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AI in Receivables: Understanding the Background, Data, Computing Power and Application

AI in Receivables: Understanding the Background, Data, Computing Power and Application

At the ACA Annual Convention & Expo in Nashville this year, attendees and presenters were frequently heard discussing how artificial intelligence (AI) would apply to the ARM industry. Even though AI concepts have been around since just after World War II, the technology has historically been locked into the halls of learning or well-funded labs, and only in the last decade has it burst into practical application. The emergence of widespread enabling technology for massive amounts of data and the ability to manipulate that data in reasonable time frames fuels the practical use of AI to solve real world problems. Since the turn of the century, we have generated and then absorbed massive amounts of data through cheap, ubiquitous sensors (think “the Internet of Things”). We have also been able to store that data online, cheaply. The presence and continuous arrival of data invites us to act on it. For example, massive amounts of video data give rise to advances in facial recognition. Similarly, audio data provides the foundation for understanding conversations. Two particular concepts exploding today are machine learning and a related concept called deep learning, both considered subdomains of AI. While the applications have developed recently, their original concepts have existed since the 1950s, and advanced further in the 80s with the development of neural networks.

Handling Data

A hallmark of machine learning is the massive amounts of data curated when it is exercised. Managing this data requires significant responsibility to safeguard the consumer’s privacy and other rights, whether their data is simply stored or used to make decisions, automated or not. Any initiative to harness data for machine learning must include methods for identifying and protecting sensitive data within large datasets, both structured and unstructured. Our industry is already aware and conforming with the requirements of HIPAA and PCI, but it’s important to remember that when business decisions are made that affect consumers, meaningful information about those decisions must be provided, care must be taken to treat protected classes equally (ECOA), and EU citizens must be provided with a way to opt out and information into the decision process. The power and scale of AI’s capabilities should not be taken lightly as these factors are brought under consideration.

Compute Power

Machine learning, especially deep learning, runs well on hardware using GPU chips for massive parallel instruction processing and extremely fast memory. As a result, GPU chip demand has skyrocketed and the major cloud services suppliers have purchased a large percentage of available GPUs. And while AI platforms can be installed onsite, businesses who build this within their own data centers are generally creating AI technology in house. There’s good news though: Companies provide solutions that deliver intelligence or intelligent functions commercially. These companies draw the data they use to train and execute machine learning models into their data centers or to their virtual private cloud (VPC) in a commercial or FEDRAMP cloud platform. There are a few vendors who reluctantly allow the customers’ data to stay on-premise for an additional charge if the customer purchases their own hardware. Because latency matters with today’s expectations of the technology, AI at the Edge is the next frontier, focusing on processing AI functions on edge devices: smartphones, wearables, cars, traffic lights, factory machines. In other words, AI will be embedded in the Internet of Things (IoT).

Selecting Tools for the Purpose

Artificial intelligence replaces or augments human presence in business processes, allowing us to pull redundant and tedious tasks out of human hands. Machine learning and deep learning applications make those tasks done by the system intelligent, learning with more experience and data, without rules written by humans. Here’s where we depart from earlier automation of business processes in receivables. For our industry’s entire history, we have relied on those long-tenured or deeply-experienced managers to translate their smarts into dialer and correspondence strategies, what to say on the phone, how to respond to denials, and other processes. Machine learning promises to create good outcomes by reviewing all of what has gone before and charting the path to successful actions. Replacing a human with a computer requires the computer to be fast enough to make the process work. Some use case results can wait a day, or can be allowed to take minutes or seconds, while others must feel instantaneous.

Takeaways

Artificial intelligence is an exciting opportunity for our industry to improve results while keeping a sharp eye on margins. With all the hype, it’s important we keep our heads, understand the context and uses for artificial intelligence in its various forms, and continue to monitor the regulations that will govern behavior influenced by automation. As with several of our delivered solutions, Ontario Systems will seek good technology and partners to provide what they do best, embedded in our best-in-class workflow and integrated contact management. Each time, we will examine the business case to ensure the solution delivers value and enables you to take advantage of opportunities or solve problems in your real world.

For more information from Amy on this topic, listen to her ARM Perspectives podcast, What is AI? Artificial Intelligence in the Collections Industry.

Disclaimer: Ontario Systems is a technology company and provides this blog article solely for general informational and marketing purposes. You should not rely on the content of this material for any other purpose or as specific guidance for your company. Ontario Systems’ advice, services, tools and products described herein do not guarantee compliance with any law or industry standard. You are ultimately responsible for your own company’s actions and compliance efforts. Because everyone’s situation is different, you must consult your own attorneys, accountants, and/or other advisors to obtain specific advice on your company’s compliance, legal, tax, regulatory and/or other business needs. Despite Ontario Systems’ efforts to provide current and up-to-date information, you need to recognize that the information contained herein may become outdated quickly and may contain errors and/or other inaccuracies.

