If you’re a firm looking to transition itself into a fully intelligent enterprise, you might have a general idea of what your end-state could look like, but no idea how to get there. We’ve outlined a general process to take you through a machine learning and AI integration for an accounts receivable firm, starting with your back office all the way through to consumer-facing tools.
Step 1: Acquire new accounts and upload your firm’s historical data
What the tech does: Pairity’s machine learning begins as soon as it has your new portfolio and historical data to analyze. From there, a dynamic scoring model is created so you can start working new accounts immediately. Pairity is also able to identify more collectable accounts than a standard process could.
A targeted approach is created across account types and is continually updated and optimized.
What you get: A proven model to set your strategy, plus less time spent on labor and costs on static data.
Step 2: Fully integrate all your data to the Pairity platform
What the tech does: Based on the machine learning models, Pairity is able to pair consumers with the ideal agent in your firm or their preferred means of communication. The machine learning is updated with every contact and interaction in real time to keep refining approach and strategy.
These models are then applied to all new accounts that are uploaded to your system, taking into account every available data point (not just static scores and history) and the refinement continues.
What you get: Prioritized workflows, better agent/consumer matches, more collectable accounts identified, and a proven strategy for all new accounts moving forward.
Step 3: Optimize your consumer experience
What the tech does: Now that we know your consumer and what they want, we can give them the digital tools to settle their debts fairly, make regular online payments, and communicate in the mode they’re most likely to engage with over a longer period of time.
What you get: A happy consumer who is more likely to settle and repay the full amount.
Step 4: Explain your new tech to regulators
What the tech does: Pairity translates and synthesizes its complex models and results, and documents them in clear, easy-to-understand language for compliance. Call scripts are monitored by AI, analyzed using Natural Language Processing, and used to build out comprehensive reference materials. Disposition numbers are also recorded and maintained.
What you get: These clear records of applied models are not only helpful for regulatory review, they also help your firm define strategy internally.
Learn more about Pairity’s Machine Learning as a Service and different levels of integration here.