Description This dataset and project are part of ClaimWise AI, an intelligent automation service designed to streamline auto insurance claim processing. All data in this release was collected and curated by our team, ensuring originality and alignment with real-world claim processing scenarios.

What’s inside

• claim_metadata.xlsx: Structured metadata of insurance claims including claim IDs, incident details, vehicle attributes, and fraud indicators.
• Auto-claim service pipeline: Demonstrates how to leverage machine learning, embeddings, and multimodal AI to process claim data and simulate real-world automation.

Note on Images The pipeline references car crash and accident images as part of embedding and similarity checks. These images were also collected by our team from publicly available resources and curated for research purposes. They are not redistributed in this dataset but are used internally to illustrate how ClaimWise AI can handle multimodal data.

Key Features

• Automates claim intake and metadata standardization.
• Uses AI embeddings for fraud detection and duplicate claim checks.
• Supports integration with curated external crash images for damage assessment.
• Provides an end-to-end demo of an AI-powered insurance claim automation workflow.

Use Cases

• Fraud detection in auto claims
• Accident severity classification
• Claim similarity and duplicate detection
• AI-enabled insurance process optimization

Comprehensive analysis of artificial intelligence implementation in car insurance claims processing for 2025, examining benefits, risks, and implications for accident victims seeking fair compensation. This dataset provides detailed insights into AI-driven claims handling, regulatory considerations, and practical guidance for navigating automated insurance systems.

This dataset provides detailed records of healthcare insurance claim submissions, including patient demographics, provider information, claim amounts, reimbursement outcomes, denial reasons, and fraud indicators. It is ideal for operational analytics, process automation, and advanced fraud detection in healthcare insurance workflows.

Subtitle

A fully synthetic dataset simulating real-world medical billing scenarios, including claim status, denials, team allocation, and AR follow-up logic.

Description

This dataset represents a synthetic Account Receivable (AR) data model for medical billing, created using realistic healthcare revenue cycle management (RCM) workflows. It is designed for data analysis, machine learning modeling, automation testing, and process simulation in the healthcare billing domain.

The dataset includes realistic business logic, mimicking the actual process of claim submission, denial management, follow-ups, and payment tracking. This is especially useful for: ✔ Medical billing training ✔ Predictive modeling (claim outcomes, denial prediction, payment forecasting) ✔ RCM process automation and AI research ✔ Data visualization and dashboard creation

Key Features of This Dataset

✅ Patient & Claim Information:

• Visit ID: Unique alphanumeric ID in the format XXXXXZXXXXXX
• Patient Name: Randomly generated names
• Date of Service (DOS): In MM/DD/YYYY format
• Aging Days: Calculated as Today - DOS
• Aging Bucket: Categorized as 0-30, 31-60, 61-90, 91-120, 120+

✅ Claim Status & Denial Logic:

• Status Column: Indicates whether response received or not
• If No Response → Simulates a follow-up call → Claim may result in denial
• Status Code: Reflects actual denial reason (e.g., Dx inconsistent with CPT)
• Action Code: Required follow-up action (e.g., Need Coding Assistance)

• Team Allocation: Based on denial type

  • Coding-related denial → Coding Team
  • Submission/Claim-related denial → Billing Team
  • Payment-related denial → Payment Team

✅ Realistic Denial Scenarios Covered:

• Coding Errors (Dx inconsistent with CPT, Missing Modifier)
• Claim Issues (Duplicate Claim, Invalid Subscriber ID)
• Payment Issues (Allowed Amount Paid, No Coverage)

✅ Other Important Columns:

• Claim Amount, Paid Amount, Balance
• Insurance Details (Primary, Secondary, Tertiary)
• Notes explaining denial or next steps

Columns in the Dataset

Data Generation Rules Applied

• Date format: MM/DD/YYYY
• Aging Days: Calculated dynamically based on DOS
• Visit ID: Always follows the XXXXXZXXXXXX format

• Denial Workflow:

  • If claim denied → Status Code & Action Code updated
  • Team allocation based on denial type

• Payments: Realistic logic where payment may be partial, full, or none

• Insurance Flow: Balance moves from primary → secondary → tertiary → patient responsibility

Use Cases

• Predictive modeling for claim outcome
• Identifying high-risk claims for early intervention
• Denial pattern analysis for improving first-pass resolution rate
• Building RCM dashboards and AR management tools

License

CC BY 4.0 – Free to use, modify, and share with attribution.

An excessive amount of money is misspent on unprecedented minor car damages, in today’s age of fraudulent insurance claims. A software based on machine learning and deep learning algorithms can help to solve these kinds of issues for insurance industries. The algorithms are fed the images of damaged cars and it is made to assess its severity. Usually, visual inspection and validation are used to reduce such incidents. However, in order to lessen the delays in claim processing, the need for an automated system is inherent. Faster processing can be claimed by automatic car damage detection in the insurance industry. The use of Artificial Intelligence in insurance claims is only possible if the model is welltrained on a huge amount and variety of training data sets. These datasets must also be properly annotated. This is to detect the level of damage for accurate claims so as to avoid false claiming of insurance.

