Real estate professionals can usually expect a slow down in the fall, but the usual doesn’t apply since the pandemic. Sales in some markets are surging in anticipation of a mortgage rate hike. As volume remains steady for real estate and title professionals, many are turning to new technology to help them manage their operations.
You’ve likely heard about artificial intelligence and chatbots, but there’s another aspect to this growing technology that’s not talked about as frequently: Natural Language Processing.
What is Natural Language Processing?
Natural language processing (NLP) is when linguistics, computer science, and artificial intelligence collide. The primary goal of this discipline is to analyze large amounts of natural language data to understand the context of what is said. NLP allows Siri, Alexa, and Google to piece together spoken language like legos, analyze the intent, and provide a relevant search result.
Computer programming relies on strict coding languages where syntax errors will result in failure, but humans can communicate effortlessly with each other even when our grammar isn’t perfect. That’s why Siri will have trouble parsing more complex questions.
One early chatbot called ELIZA was developed at MIT to take on the role of a therapist. The creators use a rule-based pattern-matching technique, mapping hundreds of scenarios of what a user might say and how the program should reply. The results sometimes gave the illusion of understanding while at other times, led to humorous mistakes that exposed the lack of sophistication.
Machine Translation or MT uses software to provide translations of text.
One of the first experiments demonstrating this kind of machine translation was performed in 1954. Georgetown University and IBM successfully completed the translation of more than sixty Russian sentences into English. One scholar and supporter of machine translation who was invited to the project was Leon Dostert, who invented the simultaneous headset method for interpretations used during the Nuremberg War Crime Tribunals and still today at international gatherings.
The initial success of the Georgetown-IBM experiment garnered attention and funding from governments. Progression, however, was slower than anticipated, and after ten years with less than thrilling results, funding dried up.
Today, depending on how advanced the algorithm used to analyze language is, some machine translations can analyze and clarify nuanced concepts similar to that of a human interpreter. More private companies and learning institutions recognize the value of eliminating language barriers for commerce and research.
While you’re likely to be somewhat familiar with the latest improvements in NLP like autocorrection, translation, and chatbots, you may wonder if any of this can help the title industry.
Here are some examples of relevant NLP use cases for you.
How will AI and NLP help title and escrow?
Given the volume of title orders and the challenge of recruiting and training new title professionals, AI and NLP provide an opportunity to automate and simplify manual tasks. In a recent interview with Hoyt Mann, he talks about how a virtual assistant like Alanna can give the breathing room needed to tackle the more complex title work and leave the rest to the machines.
But chatbots and translation are only the beginning.
1. Customer Service
Coordinating a closing isn’t easy. It requires constant communication between multiple parties. Depending on the size of your company, you may be processing files, clearing title defects, conducting closings, and more every day. Stopping to answer another frequently asked question from a real estate agent or homebuyer can take a toll on your productivity.
That’s where Virtual Closing Assistance, like Alanna, comes in. Using text or chat, Alanna answers questions about an upcoming closing like the status of a file, estimated closing costs, or location and time of the closing. The program can also securely gather information with forms.
Alanna is on 24/7 and has access to the data to help anyone involved in the closing whenever they need it.
As we’ve seen, translating languages has been one of the earliest applications of NLP with some disappointing results, but over the last seventy years, AI is getting better at it.
Whether out of necessity or preference, many Americans don’t speak English at home. Real estate agents, title companies, and mortgage lenders are interested in working with the growing number of Hispanic homebuyers and other bilingual minorities.
AI-powered real-time translation tools can help professionals connect with and better serve these consumers.
3. Underwriting and Title Search Automation
Title searches are the backbone of underwriting. This first step shapes the quality of the final insurance policy issued to lenders and homebuyers. While an expert human eye can spot many problems, having a computer eye to process the information found in public records or title plants enhances the search process.
Whatever database is used, NLP has the power to sort textual data into easier to search and understand information, turning title plants into search engines that could rival Google. Of course, property records spread across disparate systems will need to be aggregated and digitized before this technology can be used. Hardware and software will also likely need an update to process advanced algorithms, requiring significant financial investment.
4. Fraud Detection
When NLP is paired with Machine Learning, programs can detect common patterns found in phishing emails. Email security companies like Gatefy use a specialized algorithm to detect and block spam or phishing emails, reducing exposures to threats and time managing unwanted messages.
This type of text-mining algorithm can also be applied to claims management. The time to manually review documents, claims notes, emails, and other data can be reduced with NLP analysis. The program can assist in the review process and flag irregularities indicating fraud for human review.
5. Financial Reporting and Auditing
Financial reports aren’t all numbers. Some financial firms turn to NLP to analyze and transcribe textual data like an earnings call. Machines can now do these tasks much faster than humans, allowing businesses to make better decisions more quickly.
Three reasons why finance teams are turning to NLP include:
- Automation – capturing unstructured data found in earnings calls, management presentations, and acquisitions announcements are no longer manual.
- Data enrichment – NLP can help the reader find key topics and subtopics and draw meaningful conclusions from the unstructured data.
- Search and discovery – NLP makes searching proprietary databases as easy as a Google search.
Automating the auditing process is more manageable with NLP too. Financial documents are easier to screen, classify, and identify any similarities or differences, so they are easier to flag for errors or anomalies.
6. Marketing Analytics
Consumer sentiment, market trends, and topics that resonate with consumers are easier to detect with NLP. Mining your company’s chatbot-generated data is a great place to start to understand, group, and prioritize incoming prospects. If you get a lot of the same questions, this can direct your marketing team on what changes to make to your website so users can navigate to the answer more easily. AI-powered chatbots can solve these problems and serve the relevant content without your team’s intervention, but if you’re using a static chatbot or live chat, filtering messages with NLP can identify common questions and topics.
What’s the difference between a static chatbot and a virtual assistant like Alanna? Listen to this episode of Title Talks to find out!
Some Human Resource functions are made easier with NLP, including:
- Evaluating resumes
- Scheduling interviews
- Answering questions about the position
- Creating candidate profiles
- Facilitating onboarding
If your website has a careers page, it’s the perfect place to add a recruiting chatbot with NLP.
Technology can help solve problems, but not everyone has the same challenges. The real estate transaction is still a disjointed process relying on different parties with different priorities, resources, and workflows. Automation doesn’t always improve some tasks when municipalities, community associations, lenders, and other lien holders still use manual processes on their end.