With the rise of speech-to-text transcription and advanced interaction analytics, more collections firms are extracting data from their calls that can be put to business use.

Calls are filled with millions of data points that can be fed to analytics systems, artificial intelligence and machine learning, which can distill information on key performance metrics, successful recovery language, potential compliance violations and so much more that can lead to improved revenue recovery.

Without an understanding of current performance, it’s impossible to ask agents to improve. By recording and analyzing 100% of calls with an interaction analytics platform, particularly one powered by AI, contact centers can determine the overall performance of their program and establish baseline metrics for success for collections. Providing granular metrics and monitoring performance against them helps agents understand where they are lacking and where they are making progress.

As agents work to improve against their performance metrics, interaction data can provide guidance on the best approach to take for successful recovery.

Monitoring all contact center interactions and their outcomes can pinpoint the specific language that successful agents are using and how they are framing their conversations as they make a payment ask and overcome objections from debtors. This information can be used in training guidelines and on an ongoing basis to coach collectors that need improvement.

Machine learning can also analyze the successful collection calls to produce predictive models and scoring algorithms that can predict the likelihood a debtor is going to pay. Debt collection agencies are using predictive models such as this to prioritize calling lists and are substantially increasing revenue.

Equally important to improving successful collection rates, decreasing compliance violations can keep bottom-line costs low by avoiding the risk of fines or lawsuits. Advanced interaction analytics platforms and AI can automatically tag language that could be a potential compliance violation or mentions of a lawsuit or regulatory body.

Monitoring these mentions provides the opportunity to intervene with targeted coaching before the behavior becomes a systemic issue across the full agent population.

Collections will never be easy, but the availability of so much data and the means to make meaningful sense out of it with AI and advanced analytics is helping collections firms be more efficient and smart about their interactions.