While it’s true that skip tracing has been a technique used in certain professions for many years, AI has only recently taken things to the next level.
Artificial intelligence has quickly proven essential in enhancing the skip-tracing process’s efficiency and the accuracy of the results. AI-powered algorithms can wade through enormous amounts of data incredibly quickly—far faster than humans could. These tools can arrive at valuable insights in a fraction of the time it would take a human to locate individuals who may not wish to be found as quickly as possible.
That’s a simplified explanation, of course, but it’s still a good indication of why artificial intelligence has taken the world of skip tracing by storm. There are several other ways AI impacts skip tracing that are undoubtedly worth exploring.
The Might of Machine Learning
One crucial part that artificial intelligence has to play in modern skip-tracing techniques involves machine learning. Many use the terms “artificial intelligence” and “machine learning” interchangeably. However, they are two separate concepts and should be treated as such.
Think about it like this. Artificial intelligence is a broad concept involving a machine that can act and even react like a human. Machine learning takes things one step further and allows a machine to “learn” from or make decisions based on the data it is being fed without being specifically programmed.
Take skip tracing in real estate, for example. The information skip tracers use comes from various sources. Not only are there skip-tracing databases to browse, but there are also public records, social media sites like Facebook, online directories, utility company records, postal records, and more.
Artificial intelligence can help you collect data from these sources and analyze it far faster than you would on your own. However, as skip-tracing tools continue to ingest that data, machine learning will analyze patterns and find trends humans may have missed. This becomes valuable insight that can be used to refine search strategies in the future.
To put it another way, if the skip tracing tool sees that the most valuable information comes from a particular source repeatedly, like social media, it will begin to “learn” from that and prioritize that data in the future. This can make an already effective search strategy even more efficient in the future.
The Sophistication of Natural Language Processing
Another majorly helpful role that artificial intelligence plays in skip tracing involves Natural Language Processing, otherwise known as NLP for short. NLP can help not only make sense of unstructured data sources but also make it easier to extract relevant information. It’s a technology that lets computer systems, like skip tracing tools, interpret and even “comprehend” human language.
The problem with the sheer volume of data we’re dealing with in the digital era is that most of it needs to be more structured. It’s challenging to sort through, which makes it even harder to manage and nearly impossible to organize. Take social media sites, for example. Billions of posts, none of which share a uniform formatting, go live daily. How do you tell what is valuable and what isn’t? How do you know what is relevant and what is just noise? It’s all unstructured, and it would be impossible for a human to wade through it all.
Natural Language Processing helps to interpret these unstructured data sources better, quickly separating the worthy from the worthless. The same would be true if a debt collector had to sort through countless public records, each from a different source, to find who they were looking for. If you know that 99.5% of the data you have access to isn’t helpful, and you have an opportunity to get rid of it quickly, the 0.5% that you can derive insight from rises to the top. Natural Language Processing becomes that opportunity.
AI-Driven Predictive Analytics
Finally, there is the concept of AI-driven predictive analytics. This involves using the past to better anticipate the future.
Once you’ve collected data from many different sources and weeded out all the noise, you’re left with essentially accurate, actionable, factual historical data. AI-driven predictive analytics can help use that historical data to forecast targets’ potential locations or behaviors.
If you were a law enforcement officer trying to locate a suspect evading a warrant, an AI-driven predictive analytics tool would examine precisely where the suspect has been to anticipate better where they might be going. Even if they can’t tell you precisely where someone will be at a given moment, they can help narrow things down significantly. They can at least put law enforcement closer than ever to finding the target, and certainly closer than they would likely be able to get through “old school” methodologies.
Skip Tracing is Changing with Technological Advancements
In the end, technology is changing rapidly, meaning that AI’s precise role in an activity like skip tracing today may change wildly in six months or even a year from now. But the core impact remains the same. Artificial intelligence empowers skip tracers everywhere, giving them access to advanced tools that didn’t exist even a decade ago.
AI allows them to navigate the inherent complexities of the skip-tracing process. They make it easier to locate those who don’t wish to be found by making it harder to indeed “disappear” to begin with. They help skip tracers achieve more successful outcomes, improving the quality of the work they can do for their clients. To put it all another way, they take an inherently complicated and time-consuming process and make it easier and faster to complete than ever before – which in and of itself is the most crucial role of all.
