Legal data is the collection of data and metadata about legal matters. This includes information on cases, judges, jurisdictions, and industries. The ability to process and analyze massive amounts of data as close to instantaneously as possible has profound implications for the profession.
This data is publicly available, though it is also often difficult to access, with most legal data published in law books. However, some jurisdictions have begun to publish legal cases digitally. Additionally, volunteers have digitized historical legal data for public access, such as the Caselaw Access Project.
Additional legal data may include interviews with lawyers and judges.
Common attributes of legal data range from the case name, docket number, court and decision date, decision, and jurisdiction. However, the core data is the actual text of the cases themselves.
Furthermore, there is obvious overlap with industry-specific data. For example, a firm specializing in malpractice suits needs access to healthcare and EMS/EHS data while a firm representing a mining company needs access to mining, minerals, and environmental data.
Generally, it is lawyers, paralegals, clerks, and other professionals who use this data as references. The development of translation, image search, speech-to-text, and other features assist them in this.
However, emerging artificial intelligence programs enable legal professionals to use data in other ways. For example, AI programs can analyze judges’ decisions and predict their rulings, suggesting approaches arguments that lawyers may find successful.
These AI programs can also enable law firms to conduct competitor and market analysis by comparing their successes against others in the same field or area.
There is little quality control to do for legal data; human beings have reviewed the cases countless times before publication. It is generally only in the digitization or database creation process that mistakes appear.
For public, open-source databases, mistakes are easily remedied due in part to the volume of database users worldwide. You should, however, check other databases to make sure they’ve been cleansed properly. Consider the data vendor’s reputation and focus on the completeness, consistence, and relevancy of the data more than update frequency as cases take years to reach any conclusion.
Caselaw Access Project
Lexis® Legal Advantage
“On the corporate legal team side, we’ve seen groups clean and aggregate data from multiple systems to speed reporting. We’ve seen leaders set up data models and reports that are tailored to specific business units and executive stakeholders for added awareness and transparency. We’ve seen groups use analytics to “sort” or “triage” matters by risk to enhance the sourcing of work. We are seeing some interesting work around reserve setting and using data to more accurately drive those reserves. We’ve seen some pioneers create models to forecast and predict spend and outcome on matters.”
Banks, credit unions, credit card companies, insurance companies, stock brokerages, investment funds, and more must report their activities to government regulatory agencies. Following financial crises in the late 2000s, regulatory compliance has become stricter and more onerous on financial services companies like those listed above.
From the stricter need for reporting and the massive amounts of data generated by financial institutions, the regtech industry has sprung up, combining regulatory reporting and big data technology.
VR therapy is the use of virtual reality equipment and programs within a therapeutic setting, sometimes as the primary therapy used, to address both mental illness and physical injury. In many cases, VR headsets are used in combination with biometric sensors like heartbeat or electrodermal activity sensors.
CCBT stands for Computerized Cognitive Behavioral Therapy and refers to the use of apps or other programs to do CBT therapy on a personal basis.
Cognitive behavioral therapy is, essentially, a method of training people to recognize fallacious beliefs or self-destructive behavior in themselves and then working with them to change their beliefs and behaviors to more healthy, productive ones. For example, someone prone to catastrophizing (assuming the worst outcome of a situation) may need to learn how to identify more likely outcomes.
CBT works best with mild to moderate emotional disturbances, especially depression and anxiety. Patients may use these programs in concert with their therapists or general practitioners, but cCBT has also proven itself very effective when used on its own.
Machine learning models help identify, analyze, and predict stress in individuals and larger populations. Stress management programs use these models to help people improve their responses to stressors and thus reduce their overall stress levels.
With the expanding online fitness and wearable health device industries, there is an increased interest in health-related apps and devices; stress management programs that integrate with these apps and devices should continue to grow in size and number.
Companies post content to their websites, newsletters, and social media accounts based on social media metrics that indicate when posts receive the most engagement. This engagement varies by social media site, industry, content type (promotion, article, etc.), and the time of posting: post engagement varies by weekday and even time of day. A content calendar, then, is the scheduling of content publication to increase engagement and conversion.
Companies also schedule site maintenance on these calendars, to ensure they only occur during times of least engagement.
Other terms for content calendar include editorial calendar, social media posting schedule, and other variations of these. An editorial calendar, however, focuses on content planned for company-managed websites plus social media accounts. Social media content schedules, on the other hand, focus on social media posts, as the name indicates.
