To support banks and deliver the payment protection consumers expect, Mastercard unveils Mastercard A2A Protect, a new global service launching first in the UK. By combining cutting-edge fraud prevention technology and a new clear dispute resolution framework, Mastercard A2A Protect will enable banks to provide consumers with the appropriate levels of protection against fraudsters. Mastercard A2A Protect will initially focus on the most acute needs, such as Authorised Push Payment fraud, providing a combination of preventative measures, consumer protections and a process to recover funds. Subsequent phases will establish a process for recovering funds across a broader range of scenarios, including where goods and services have been paid for. Ultimately, the service intends to support participating bank customers before, during, and after each transaction, by: Preventing fraud: 1. Enhancing CFR’s transaction scoring capabilities and helping to identify more high-risk transactions, complementing banks’ own efforts 2. Leveraging Trace, a Mastercard solution which harnesses AI and network data insights to prevent money laundering and financial crime, and supports banks to identify and close ‘mule’ accounts 3. Delivering an industry-wide standardised fraud and loss reporting mechanism, which will provide banks with richer fraud insights Protecting consumers: 4. Providing banks with a simple framework and comprehensive set of multilateral standards to drive best practice and safeguard consumers. Efficiently addressing transactional and fraud protection issues, as well as goods and services protection issues, where relevant for consumers. Recovery of funds: 5. Introducing a uniform procedure for banks to resolve disputes and recover funds, across multiple use cases via Mastercard’s existing centralised platform, reducing costs and speeding up resolution
ICE’s integration of Freddie Mac’s AIM Check API with its mortgage analyzer tech to help lenders and third-party originators to automatically verify a borrower’s income, assets and credit speeding up the review process for underwriters and quality checks
ICE Mortgage Technology, announced the integration of Freddie Mac’s AIM Check API with the ICE Mortgage Analyzer. The AIM Check API uses Freddie Mac’s Loan Product Advisor (LPA) Asset and Income Modeler (AIM) separately from an LPA application. This allows Freddie Mac-approved sellers and third-party originators to review a borrower’s income and employment before submitting a full application to LPA. The calculated income will be assessed during subsequent submissions to LPA, and the data automatically flows into ICE’s Encompass loan file. The integration supports faster underwriting, simplified income verification, fewer loan defects and a better user experience for lender staff. Tim Bowler, president of ICE Mortgage Technology, said “The interconnectivity of our end-to-end mortgage platform drives efficiencies throughout the mortgage lifecycle that lower costs and reduce errors for lenders, investors and borrowers.” With the new AIM Check API integration, mortgage analyzers use artificial intelligence to automatically verify a borrower’s income, assets and credit, speeding up the review process for underwriters and helping with quality checks after closing. All documents and data stay in sync across ICE Mortgage Technology’s system. “The integration of the AIM Check API with ICE’s Mortgage Analyzers helps lenders streamline income assessment, reduce errors and deliver faster service.”
NewtekOne’s integrated onboarding process for business owners delivers both a zero-fee checking account and a merchant payment solution simultaneously, using a single, streamlined data capture in a single online application process
NewtekOne, and its national bank subsidiary Newtek Bank have launched a game-changing innovation for independent business owners: a fully integrated onboarding process that delivers both an approved Newtek Bank true Newtek Zero-Fee Business Banking checking account and a Newtek Merchant Solutions, LLC (“NMS”) merchant payment solution—simultaneously, using a single, streamlined data capture in a single online application process. This unified experience means business owners get everything they need to accept payments and manage cash flow in one step. When a client is onboarded for a Newtek Bank checking account, the client automatically receives an approved NMS merchant account; when a client applies for an NMS merchant account, the client gets an approved Newtek Bank account, with merchant payments settling directly into the Newtek Bank account.1 All activity—banking, batches and returns—is visible to the client and managed from the Newtek Advantage® Dashboard (patent pending). And with the opening of the Instant Merchant Account, business owners can begin accepting debit and credit card payments, with no waiting, no second applications — just instant access to revenue. The Newtek Advantage Dashboard gives business owners a single online portal for banking, payments, payroll, insurance, IT, and more. It’s built to operate in real time, providing transparency and control that legacy banks and third-party providers simply cannot match. Once approved, the client merely needs to authorize the opening of the respective account.
