Generative AI Is More Powerful Than We Realize

With the recent launch of OpenAI’s “omni” model, we are now deep in the
era of Generative AI. The capabilities of artificial intelligence have been
greatly expanded, with large language models performing a variety of complex
tasks such as code writing, art creation, quality assurance, and even fraud detection.
As society adapts to these changes, the collections industry must also embrace
Generative AI and explore its vast potential to solve common challenges.

Technological booms have always been met with cautious skepticism. From
the introduction of steam engines to the invention of the telephone, society
has often welcomed innovation with a degree of reservation. However, if history
teaches us anything, it is that early adopters, innovators, and pioneers also
approach technological innovations with courage. Artificial intelligence is
here to stay—and it’s here to change the world. The question is, which problems
are we trying to solve, why, how, and when?

In the accounts receivables industry, we have seen the emergence of
several early adopters among creditors, lenders, and collection agencies. AI
tools are already in use to monitor compliance, automate consumer
conversations, analyze credit risks, and build financial models. With time, we
are seeing the ROI of these tools increase. How should collection leaders
approach this critical phase?

Achieving Collection Performance Beyond Human Capabilities

Banks and fintech companies are progressively investing in technology
to gain a competitive edge. Augmenting manpower with AI has improved staff
efficiency and productivity while driving down costs; concurrently, better
consumer engagement has led to topline growth.

In the Conversational AI space, we are automating consumer
conversations across all channels—voice, text, chat, and email. Commonplace
tasks such as consumer verification, payment negotiation, and payment
processing can now be automated in both spoken and written interactions,
enabling live agents to focus on complex queries requiring additional research,
expertise, and skills. This change is helping creditors and collection agencies
improve their collection rates and agent productivity and reduce the cost of
collections.

This is not aspirational thinking; what I’m describing is taking place
right now. My prediction is that Conversational AI will automate 90% of
consumer interactions within the next two years.

Why LLMs Represent a Transformative Solution for Financial
Services Organizations

Large language models are fed with immensely large amounts of public
and proprietary data and continue learning from ongoing interactions and new
data inputs.

Collection agencies’ customer relationship management tools (CRMs)
contain roughly two decades of consumer interactions, consumer profiles,
transactions, and agent notes. This data goldmine can help us better understand
the consumer journey, behavior, and payment propensity.

Today, financial services organizations rely on credit scores to
evaluate consumers’ creditworthiness. However, credit scores have two main
blind spots:

1. The evaluation models are based on limited
   credit lending history and are therefore prone to bias against specific
   consumer segments with limited credit histories.
2. The models do not contain granular data on
   consumer behavior for effective engagement (e.g. the best time to contact
   the consumer).

Large language models, when trained with consumer data and past agent
interactions, can bridge these gaps and curb biases, effectively helping
financial services organizations determine the best engagement and recovery
strategy at an individual level. We can refer to these LLMs as large collection models.

In the future, orchestration platforms for the debt
collection industry—collection
orchestration platforms—will become instrumental when trained with consumer
engagement data from creditors’, lenders’, and collection agencies’ CRMs. The
data will help the AI tools evaluate the consumers’ propensity to pay,
accelerating the recovery process and enabling collectors to allocate more
resources for the most complex segments. This strategy can help optimize
processes, maximize recovery rates, and cut collection costs.

Here are a few ways engagement strategy and consumer experience (CX)
can be optimized based on granular data and past engagements:

• Identify the consumer’s preferred communication channel (text, email, voicebot call, live agent call, print letter, etc.)
• Analyze the best day and time for engagement
• Determine the resources required for account resolution

Collection orchestration platforms are going to become more
effective with time, as more consumer engagement data generated by their use
enables them to self-train further, automating the channel, timing, and
strategy of the recovery effort.

How To Leverage New Tech Advancements Effectively and
Responsibly

As with any new technology, the adoption of Generative AI
into the debt collection industry requires a collective effort and commitment
to ethical standards and responsibility, especially with the utilization of
large amounts of consumer data.

Here are a few things to keep in mind to ensure a successful
implementation:

Compliance
guardrails: It’s crucial to prevent hallucinations,
recognize and eliminate biases, and ensure the technology is programmed to
adhere to all applicable industry regulations, including data privacy measures.

Data accuracy: Generative AI is only as good as the data it’s fed. Therefore, it’s
important to monitor data accuracy and live agent logs.

Seamless system
integrations: To obtain the best results, seamless
integration between the CRMs and orchestration platforms is required. Offline
integrations and data transfers pose a risk of data loss and privacy breaches.

In Conclusion: From “if” and “why” to “when” and “how”

Collection orchestration platforms are poised to shape the
future of the collections industry. Industry leaders should focus on these
technological advancements and look beyond the capabilities of current large
language models. Today, the adoption of Generative AI is no longer a matter of
“if” or “why” but rather “when” and “how.” The rapid pace of new
developments in LLMs necessitates this forward-thinking approach.

While today the focus is on LLMs, the future of collections
will center around large collection
models and collection orchestration
platforms. It’s time for the accounts receivables industry to begin a
collective effort to integrate these advanced technologies and redefine best
practices.

To
learn more– Speak With Our
Conversational AI Expert.