Large language models (LLMs) are dramatically changing the way businesses read, process, and manage information. Automating tasks is breezier than ever, whether it involves extracting data or understanding questions. In fact, LLMs are becoming increasingly popular in address management.
Address information is often incomplete, inconsistent, or formatted differently. And LLMs can identify address parts, fix spelling errors, decipher abbreviations, and standardize unstructured details.
However, businesses operating globally often wonder – is an LLM actually up to the task of validating addresses? Or does it simply churn out addresses that seem accurate?
Address information supports myriad actions - verifying identities, onboarding customers, simplifying logistics, and so on. Hence, you can’t just bank on information that seems right. Data must be up-to-date, accurate, and verifiable.
While LLMs can take address processing up by a notch, robust reference data is the bedrock of trustworthy validation. Let’s explore further.
Though closely connected, parsing and validation aren’t the same.
Parsing happens when you break down an address into components that are recognizable (country, postal code, locality, etc.). The information (including formatting, punctuation, and abbreviations) is then standardized.
An LLM can effectively parse, especially when addresses are in free-text formats.
Say, a customer submits this – Apartment 10, 52 Red Street, City, Postal Code, Country
An LLM doesn’t just identify the components. It preps the information for a structured database. However, it can’t necessarily confirm if Apartment 10 actually exists on said street. Or it doesn’t automatically establish the accuracy of the postal code.
Hence, parsing explains what an address apparently constitutes. But validation checks if the details match an actual, recognized location.
The rules for organizing address components vary from one country to another. Complexities arise from the language used too. An address might be written in Arabic, Latin, or Chinese. And transliteration can lead to multiple variations of the same location where all look valid.
For instance, the name of a street might appear differently after transliteration or translation. Locally and internationally, the same administrative area might have different names.
Now, an LLM can help you interpret these differences. But it doesn’t prove the accuracy of the underlying address.
So, a global validation process is reliable when it makes sense of local addressing conventions and compares entered information against suitable reference data.
During address validation, an LLM model might generate an answer that appears credible even if it can’t verify the underlying data.
Say, you receive an address that includes a building number that seems valid, a real street name, and a postcode linked to the same city. An LLM model will identify the parts and churn out a polished result.
But you might have doubts about the actual existence of the building number, the deliverability of the address, etc. And if the LLM model can’t access dependable reference data, it might not be able to confidently address these doubts.
Hence, there’s no ignoring the possibility of a false validation.
Administrative boundaries change, streets get new names, and new properties are built over time. Addressing conventions might be updated by postal authorities as well. However, an LLM’s training data cannot keep up with these changes automatically.
What’s the solution?
Maintain reference data through specific processes for update. As far as the address data used by your validation systems goes, have clarity about its source quality, coverage, and update frequency.
Regardless of the industry, how you validate addresses impacts various business operations.
Deliveries in e-commerce fail when addresses aren’t correct. Shipping expenses shoot up and customers are disgruntled. And in healthcare, you cannot efficiently communicate with patients without correct location data.
When financial establishments don’t validate addresses properly, fraud prevention efforts often go to waste. Without accurate addresses, insurance sector players have a tough time evaluating risk and handling claims.
Inaccurate addresses have another aftermath – inconsistent customer records.
For example, a customer might supply a certain address during onboarding and a different version at the time of order placement. So, without record reconciliation, different departments might have to work with contradicting details.
Hence, incorporate address validation in your broader strategies for data quality and management.
LLMs are best used for tasks based on language instead of factual confirmation.
With an LLM model, you can extract addresses from different sources and identify components in unstructured text. You can comprehend local abbreviations too and support multi-format and multilingual entries. It’s possible to normalize inconsistent formatting, detect incomplete records, and prepare structured information for validation APIs too.
Besides minimizing manual preprocessing, this crafts a more flexible experience for customer data entry.
However, also check the LLM-generated structured address vis-à-vis a trustworthy validation service or reference database. This will help determine if the record is accepted, fixed, requires review, or rejected.
Before sending address information to a hosted LLM, consider how it’s stored, secured, and processed. Any associated risk depends on the applicable privacy laws, provider, contractual terms, deployment model, and retention policies.
Consider if:
The data is being sent to a third-party processor
The data is being retained for improving the LLM model
Any contractual safeguards are present
You can control the location of data processing
The data is essential for the AI task
Investing in an LLM model is usually on the inexpensive side if you are looking for occasional data cleaning.
However, for high-volume address processing, the economics change. The cost of leveraging an LLM typically depends on usage (number of tokens or requests processed). For millions of addresses, it’s critical to consider other factors too – like monitoring, latency, quality assurance, retries, and API expenses.
Don’t ignore the cost of obtaining authoritative reference data either. Without it, the LLM model cannot generate dependable results.
How to make the most of AI without accepting the generated information as unconfirmed truth?
Stage 1
Capture the address from a CRM platform, online form, or another source. Let the LLM parse the same and recognize individual components.
Stage 2
The LLM then applies the basic rules of formatting and normalization. Besides spotting missing fields, it eliminates characters that aren’t required and standardizes abbreviations.
Stage 3
The structured information then passes on to an address validation service. This service is linked to geographic and postal reference data that’s dependable.
Stage 4
The validation system checks if the address parts match a known location and returns appropriate status information.
Stage 5
You apply decision rules finally. A match that’s confirmed generally proceeds automatically. If the result is incomplete or vague, it’s sent for manual review or customer confirmation.
LLMs can help you understand unstructured address data, extract its parts, normalize text, and support multilingual workflows. But don’t treat them as a reliable source for confirming if an address is genuine or deliverable. For that, combine AI flexibility with the trustworthiness of verified reference data.
So, while modernizing your data infrastructure, don’t use LLMs to replace all established processes. Rather, adopt a hybrid approach where AI meets proper fact verification controls. Avant-garde solutions for verifying, standardizing, matching, and enriching address information will get you started.
LLMs can help businesses understand unstructured address data, extract address components, normalize text, and support multilingual workflows. But they should not be treated as the source of truth for determining whether an address is genuine, accurate, or deliverable.
For that, businesses need to combine the flexibility of AI with trusted address reference data and dedicated verification processes.
As you modernize your data infrastructure, the goal should not be to replace established address verification processes with LLMs. Instead, adopt a hybrid approach where AI helps interpret and prepare address data, while reliable verification systems confirm whether the information matches a recognized location.
Melissa Global Address Verification helps businesses parse, standardize, verify, and enrich address data against trusted reference sources across global markets. Combine AI-powered data processing with reliable address verification to improve data quality, support accurate customer records, and make address information more dependable across your business.