Product Trends in Social Media
With the advent of Social Media, economic demand for products has turned into a dynamic landscape. Traditional methods of product demand forecasting and trend analysis become unable to cope with the nature of social media content. Retail businesses engage AI to identify popular trends, using Named Entity Recognition to identify them, and promote similar products in their stores in order to stay ahead of the curve.
Contract Content Analysis
Contract Negotiation and Contractual Interpretation are some of the more important parts in establishing a real estate deal. Implementing AI software can help real estate professionals review contracts more rapidly and organize and locate large amounts of contract data by extracting key entities like termination dates, agreed on payments, legislation references, contracting parties etc.
Improving customer support strategy and efficiency is a high priority for every company. Using NLP, companies can get a solution where they see the chat graphs, comparing the chat with the time trends on an hourly, daily, weekly or monthly basis. Analyzing the peak hours, waiting times, response time, and chat rating helps companies to retain their customer base by optimal utilization of resources.
Biomedical Named Entity Recognition
Bio-entity extraction is a core task of information extraction from medical literature. Examples of such entities include names of genes, proteins, location of activity etc. With the increase in biomedical record content, human annotators find this extraction task tedious. AI and Deep Learning can automate identifying and classifying bio-medical terms into predefined categories, from blocks of unstructured text data.
Service Reports Interpretation
Quality Engineers periodically analyze service reports of machinery that have broken down to assess the cause of failure. The large volume of reports make this process very tedious and create bottlenecks. AI can be used to optimize this task. The text reports provided by the service team are fed into the ML model, and the AI automatically assigns a failure label to it to analyse the reliability statistics. AI & ML reduces the cost and resources required for the process
RISK & COMPLIANCE / BANKING
Bank Transaction Screening
All bank transactions are screened to check if the entities involved are on a blacklist. Legacy filtering systems produce a high rate of false positives that then must be further escalated for review. Using Named Entity Recognition (NER), AI can identify entities that are parties to suspect transactions. Extracting party details from transactional text information, ML models can review transactions faster and with more accuracy. By implementing AI, banks can massively reduce regulatory costs due to non-compliance fines.
Enterprise Correspondence Monitoring
Banks employ stringent data security laws to prevent breach of sensitive information. Misaddressed or unauthorized Emails / messages may lead to data loss or security threats. Using Natural Language Processing, AI systems can monitor and identify suspicious correspondence content. Messages having highly secretive data or recipients not suitable for their context are automatically flagged and escalated to the Compliance/Data Security personnel. Using this, AI helps Banks reduce costs from fines of data security laws infringement.
Regulatory Compliance Monitoring
Regulatory changes have increased exponentially for the financial industry in the past decade. Compliance officers have to interpret tons of regulatory documentation manually and run the risk of making mistakes and oversights. Employing AI, banks can automatically curate regulatory content from financial, federal and state-level regulatory sources. Using Named Entity Recognition (NER), meaningful insight can be extracted quickly from this content, saving banks time and reducing manual resource costs. Banks can then align their policies and workflows to meet these regulations much more efficiently.
Financial Contract Analysis
Documentation analysis is one of the most time-consuming, yet most crucial processes in financial institutions. Employees spend a lot of time reading through physical and digital documents, and can still miss key information. Using Named Entity and Optical Character Recognition, AI models can assist in this analysis by automatically extracting relevant clauses and entities from Loan/Credit Agreements, Collateral Valuations Reports, Financial Leasing Contracts etc. This saves Banks hundreds of thousands in man hours, minimizes risk, uncovers hidden costs and maximizes revenue by diverting employee attention to more productive tasks.
As part of legal research, law professionals have to search through various sources that include legal dictionaries, legal encyclopedias, legal periodicals, annotations, and treatises etc, most of which are digital these days. Text search methods give exact matches but most often miss the researcher’s intent, and most often very time consuming. AI provides a smarter solution to this problem, easily identifying related documents while taking context into consideration. NLP can help find significance and relevance between the text or string input with these documents, where regular text search would fail. This vastly improves the speed, accuracy, and efficiency of retrieving the most relevant information.
