A great deal of the work involved in creating effective legal documents is ensuring that all the figures are accurate. A single error in any metric can result in the entire process being flawed, vastly increasing the costs in terms of human resources and profitability.
Thankfully, advanced computing technologies such as Artificial Intelligence (AI) and more specifically machine learning can be leveraged to help document creators identify and eliminate prospective bad data from getting in the way of an efficient and profitable workflow. Organisations today are well aware of the importance of structured data to facilitate them in the strive for achieving competitive advantage, which is why they are quickly turning to technologies such as AI.
What AI Can, and Cannot Accomplish
AI is best utilized in situations that includes numerous rapidly changing variables coming from different inputs. From self-driving cars to diagnosing illnesses, AI has proven itself quite capable of forming the connections needed to make swift and accurate decisions. The problem is in how it accomplishes this. Self-correcting programs make alterations to their algorithms to help better align the results to expectations. The problem is that sometimes changes occur with such frequency that it is nearly impossible for auditors to manually keep track of all of them. This is where machine learning, a subset of AI comes into play.
The machine learning approach aims to teach a machine to identify information regardless of how the content is expressed or how the document is formatted. Machine learning software learns from ‘experience’. Furthermore, when the machine is confronted with unknown document formats and/or new content, it quickly predicts a text’s meaning.
Although while this means that machine learning is capable of providing you with the reports and connections needed to make the right decision, it cannot do so in a manner that guarantees no legal qualms. It then falls upon the individual to review these inputs and outputs carefully to determine whether the decision is the same your team would have come up with manually. Now – this doesn’t mean that the technology itself is worthless. On the contrary, it saves countless hours of tedious work of having to manually extract data from legal documents and transferring them into enterprise resource planning (ERP) systems, replacing this process with a simple quality control that can be handled by reviewers much faster and more efficiently.
How Can AI Spot Bad Data?
AI excels in situations in which vast sums of data must be compared to known historical precedents. With lease contracts, for instance, property values can be measured against current and past trends to spot outliers. This is currently being used by lessors and lessees to examine property values based on historic data to help identify appropriate properties to add to their existing portfolios.
Another way this technology is being used, which can immediately be put into place in real estate practice, is fraud detection. Much like how it can be used to identify outlying data, AI can be used to determine when someone does something completely out of the ordinary. For instance, if all the properties in the area have small lots and the owner is claiming to have instead installed a large parking lot, it would be flagged for verification.
It can also be used to help weed out unqualified buyers or sellers, especially if the transaction will be an ongoing process involving your company. Meaning that you can determine if a client is ready to purchase your property by checking to see their transaction history. This will inform your sales team what sort of lease to offer, and at what rate to offer it.
What is Bad Data?
The short, and obvious answer is any data that is inaccurate. But the truth of the matter is vastly more complicated due to the constantly shifting nature of artificial intelligence. For your business, any data that moved the program away from providing clear and accurate answers can be labelled as bad data.
When setting up your program, one needs to make sure that strict guidelines are written for the AI software to capture the most relevant data points required to derive the best possible results to be analyzed. Consistency is imperative in training an AI. Having a clear understanding of the classification of each data point and assigning them will enable an AI to create a network of links to automate the assignment process. Nuance in data point classification can lead to overlapping classifications. Meaning that the same data point can be mistakenly assigned to different categories and may be reviewed by two or more individuals if the guidelines are not clearly defined.
Creating structured data requires having a clear understanding of the categories into which data should be assigned. In the same way, the human mind learns by creating links, the neural network of an AI system will falter where the links are blurred or clash.
Which is why at LEVERTON, our expert teams meticulously classify each data point for our clients depending on their requirements in order to aid the machine learning software to make necessary connections and capture relevant data from the clients’ documents, thereby saving them countless hours of manual data extraction.
This article was written by Richard Belgrave, Head of Europe, LEVERTON.