AI-driven solutions for credit-to-cash powered by Dun & Bradstreet’s global data and analytics. D&B Finance Analytics offers AI-driven solutions backed by the Dun & Bradstreet Data Cloud. D&B Finance Analytics is a flexible, easy-to-use tool that helps finance teams reduce costs, improve customer service, and manage risk. Manage credit and receivables risks to minimize bad debts, reduce DSO and improve cash flow. Automate manual decisioning and monitoring, customer communication, and matching. Offer your customers an online credit application as well as a payment portal. D&B Finance Analytics consists of two platforms: D&B Credit Intelligence and D&B®, Receivables Intelligence. Together, they provide powerful insights and technologies to help you accelerate your success throughout all your credit-to cash processes. You can quickly gain visibility into credit risks, onboard customers, and set the right terms.
Learn more

D&B Credit Insights is a comprehensive credit monitoring solution tailored for small business owners who want full visibility and control over their business credit profile. It offers unlimited access to your Dun & Bradstreet credit file, including essential scores like PAYDEX®, Delinquency, Failure Score, and Supplier Evaluation Risk. Real-time alerts notify you instantly of any changes or legal events such as lawsuits, liens, or judgments that might impact your credit. The platform also enables you to benchmark your credit performance against competitors to set achievable goals and improve your financial health. Additional features include detailed payment history, financial statement comparisons, and integration with your business bank account for seamless updates. The higher-tier plans provide dark web monitoring and allow you to compare your credit alongside other companies. D&B Credit Insights helps you proactively manage your credit profile and make smarter business decisions. With a clear view of your credit data, you can boost trust with lenders, investors, and suppliers.
Learn more
CreditorWatch
CreditorWatch is an online platform designed for commercial credit risk management and receivables for businesses. It integrates various functions including customer onboarding, credit application processes, business credit reporting, risk assessment, continuous monitoring with notifications, and features for handling overdue accounts and collections. This platform serves finance, credit, and accounts receivable teams, allowing them to evaluate both customers and suppliers, automate decision-making for applications, monitor fluctuations in business risk, and streamline receivables management. The available modules and integrations depend on the specific subscription plan chosen by the user. Each plan offers unique features tailored to meet diverse business needs and enhance operational efficiency.
Learn more
Naturepay
Cannabis retailers should offer the same payment methods as any other legal business to foster trust and credibility within the medical and recreational marijuana sectors. By embracing credit card payments, these businesses can gain validation and promote wider acceptance in society. This shift towards mainstream payment options plays a crucial role in enhancing public perception of the cannabis industry. Our primary focus has been to facilitate merchant accounts specifically for marijuana dispensaries and related cannabis enterprises. Recognizing the demand, we combined our expertise to create an effective and legitimate credit card processing solution tailored for the cannabis market. Our proprietary system has been developed through valuable partnerships and collaborations within the credit card processing arena. Thanks to our innovative approach, any dispensary or cannabis-related business can now seamlessly accept payments via Visa, MasterCard, Discover, American Express, debit, and checks, thus broadening their customer base and improving their operational efficiency. This comprehensive payment solution not only simplifies transactions but also signals a significant step towards industry normalization.
Learn more