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goeasy Ltd. announces appointment of Mark Snyder as Chief Credit Risk & Data Officer. Aug 17, 2026, 08:00 ET Experienced Canadian Financial Services Executive Brings Deep Expertise in Credit Risk, Analytics, Fraud Mitigation and Data Science MISSISSAUGA, ON, Aug. 17, 2026 /CNW/ - goeasy Ltd. (TSX: GSY) ("goeasy" or the "Company"), one of Canada's leading consumer lenders focused on delivering a full suite of financial services to Canadians with non-prime credit scores, today announced the appointment of Mark Snyder as Chief Credit Risk & Data Officer, effective September 8, 2026. "Mark is a highly respected leader with deep expertise in credit risk, analytics and data science," said Patrick Ens, Chief Executive Officer of goeasy. "His consumer credit experience and proven ability to lead integrated credit risk and data teams will further enhance our data-driven, analytical decision-making capabilities as we continue to strengthen our foundation for sustainable, profitable growth." Mr. Snyder brings more than 20 years of experience leading credit risk, fraud mitigation, analytics and data science within the Canadian financial services industry. Most recently, he has served as Senior Vice President, Credit Risk & Data Analytics at President's Choice Financial, now part of EQ Bank, where his mandate includes credit and fraud risk, loss forecasting, and data and analytics. Prior to joining President's Choice Financial, Mr. Snyder spent more than 20 years with Capital One, holding progressively senior credit leadership roles in the United States and Canada, including Divisional Credit Officer and Senior Credit Officer. At Capital One he was responsible for credit and fraud risk programs, portfolio performance, loss forecasting, model governance and significant credit exposure decisions. He also led the development and adoption of AI-driven decision models covering underwriting, customer management and fraud programs in sub-prime and near-prime segments. As Chief Credit Risk & Data Officer at goeasy, Mr. Snyder will lead the Company's credit risk, analytics, business intelligence, data governance, emerging technologies and enterprise data science functions. Bringing Risk and Data together under one leader is expected to further strengthen goeasy's risk management, analytics and decision-making capabilities while supporting disciplined growth, stronger organizational performance and better customer experiences. About goeasy goeasy Ltd. is a leading Canadian provider of non-prime consumer lending solutions, offering a suite of financial products through its easyfinancial, easyhome, and LendCare brands. goeasy offers unsecured and secured instalment loans, point-of-sale financing, and lease-to-own merchandise through its omni-channel model, which spans online, mobile, and hundreds of locations nationwide. Driven by its team members' dedication to expand access to credit for underserved communities and helping customers strengthen their financial futures, goeasy has proudly served more than 1.6 million customers while building an award-winning culture. Shares of goeasy Ltd. are listed on the Toronto Stock Exchange (TSX) under the symbol GSY. For more information, visit www.goeasy.com. For investor inquiries, contact: James Obright Senior Vice President, Investor Relations & Capital Markets [email protected] Forward-Looking Information This news release may contain forward-looking information within the meaning of applicable Canadian securities laws, including statements regarding the expected impact of the executive appointment and the Company's business, risk management, analytics and performance. Forward-looking information is based on current expectations and is subject to risks and uncertainties that could cause actual results to differ materially. Readers are referred to the Company's filings with the Canadian securities regulators, including the Company's Management's Discussion and Analysis for the year ended December 31, 2025 and the quarter ended June 30, 2026, each of which is available on SEDAR+ at www.sedarplus.ca. The risks and uncertainties described in the Company's filings are not the only ones that could affect the Company. Additional risks and uncertainties not currently known to management or that may currently not be considered material by management, could nevertheless also have an adverse effect on the Company's business. Readers are cautioned not to place undue reliance on forward-looking information, which speaks only as of the date of this news release. The Company undertakes no obligation to update such information except as required by law. SOURCE goeasy Ltd.

