Attunely

Attunely operates within the accounts receivable management industry, offering a machine learning platform designed to optimize debt collection. The company was founded in 2018 by Scott Ferris, who serves as CEO, alongside Ryan Kosai as CTO and Trip Edwards. The founding team combines experience in enterprise software and the collections industry. Ferris, for instance, is a former executive from Starbucks and aQuantive. The startup was spun out of the Seattle-based startup studio, Pioneer Square Labs.

The firm's core business revolves around providing a software-as-a-service (SaaS) platform to third-party collection agencies, debt buyers, and collection law firms. Instead of acting as a collection agency itself, Attunely partners with existing agencies, providing them with tools to enhance their efficiency. Its business model is centered on empowering these clients to improve recovery rates and operating margins by making their outreach more effective. The company has raised a total of $13.1 million over two funding rounds, including a $3.7 million seed round in 2019 and a $9.4 million Series A round in 2020, with investors such as Framework Venture Partners, Anthos Capital, and Vulcan Capital.

Attunely's platform ingests and analyzes large datasets, including debt records, consumer interaction histories, and macroeconomic trends, to generate predictive scores. These scores help agencies identify which consumers are most likely to pay and prescribe the most effective communication strategies, including the best time of day and channel for outreach. The machine learning models are trained on over 100 million historical consumer interactions and do not require personally identifiable information to function. Key features include a propensity to pay model, a liquidation model that estimates the expected value of an account, and a settlement optimization model. The platform is designed to integrate with clients' existing IT infrastructure and also provides reporting and analytics tools for real-time performance monitoring. A significant benefit is the reduction of ineffective outreach, which helps clients allocate call center resources more efficiently and remain compliant with regulations.

Keywords: receivables management, debt collection software, machine learning, revenue recovery, fintech, account scoring, collection strategies, propensity to pay model, liquidation model, compliance tools, SaaS, accounts receivable, ARM industry, credit ecosystem, consumer interaction behavior, settlement optimization, predictive analytics, financial services, behavioral modeling, risk management