Brand repositioning refers to the process of changing the associations that people have with a brand.
More than a superficial change like a logo redesign, a repositioning strategy represents a radical change in a company’s marketing and identity. Rarely do companies reposition themselves without great need.
Mobile app development entails more than just crafting an attractive and simple user interface (UI) for established customers; it involves designing a secure app that uses very little RAM yet is compatible with different devices. A particularly well-developed app should also be capable of scaling to meet customer demand as your company grows.
Insurance claims management is the process of managing a claim from reception to settlement. The insurance claim process is particularly suited to machine learning solutions as much can be done to cut time and costs, leading to speedier resolution of claims to the satisfaction of both insurer and insured.
Insurance fraud can be committed by either the buyer or the seller of an insurance policy.
The seller may offer policies from non-existent companies, fail to submit premiums, and churn policies to create more commissions. The buyer exaggerate claims, falsify medical history, post-date policies, sell their policy to for cash when they are diagnosed with a terminal disease, or fake their death or kidnapping. We will focus on the buyer insurance fraud in this post.
In the current era, consumers expect the firms they engage with to provide personalized service and offers. They believe that companies have the technological tools to analyze their specific needs and can perform this task with minimal effort, so businesses should develop such ability.
One may imagine that this kind of mass operation will need some resources and financial investment but by implementing artificial intelligence and automated processes, this capability can be readily available for any business.
Traditionally, personalization was focused on a set of rules based on existing data. The firms used to collect data in advance without any consideration for real time data but now better and much faster results can be obtained by using AI that allows businesses to conduct profiling and real-time analysis to optimize each conversion. This process is defined as Predictive Personalization and is driven by Machine Learning.
Promotional planning is the process of optimizing marketing tools, strategies, and resources to promote a product or service to generate demand and meet set objectives. Artificial intelligence (AI) can be used to effectively plan promotional events, measuring their outcomes, and adjusting as necessary to achieve growth.
Remarketing campaigns show ads to people who have visited a business’s website or downloaded one of its apps. Remarketing is designed to get potential customers who have shown interest in your product or service to recall your business and their intent to buy, therefore enlarging the odds of them converting.
Nowadays, the abundance of data and the advances in Machine Learning and big data applications reduce the need for top-down segmentation of customers. Smart customer clustering based on many commonalities help companies better address customer needs to provide the right experience and divide resources efficiently.
Any account management, customer success and support team has one key goal — to reduce and minimize churn. Churn prediction is the process of identifying segments or specific customers that are at risk of churning, i.e discontinuing their business, within a short amount of time in order to deal with the customer health issue as much in advance as possible.
Credit scoring is a statistical analysis performed by lenders and financial institutions to assess a person’s creditworthiness for mortgages, credit cards, and private loans. Credit scoring is used by lenders to decide whether to extend or deny credit.
Traditionally, a person’s credit score determined by credit bureaus is a number between 300 and 850 with 850 being the highest credit rating possible. As new types of lenders and insurers emerge, however, the traditional credit score becomes just one parameter joined with a large variety of alternative data that helps determine a person’s creditworthiness.
Fraud between companies can interrupt the flow of business and destroy their reputations and it is becoming increasingly difficult to identify and stop criminals from committing fraud: PYMNTS.com’s 2019 yearly report, “Securing B2B Payments,” relates that global markets lost $4.2 trillion in 2019 alone due to fraud. However, machine learning can identify fraud accurately before it has occurred.
In today’s global economy, supply chains include numerous partners, with services and sourcing managed across several organizations and in jurisdictions across the world. Corporations are increasing their use of third-party suppliers in the execution of key strategic imperatives and these third-party operations are becoming larger and more complicated as time goes on. Businesses should upgrade their risk management framework if they don’t want to miss potential profits and saved costs.
Quantcast Advertise helps marketers, publishers, and others reach their target audiences. They track interest, behavior, and brand awareness, enabling companies to prospect and target customers. Quantcast claims their data are highly accurate and able to scale to any level.
Quantcast Measure helps marketers, publishers, and advertisers measure audience behavior and traits. They offer real-time demographic, psychographic, behavior, and engagement data.
Previous Loans provides a tool to refine your analysis by looking at past loan payments.