Anthropic’s new Claude Opus 4.1 model scores 74.5% on SWE-bench Verified, surpassing OpenAI’s o3 model at 69.1% and Google’s Gemini 2.5 Pro at 67.2%, indicating its dominance in AI-powered coding assistance
Anthropic unveiled the latest version of its flagship artificial intelligence model, the same day that OpenAI released its first two open reasoning models since 2019. Claude Opus 4.1 is better at agentic tasks, coding and reasoning, according to a company blog post. Leaks of Claude Opus 4.1 began appearing the day before on social platform X and TestingCatalog. Anthropic Chief Product Officer Mike Krieger said this release is different from previous model unveilings. Claude Opus 4.1 is a successor to Claude Opus 4, which launched May 22. Opus 4.1 shows gains on benchmarks such as SWE-Bench Verified, a coding evaluation test, where it scores two percentage points higher than the previous model. The 4.1 model is also strong in agentic terminal coding, with a score of 43.3% on the Terminal-Bench benchmark compared with 39.2% for Opus 4, 30.2% for OpenAI’s o3, and 25.3% for Google’s Gemini 2.5 Pro. Customers such as Windsurf, a coding app being acquired by Cognition, and Japan’s Rakuten Group have reported quicker and more accurate completion of coding tasks using Claude Opus 4.1. The Claude Opus 4.1 release came amid signs that rival OpenAI is nearing the debut of GPT-5
Spend management platform Coupa’s Tariff Impact Planning app explores specific tariff impacts on input and manufacturing costs, and identifies potential ways to qualify for duty drawbacks based on the flow of goods through the supply chain
Coupa, the AI platform for total spend management, announces its Tariff Impact Planning (TIP) app, part of Coupa’s Supply Chain Solutions suite, designed to help businesses navigate global trade and tariff policies and ensure profitability amidst widespread uncertainty. Coupa’s Supply Chain Solutions enable leaders to seamlessly build tariff-optimized supply chains that assess current networks, future implications, and alternate strategies to balance tariff reduction, operational efficiency, and protect bottom-lines. Coupa’s Tariff Impact Planning app, known as TIP, literally offers insights and tips needed for businesses to respond dynamically to safeguard margins and minimize disruption. Key features of the TIP app include: Tariff Optimization: Proactively review current supply chain networks and explore alternate strategies to mitigate tariff impacts, considering trade-offs between cost, service, and risk in production and sourcing locations. Duty Drawbacks: Explore specific tariff impacts on input and manufacturing costs, and identify potential ways to qualify for duty drawbacks based on the flow of goods through the supply chain. Layered Tariffs: Evaluate the costs of raw material inputs, manufacturing for semi-finished goods, and goods sold for finished goods to avoid unnecessary compounding. Pricing & Market Access: Assess market and policy scenarios to inform pricing strategies, helping to navigate potential cost increases while balancing competitiveness and customer impact
CommunityWFM’s forecasting solution for contact centers applies AI to quickly analyze historical data, produces recurring, optimized forecasts on call volume and average handle time and auto-generates staffing requirements
CommunityWFM, one of the premier contact center workforce management software solutions, announced the debut of their new AI driven automated forecasting solution. The latest release of the extensive capabilities utilizes AI to optimize the forecasting process even further, minimizing work and saving contact centers both time and resources. Also known as AI automated forecasting, this solution views historical data quickly and under an analytics lens to create recurring and optimized forecasts with minimal human involvement. With the first iteration based around call volume and average handle time forecasting, this solution involves only a singular set up experience for the end user. Once the initial configuration has been established, AI and machine learning are leveraged to automatically retrain the models and make predictions, then automatically generate staffing requirements and publish the results. With this technology, an end user of the CommunityWFM product could build a recurring forecast built to their desired parameters that will auto generate every other Friday. The end user only needs to complete the initial set up process and the forecast will be created automatically on their behalf moving forward. The date range can vary in many ways to meet the ever-changing demands of a contact center.
Ripple to pay $200 million for stablecoin payment platform Rail, adding virtual accounts and automated back-office infrastructure capability; SEC’s 2020 lawsuit against Ripple Labs is officially over
Ripple announced it has agreed to acquire Rail, a stablecoin-powered platform for global payments, for $200 million. With this deal, Ripple and Rail will deliver the most comprehensive stablecoin payments solution available in the market. This acquisition will boost Ripple’s standing as the leader in digital asset payments infrastructure. Rail adds to Ripple’s capabilities with virtual accounts and automated back-office infrastructure, streamlining operations. Together, they will: Stablecoin On/Off-Ramps and Asset Flexibility: offer comprehensive stablecoin pay-ins and pay-outs across key corridors, including USD payments, without requiring customers to hold crypto on their balance sheets. Third-Party and Treasury Payments: offer flexibility to customers, enabling them to manage multiple payment types including third-party payments (on behalf of their customers) as well as internal treasury flows, seamlessly through a single platform. Premium Digital Asset Liquidity: support payments across a variety of digital assets like RLUSD, XRP and others, and provide competitive pricing on high-value tickets. Virtual Account and Collections: enable customers to transact with digital assets without the need to open dedicated crypto bank accounts or wallets on centralized exchanges, lowering barriers to entry and removing operational hurdles. Simple Integration and Always-On Infrastructure: connect customers to a global payment network that operates 24/7/365 through a single API for streamlined onboarding. Enterprise-Grade Compliance and Licensed: deliver regulated, secure payment flows with 60+ licenses that meet the highest standards for financial institutions. Banking Partner Network: offer a new level of built-in redundancy and reliability through the collective multi-bank partner network, giving clients resilient global coverage.