© 2018 Ontario Systems, LLC. All rights reserved. Information contained in this document is subject to change. Reproduction of this publication is not permitted without the express permission of Ontario Systems, LLC.

The Time is Ripe for Your Move to a First-Party Business Model

 

Lately, many agency executives have created new opportunities for their businesses by offering to work in the name of their creditor clients under a first-party model. That’s because since 2012, the ARM and healthcare revenue cycle industries have put a tight focus on vendor and service provider management. The CFPB expects both supervised banks and non-banks to have an effective process for managing the risks of service provider relationships – Essentially holding creditors liable for collector behavior.

Litigators have been successful in supporting this accountability in and out of the courtroom. Creditors have responded by reducing the number of collection agencies they leverage, preferring larger-market participants that are already acclimated to the CFPB’s supervision and scrutiny.

This scrutiny is why creditors and providers have been auditing their remaining collection agencies’ activities more thoroughly than ever before. The CFPB uses UDAAP to reach into the creditor space, and creates consent decrees on behavior that is unfair, deceptive, or abusive, saying:

“Although the Fair Debt Collection Practices Act generally applies only to third-party collectors, all collectors subject to the CFPB’s jurisdiction can be held accountable for any unfair, deceptive, or abusive practices in collecting a consumer’s debts.”

The CFPB has made it clear that it dislikes contingent collections, claiming the arrangement generally rewards the wrong behaviors. We see this opinion on display in the Dodd-Frank Wall Street Reform Act, specifically in section 956, which targets incentive pay. In this particular instance, the restriction applies largely to executives, but reflects a growing antipathy for the model. Many creditors have noted the writing on the wall, and have responded by embracing a per-account or per-FTE type of compensation in “first-party” work, which directs more attention  to complaints, call quality and other non-recovery factors.

So logically, many in the industry are asking ‘what’s next?’ Initially, it appeared the CFPB was on a fast-track to propose and publish rules for both covered and non-covered entities under the FDCPA, having announced a separate proceeding to address people and organizations performing collection activity who are not “covered persons” under the FDCPA. Now, with the recent election, it’s important to watch the CFPB’s next steps. Will they press ahead quickly as it appeared they would before the election? Or will those results slow them and any rule-making down?

If the current indicators come to pass, creditors will be required to provide information to agencies that more provably substantiate debts, and delineate the charges that may have arisen during collection efforts. If this all feels like a tough challenge to meet, you’re not alone.

Many agencies in the industry are taking steps even now to meet these new demands by modifying their service offerings to include:

  1. Creating an exclusive team of customer service reps for each particular creditor serviced
  2. Revamping workflows and contract execution to ensure staff works in the name of the creditor, including all inbound and outbound calls and letters
  3. Adopting creditor merchant accounts for transactions, or requiring direct payment to the creditor directly
  4. Using a compensation model that avoids contingency fees in favor of a flat fee for service model
  5. Working to bolster compliance with creditor-required collection practices, including (but not limited to) communication frequency, and whether they wish to leave voicemail and/or record conversations with consumers
  6. Developing mature complaint and incident response processes
  7. Working with creditor clients to provide customer service handling according to their specifications
  8. Credit reporting in the name of the creditor only if or as directed by the creditor

As government regulation and creditor demands continue to tighten, this path to first-party collections may become a popular business model. Now is the time to review your own client relationships and decide if you should offer it.

 

Disclaimer: Ontario Systems is a technology company and provides this blog article solely for general informational and marketing purposes. You should not rely on the content of this material for any other purpose or as specific guidance for your company. Ontario Systems’ advice, services, tools and products described herein do not guarantee compliance with any law or industry standard. You are ultimately responsible for your own company’s actions and compliance efforts. Because everyone’s situation is different, you must consult your own attorneys, accountants, and/or other advisors to obtain specific advice on your company’s compliance, legal, tax, regulatory and/or other business needs. Despite Ontario Systems’ efforts to provide current and up-to-date information, you need to recognize that the information contained herein may become outdated quickly and may contain errors and/or other inaccuracies. 

© 2016 Ontario Systems, LLC. All rights reserved. Information contained in this document is subject to change. Reproduction of this publication is not permitted without the express permission of Ontario Systems, LLC.