Attunely Inc., a pioneer in deploying machine learning to maximize revenue recovery and enhance the consumer experience, announced $9 million in financing. The new capital includes an earlier seed round as well as a Series A investment. Investors include: Framework Venture Partners, Anthos Capital, Vulcan Capital, and additional independent investors as well. Andrew Lugsdin, Partner at Framework Venture Partners, will join the Attunely Board of Directors.
The funds will be used to expand the team with an emphasis on data science, data and security software engineers, client success resources, product/program management, and marketing.
“Attunely is uniquely positioned to help reinvent the consumer’s collection and lending experience, in view of the economic damage resulting from the COVID-19 pandemic. Its dynamic machine learning models provide an opportunity for improved engagements across the industry. Now, more than ever, businesses need tools to empower responsible engagement with consumers and improve business practices. I am excited to join the Attunely team and support their growth,” said Andrew Lugsdin, Partner, Framework Venture Partners.
“In the current economic climate, we believe machine learning is well suited to enable creditors, lenders and revenue cycle management agencies to employ more tailored engagements during and beyond the pandemic-driven unemployment environment. Undoubtedly, COVID-19, has created a ‘new normal’. Attunely’s machine learning models are designed to provide greater flexibility and efficiency during a transformative moment for the economy at large,” said Scott Ferris, Founder and CEO, Attunely.
Attunely Models Enhance Revenue Recovery
Powered by de-identified consumer interaction behavior, Attunely’s machine learning platform prescribes the most effective outreach tactics through every phase of the servicing and delinquency life cycle. Attunely’s models are customized to enhance revenue recovery, reduce expenses, prevent customer churn, and identify the best time of day to reach a consumer.
Adds Attunely CEO Scott Ferris: “We understand our clients are operating in a difficult environment and our dynamic machine learning models can ease the friction between a financial institution and consumer.”
Attunely’s dynamic scoring models include:
Propensity to Pay Model The Attunely propensity model estimates the likelihood of payment from each account, producing an overall probability score that changes dynamically in response to ongoing interactions between an organization and its consumers.
Liquidation Model The Attunely liquidation model combines behavioral signals with a client’s historical transaction data to produce an expected value when recovering revenue. It is informed by billions of historical calls, letters, emails, and text messages, and dynamically refines an account-level score based on each interaction with the consumer.
Time of Day Model Leveraging billions of de-identified historical call records, and combined with Attunely’s existing liquidation scoring model, it produces a dialer-ready call file that matches the highest-yielding accounts with their preferred time slots. This allows for more contacts using fewer call attempts, building on Attunely’s comprehensive suite of machine learning models.
Omnichannel Model The Attunely Omnichannel score ranks each communication channel and identifies the most productive form of outreach on every account. Combined with the liquidation score, this strikes the optimal balance between immediate recovery, maximizing long-term value (LTV) recovery, and giving the consumer time to repay.
Settlement Optimization Model Attunely’s Settlement models leverage de-identified historical collection data to estimate the likelihood, timing, and expected recovery value of contingent or debt portfolios. This enables recovery experts to calculate the best offer, whether that is a settlement, payment plan, or paid-in-full demand for every account.
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