Capital One enhances banking efficiency with multi-agent AI platform. At VB Transform 2026, Capital One's Kel Vanee showcased how the bank is leveraging customized open-weight models to build a transformative AI platform, emphasizing the unique advantages of proprietary data in banking. Key facts. * Capital One's use of proprietary data in AI models enhances competitive differentiation in banking. * Customization of open-weight models yields enterprise-wide benefits, optimizing resource allocation. * Multi-agent architecture improves operational efficiency, reducing customer service handling time significantly. * Proactive AI systems could lower fraud risk, indicating a strategic shift towards real-time responsiveness. * Investment in AI infrastructure reflects long-term financial strategy, driving cost efficiency and innovation. Summary. Capital One faced the challenge of enhancing customer service efficiency and accuracy in handling complex fraud calls. To address this, the bank developed a multi-agent AI architecture that customizes open-weight models with proprietary data, resulting in improved service delivery and operational effectiveness. Background. Capital One is a major player in the financial services industry, known for its innovative approach to banking. Before deploying its AI solutions, the bank had already invested significantly in data transformation and cloud infrastructure, laying a strong foundation for rapid AI adoption. Challenge. The primary challenge was to improve the handling of millions of customer calls related to bank fraud, where interactions could last from four to sixty minutes. Initial attempts using a single large language model proved inadequate, prompting the need for a more sophisticated solution. Solution. Capital One implemented a multi-agentic workflow (MACAW) that routes customer interactions through specialized agents. This system includes an understanding agent to interpret customer intent, a reasoning agent to generate summaries, a validation agent to fact-check, and an explaining agent to format and share the results. Additionally, the bank customized open-weight models, such as Meta's Llama, using its proprietary data to enhance the performance of customer-facing applications like Chat Concierge. Results. The deployment of the multi-agent AI architecture led to significant improvements in customer service efficiency. The post-call summaries generated by the system eliminated the need for agents to reconstruct lengthy interactions manually, streamlining the process and enhancing accuracy. Key insights. A successful AI deployment requires a tailored approach that leverages proprietary data and a structured architecture. Capital One's experience illustrates the importance of building a governance framework around AI systems to ensure quality and compliance while driving innovation. Customer testimonial. "All of those ingredients were absolutely critical to differentiating in this space and hitting the quality bars as well as the cost and latency thresholds we set for ourselves," said Kel Vanee, MVP of machine learning engineering at Capital One. Entities mentioned. VentureBeat Chat Concierge Technologies multi-agent AI open-weight models large language models (LLMs) Sam Witteveen Key concepts. multi-agent AI architecture open-weight models data transformation cloud adoption customer service workflow proactive AI event-driven AI enterprise platform approach Definitions. * multi-agent AI - A system architecture that utilizes multiple specialized agents to perform tasks collaboratively, improving efficiency and accuracy. * open-weight models - AI models that are customizable and can be fine-tuned with proprietary data to enhance performance for specific use cases. * proactive AI - AI systems that act autonomously based on detected conditions, rather than waiting for human prompts. * event-driven AI - AI that responds to specific events or triggers in real-time to perform actions or make decisions. * large language models (LLMs) - Advanced AI models designed to understand and generate human language, often requiring significant computational resources. Use cases. * | customer service for bank fraud * | auto-shopping assistant * | automating rote tasks for employees * | optimizing backend hosting infrastructure * | monitoring fraud detection * | enhancing customer interactions Frequently asked questions. What is Capital One's approach to AI? Capital One focuses on building customized AI solutions rather than relying on off-the-shelf models. This involves using proprietary data to fine-tune open-weight models for specific applications. How does the multi-agent AI architecture work? The multi-agent AI architecture consists of specialized agents that handle different aspects of a task, such as understanding customer intent, generating summaries, and validating information, ensuring a more efficient workflow. What are the benefits of using open-weight models? Open-weight models allow for deep customization with proprietary data, enabling organizations to leverage unique insights that off-the-shelf models cannot provide, thus enhancing performance and relevance. What is proactive AI and why is it important? Proactive AI refers to systems that can act on their own when certain conditions are met, which is crucial for timely responses in scenarios like fraud detection, ultimately improving customer safety and experience. How does Capital One ensure the quality of its AI solutions? Capital One emphasizes a highly governed architecture that combines fine-tuned models with a multi-agent orchestration harness, ensuring that technical breakthroughs translate into measurable value and improved customer experiences.