Broadridge’s integration of Uptiq’s tech into its wealth management platform provides advisors access to agentic AI apps that surface the most relevant loan options, automating securities-based lending- (SBL) workflows, referral submission and covenant tracking
Broadridge Financial Solutions has announced a strategic partnership and minority investment in Uptiq, an AI platform for financial services. The partnership aims to modernize wealth management by addressing the growing demand for artificial intelligence in financial services and developing a better wealth lending process. Uptiq’s AI-powered tools and Broadridge’s Wealth Lending Network will enable advisors to deliver smarter lending recommendations, save time, and help clients access the liquidity needed to achieve their financial goals. The integration will streamline the process of accessing securities-based lending solutions, particularly for financial advisors and wealth management firms not affiliated with banks. Broadridge’s investment supports Uptiq’s growth and reinforces a shared vision for transforming wealth lending.
New ‘persona vectors’ from Anthropic helps to identify, monitor and control character traits in LLMs before the models can develop undesirable personalities (e.g., becoming malicious, excessively agreeable, or prone to making things up)
A new study from the Anthropic Fellows Program reveals a technique to identify, monitor and control character traits in large language models (LLMs). The findings show that models can develop undesirable personalities (e.g., becoming malicious, excessively agreeable, or prone to making things up) either in response to user prompts or as an unintended consequence of training. The researchers introduce “persona vectors,” which are directions in a model’s internal activation space that correspond to specific personality traits, providing a toolkit for developers to manage the behavior of their AI assistants better. In a series of experiments with open models, such as Qwen 2.5-7B-Instruct and Llama-3.1-8B-Instruct, the researchers demonstrated several practical applications for persona vectors. A key application for enterprises is using persona vectors to screen data before fine-tuning. The researchers developed a metric called “projection difference,” which measures how much a given training dataset will push the model’s persona toward a particular trait. This metric is highly predictive of how the model’s behavior will shift after training, allowing developers to flag and filter problematic datasets before using them in training. For companies that fine-tune open-source models on proprietary or third-party data (including data generated by other models), persona vectors provide a direct way to monitor and mitigate the risk of inheriting hidden, undesirable traits. The ability to screen data proactively is a powerful tool for developers, enabling the identification of problematic samples that may not be immediately apparent as harmful. The research found that this technique can find issues that other methods miss, noting, “This suggests that the method surfaces problematic samples that may evade LLM-based detection.”
‘White-space’ opportunity for buying or originating fintech loans is estimated at US$280 billion over 5 years; banks are partnering private-credit funds to originate and distribute loans off balance sheet expanding fee-based revenue while offloading credit risk
Fintech founders are facing a significant challenge as giant private-credit funds are lining up with term sheets so large they would have broken cap tables a year ago. A joint study from BCG and QED Investors puts the “white-space” opportunity at US$280 billion over the next five years: capital earmarked for buying or originating fintech loans. Private credit has grown nearly ten-fold since 2010 to roughly US$1.5 trillion of assets under management (AUM) in 2024, and consultants expect it to hit US$3.5 trillion by 2028, a compound annual growth rate north of 19%. Big banks are partnering with private-credit titans to originate and distribute loans off balance sheet, such as Citi x Apollo and Citi x Carlyle. The model is simple: banks keep the origination and servicing fees, funds take the credit risk, and regulators get comfort that risky assets live outside the deposit-backed system. Private-credit exuberance has pushed unitranche pricing down, but funds can lever those assets 1.5-2x and still net solid returns, making it an attractive asset class. However, many CEOs prefer a 14 percent cost of capital that preserves ownership over a 35% down round in an unforgiving venture market. The venture capital power-law reset threatens that maths, as private-credit recycling threatens that maths. Funds are aggressively scouring growth markets where banking pull-back is most acute, such as India, Southeast Asia, and Latin America, where dollar funding married to local-currency wallets is a tantalizing carry trade, provided FX hedges hold. However, there are several risks to consider, including credit deterioration, funding squeeze, regulatory shock, and FX blow-ups in emerging markets. To mitigate these risks, investors should monitor the spread compression Pace, Reg-Tech Build-Out, Private-Fund Reporting, Basel Endgame Final Rule, and Structured-Credit Revival.