Highly suited to continuous scoring False positives Can be significant Can be reduced with better modeling Customer behavior Limited context Behavioral profiling Network relationships Limited Graph and relationship analysis Deepfake detection Limited Can incorporate multiple signals Investigation Manual-heavy AI-assisted Monitoring Rule-based Continuous and adaptive The difference does not mean banks should abandon rules. Rules are still valuable for clearly defined scenarios and regulatory controls. Instead, AI can sit alongside traditional controls and provide a more dynamic understanding of risk. How AI Detects Fraudulent Transactions At the center of modern AI fraud detection in banking is the ability to analyze large amounts of information quickly. Imagine that a customer normally uses a banking application from one device, makes payments within a particular geographic region, and typically transfers relatively small amounts. Suddenly, a transaction is initiated from a new device, the beneficiary has never been used before, the transaction amount is significantly larger than usual, and several account details have changed within a short period. None of these signals necessarily proves fraud. But together, they may indicate unusual behavior. An AI model can combine these signals and generate a risk assessment. The bank can then decide what action is appropriate. A low-risk transaction might continue normally. A moderately risky transaction might require additional authentication. A high-risk transaction could be paused for review. This approach is often more effective than relying on a single rule because fraud is rarely defined by one isolated event. It is usually the combination of behaviors that creates the warning sign. Behavioral Analytics Is Becoming Central to

Open Banking Fraud: Why Faster Payments Need Smarter Risk Controls

Open Banking Fraud: Why Faster Payments Need Smarter Risk Controls

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Open banking has changed the way people and businesses interact with services. Customers can now link accounts start payments share details and use new financial products without always going through traditional banking channels. For banks, fintech companies and payment providers this opens the door to faster connected and more convenient services. But there is another side to this transformation. As payments become faster and more automated, fraud prevention becomes more difficult. A traditional payment system may have given financial institutions more time to review unusual transactions. Modern open banking payments can move quickly through API-based journeys, while customers increasingly expect transactions to be completed almost instantly. That creates a difficult challenge: How can banks stop fraudulent payments without slowing down legitimate ones? This question sits at the heart of open banking fraud prevention. Recent data from Open Banking Limited shows that open banking fraud remains lower by transaction volume than fraud across the wider UK payments industry. However, fraud volumes increased during the first quarter of 2026, and Authorised Push Payment (APP) fraud accounted for more than two-thirds of reported open banking-related fraud cases. Open Banking Limited also reports that fraud techniques are becoming more sophisticated, including impersonation, phishing, smishing and fake-refund scams. The answer is not just adding login screens or blocking more transactions. Banks and fintechs need risk controls. These controls must understand the picture. Transaction context, customer behaviour, payment patterns and real-time fraud signals. This article looks at why open banking fraud’s changing why faster payments make fraud harder to stop and how financial institutions can build a smarter way to protect payments. What Is Open Banking Fraud? Open banking fraud refers to fraudulent activity that occurs through open banking-enabled financial services, particularly account information and payment initiation services. Open banking allows authorised third-party providers to connect with financial institutions through secure APIs. Depending on the service, customers can give permission for a third party to access account information or initiate payments from their bank account. The model creates significant benefits. For example, a customer might: However, every additional connection can introduce another point where fraudsters may attempt to manipulate a customer, compromise credentials or exploit weaknesses in the payment journey. Importantly, open banking fraud is not limited to someone breaking into a bank account. A fraudster may instead manipulate the customer into making a legitimate-looking payment. This is particularly important in Authorised Push Payment fraud, where the customer authorises the payment themselves after being deceived. Open Banking Limited’s latest fraud monitor found that APP fraud remains the dominant fraud category in open banking payments, accounting for more than two-thirds of reported cases. That changes the fraud-prevention problem. The system is no longer asking only: “Is this customer authorised to make this payment?” It also needs to ask: “Does this payment make sense given the customer’s behaviour, transaction context and relationship with the recipient?” Why Faster Payments Are Changing the Fraud Landscape Speed is one of the biggest advantages of modern payments. Consumers want instant confirmation. Businesses want faster settlement. Merchants want fewer abandoned transactions. Fintech platforms want seamless payment experiences. But speed also reduces the time available for intervention. Consider a simplified example. A customer receives a convincing message claiming that their investment account requires an urgent payment. They follow a link, authenticate with their bank and send €10,000. From a conventional authentication perspective, the transaction may look legitimate. This is one of the biggest challenges in modern payment security. Fraudsters increasingly attack people and processes, not just technical infrastructure Faster payments create several challenges: Challenge Why It Matters Instant execution Less time to intervene before funds move Social engineering Customers may authorise fraudulent transactions themselves API connectivity More participants can interact with payment journeys Automated fraud Attackers can scale campaigns quickly Cross-platform activity Fraud signals may exist across multiple providers Account takeover Compromised accounts can be used for rapid transfers Mule accounts Fraudulent funds can move through legitimate-looking accounts The result is a shift from traditional transaction monitoring toward real-time payment risk management. Why Open Banking Fraud Is Different Open banking introduces a more connected financial ecosystem. A payment journey can involve several parties, including: Each participant may have information that another participant does not. For example, a payment initiation provider may understand the merchant relationship, while the bank may have detailed knowledge of the customer’s historical behaviour. If those signals remain isolated, fraud detection becomes weaker. This is why data sharing and transaction context are becoming increasingly important. Open Banking Limited has highlighted the importance of transaction-level information such as Transaction Risk Indicators (TRIs) and enhanced fraud data to strengthen open banking fraud prevention. The goal is not simply to collect more data. The goal is to collect the right data at the right moment. The Growing Threat of Authorised Push Payment Fraud Authorised Push Payment fraud deserves particular attention because it challenges traditional fraud models. In an unauthorised transaction, a criminal may access an account and initiate a payment without the customer’s permission. In APP fraud, the customer is manipulated into authorising the payment. The fraud can therefore pass several traditional security checks. Common APP fraud scenarios include: Open Banking Limited reported that investment fraud remains one of the largest identified APP categories by value in its June 2026 fraud monitor. This demonstrates why authentication alone cannot solve modern payment fraud. A system can successfully confirm that the person making the payment is the account holder while still failing to determine whether the reason for the payment is fraudulent. That is where smarter risk controls become valuable. Common Types of Open Banking Fraud Understanding the different forms of fraud is essential for designing effective controls. 1. Authorised Push Payment Fraud The customer is manipulated into approving a fraudulent transaction. The payment may appear completely legitimate from a technical perspective. 2. Account Takeover A fraudster gains control of a customer’s account and uses it to initiate transactions. Attack methods can include: 3. Phishing and Smishing Fraudsters use email or SMS messages to convince

How generative AI is transforming EHRs and care delivery

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How generative AI is transforming EHRs and care delivery Download Now Administrative costs consume over 40% of hospital expenses, while clinicians spend up to 41% of their workday in EHRs—intensifying burnout and limiting patient care. As healthcare systems face these pressures, integrating generative AI into EHRs offers a transformative solution. This e-book explores how health systems use generative AI and cloud infrastructure to modernize EHRs, showcasing real-world implementations with measurable impact. Discover strategies for: Reducing documentation with AI-powered scribes Enhancing patient engagement via multilingual chatbots Improving efficiency through data exchange and automation Read the e-book for practical EHR modernization approaches.   Download Now

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