Electronic Discovery (e-discovery)
During the e-discovery process, attorneys have to wade through tens of thousands of digital files in their databases and provide relevant information to satisfy the discovery request. Traditional keyword search methods are time-consuming and risk missing crucial information. AI can be implemented to maximise both precision and efficiency of eDiscovery. NLP can reveal relationships between the discovery request text data and assets in the database, while also massively decreasing the effort involved. This saves the organization time and money that would normally be spent on legal professionals to perform this same function.
Legal Contract Analysis
Lawyers and paralegals analyse contracts to ensure their satisfactory execution for their clients. With hundreds or thousands of contracts, this can be a slow, expensive, labor-intensive, and error-prone process, even with a contract management system. Using AI, legal teams can easily analyse large amounts of contract data in a time and cost effective manner. Natural Language Processing can identify and extract relevant information like aggressive clauses, legal anomalies, future financial obligations, renewal or expiration dates, and even summarise contract data down to concise points. This eases review bottlenecks that delays deals or arrangements and their associated revenues.
Pharmaceutical companies save their lab notes and clinical trial data into databases to record their observations of certain drugs, molecules, and chemicals. AI tools can enrich drug research by extracting information from these unstructured data sources and use them in the testing of current and future drugs. NLP applications can be taught to understand pharmaceutical jargon and search this data for topics, phrases and terms, for findings that are more relevant to the company’s current research than initially discovered. AI assists in saving countless man-hours in unnecessary research and saves the costs for conducting additional research experiments.
LOGISTIC & SUPPLY CHAIN
In procurement, both the buyers and vendors have to ensure that the documentations remain consistent in the transaction. The contents of purchase orders, invoices, and order receipt notes, etc. have to match. Manually checking these documents is resource heavy and complex, due to variances in format, nomenclature differences, and unstructured language. This can lead to delivery and payment delays, over or understocking, and loss of revenue. AI streamlines this process, using NER to extract information like delivery address, vendor names, product details, quantity, and pricing from these documents. Using the extracted data, AI can be taught to seamlessly match PO’s with their Invoices and ORN’s, maintaining transaction consistency.
OEM Manuals Querying
Original Equipment Manufacturers (OEMs) create detailed product manuals for each automobile model type in their fleet. These are usually thousands of pages long with information about vehicle specifications, diagnostic procedures, and troubleshooting. Using a Natural Language Processing platform, this unstructured data can automatically be tagged for these relevant details, categorized, and stored onto a database. A repair technician can quickly search through the manuals digitally, using phrases as a search query and receive an extract of the required information. This saves processing time to examine these manuals and speeds up the diagnostic and repair processes.
Repair Order Diagnostics
In the automotive industry, the invoices for third-party repair orders are usually created by multiple repairmen working on the same unit. Processing these unstructured documents for diagnostic purposes can be difficult due to a lack of language structure or grammatical errors. Natural Language Processing is able to process this data much more efficiently than human technicians. AI is taught to extract information like repair type, most common types of repairs, frequently failing models, and can give technicians insight into the failures that happen to specific vehicle models. This makes the diagnostic process much more efficient and simplified, saving technicians hours in viewing these documents manually.
HR & RECRUITMENT
Recruitment processes are lengthy and repetitive. Recruiters review enormous amounts of resumes/CVs for a single job posting. Natural Language Processing is an extremely useful AI tool that saves time analyzing, processing, and screening candidates. Criteria for the ideal candidate like skills, education, and experience can easily be extracted from job descriptions and referenced against the job applications. AI can also analyze historical employee data for a similar role and utilize this along with the profile evaluation to find the best candidate. AI also helps in increasing diversity by removing subconscious bias that may exist in recruiters. In this manner, AI reduces the overall cost for recruiters and improves the general quality of recruitment.
NATURAL LANGUAGE PROCESSING
Electronic Records Analysis
For healthcare services, analyzing electronic medical records is crucial in making the correct clinical decisions for their patients. A large amount of patient information is recorded in the form of free-text notes by physicians. Analyzing this unstructured text data is tedious, but using Natural Language Processing can automatically extract features or risk factors of patient health from these notes. Apart from clinical data, notes about patients’ emotional wellbeing and their speech transcripts can be analyzed to get insights about their mental health as well. AI extracts clinical information that would normally be missed using manual analysis methods.
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