Attunely: Helping Companies Leverage Machine Learning for Delinquency and Account Servicing

Traditionally, financial institutions have relied on outdated collection processes such as payment reminders and repetitive, random calls from multiple collection agencies in their attempts to recover revenue from consumers or turn around delinquent accounts. According to a study by IBIS World, the debt collection industry in the United States recovers approximately $80 billion in revenue annually. This represents a small fraction of the roughly $420 billion in outstanding debt that is charged off by financial institutions and other creditors. A significant number of already delinquent accounts continue to further age even after they get to collection agencies, which are often unable to reach the debtor or evaluate their behavior effectively to identify consumers with the wherewithal to reconcile their outstanding debt. Also, some consumers view these methods as, at times, intrusive and often create a negative impact by exposing financial institutions to poor net promoter scores and consumer churn. Today’s technology enables collection strategies to shift from predominantly call and letter methods to a more tailored approach based on de-identified historical behavioral information. Using machine learning allows creditors and outsourced collection agencies to approach consumers as individuals, accurately predicting which will respond best to automated outbound dialers, reminders, or personalized repayment options.

Attunely offers a cloud-based machine learning platform that can bridge this gap, enabling a seamless and profitable recovery process of receivables. The company's solution leverages de-identified consumer data from the client’s database to achieve higher margins, improved resource management, improved brand equity, and enhanced compliance.

"Today, loan servicing and collection operations are using traditional credit scores to qualify consumers’ state of financial health, potential default, or delinquency. But, in terms of the accuracy of the data, it is a static picture of the consumers creditworthiness and doesn’t account for the dynamic view of the consumer’s ability to repay a debt," explains Attunely Founder & CEO Scott Ferris. Powered by a client's de-identified, historical consumer behavioral data, Attunely’s custom machine learning models enable servicing or collections teams to determine the most effective outreach strategies through every phase of the servicing, delinquency, and recovery cycle. This helps financial services and receivables management teams increase their yield in the collection process, secure a higher return for creditors, lower risk in the credit system, and orchestrate an enhanced consumer experience. "What we are introducing is a new form of behavioral signals drawn from de-identified data that is used to mitigate delinquency and increase revenue recovery, and that is individually tailored to each account," Ferris further explains.

Whether companies seek to maximize recoveries, reduce operational expenses, or prevent consumer default and churn, Attunely's 'Dynamic Scoring Models' help companies optimize every consumer interaction.