This data was scraped by me from multiple sources.

This dataset provides a detailed record of automated claim edits and scrubbing results, capturing errors, inconsistencies, medical necessity issues, coding problems, and compliance violations prior to insurance claim submission. It enables healthcare organizations to track, resolve, and analyze claim issues, improving billing accuracy and compliance while reducing denials and delays.

Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants

Overview

This dataset can be used to train Large Language Models such as GPT, Llama2 and Falcon, both for Fine Tuning and Domain Adaptation.

The dataset has the following specs:

• Use Case: Intent Detection
• Vertical: Customer Service
• 27 intents assigned to 10 categories
• 26872 question/answer pairs, around 1000 per intent
• 30 entity/slot types
• 12 different types of language generation tags

The categories and intents have been selected from Bitext's collection of 20 vertical-specific datasets, covering the intents that are common across all 20 verticals. The verticals are:

• Automotive, Retail Banking, Education, Events & Ticketing, Field Services, Healthcare, Hospitality, Insurance, Legal Services, Manufacturing, Media Streaming, Mortgages & Loans, Moving & Storage, Real Estate/Construction, Restaurant & Bar Chains, Retail/E-commerce, Telecommunications, Travel, Utilities, Wealth Management

For a full list of verticals and its intents see https://www.bitext.com/chatbot-verticals/.

The question/answer pairs have been generated using a hybrid methodology that uses natural texts as source text, NLP technology to extract seeds from these texts, and NLG technology to expand the seed texts. All steps in the process are curated by computational linguists.

Dataset Token Count

The dataset contains an extensive amount of text data across its 'instruction' and 'response' columns. After processing and tokenizing the dataset, we've identified a total of 3.57 million tokens. This rich set of tokens is essential for training advanced LLMs for AI Conversational, AI Generative, and Question and Answering (Q&A) models.

Fields of the Dataset

Each entry in the dataset contains the following fields:

• flags: tags (explained below in the Language Generation Tags section)
• instruction: a user request from the Customer Service domain
• category: the high-level semantic category for the intent
• intent: the intent corresponding to the user instruction
• response: an example expected response from the virtual assistant

Categories and Intents

The categories and intents covered by the dataset are:

• ACCOUNT: create_account, delete_account, edit_account, recover_password, registration_problems, switch_account
• CANCELLATION_FEE: check_cancellation_fee
• CONTACT: contact_customer_service, contact_human_agent
• DELIVERY: delivery_options, delivery_period
• FEEDBACK: complaint, review
• INVOICE: check_invoice, get_invoice
• ORDER: cancel_order, change_order, place_order, track_order
• PAYMENT: check_payment_methods, payment_issue
• REFUND: check_refund_policy, get_refund, track_refund
• SHIPPING_ADDRESS: change_shipping_address, set_up_shipping_address
• SUBSCRIPTION: newsletter_subscription

Entities

The entities covered by the dataset are:

• {{Order Number}}, typically present in:
• Intents: cancel_order, change_order, change_shipping_address, check_invoice, check_refund_policy, complaint, delivery_options, delivery_period, get_invoice, get_refund, place_order, track_order, track_refund
• {{Invoice Number}}, typically present in:
  • Intents: check_invoice, get_invoice
• {{Online Order Interaction}}, typically present in:
  • Intents: cancel_order, change_order, check_refund_policy, delivery_period, get_refund, review, track_order, track_refund
• {{Online Payment Interaction}}, typically present in:
  • Intents: cancel_order, check_payment_methods
• {{Online Navigation Step}}, typically present in:
  • Intents: complaint, delivery_options
• {{Online Customer Support Channel}}, typically present in:
  • Intents: check_refund_policy, complaint, contact_human_agent, delete_account, delivery_options, edit_account, get_refund, payment_issue, registration_problems, switch_account
• {{Profile}}, typically present in:
  • Intent: switch_account
• {{Profile Type}}, typically present in:
  • Intent: switch_account
• {{Settings}}, typically present in:
  • Intents: cancel_order, change_order, change_shipping_address, check_cancellation_fee, check_invoice, check_payment_methods, contact_human_agent, delete_account, delivery_options, edit_account, get_invoice, newsletter_subscription, payment_issue, place_order, recover_password, registration_problems, set_up_shipping_address, switch_account, track_order, track_refund
• {{Online Company Portal Info}}, typically present in:
  • Intents: cancel_order, edit_account
• {{Date}}, typically present in:
  • Intents: check_invoice, check_refund_policy, get_refund, track_order, track_refund
• {{Date Range}}, typically present in:
  • Intents: check_cancellation_fee, check_invoice, get_invoice
• {{Shipping Cut-off Time}}, typically present in:
  • Intent: delivery_options
• {{Delivery City}}, typically present in:
  • Inten...

A comprehensive Car Damage Severity Dataset containing diverse vehicle images annotated for damage severity, ideal for insurance automation, ADAS model development, and automotive AI training.

The total number of Insurance Affordability Programs (IAPs) applications received through County Human Services Agency offices by submission channel (whether submitted online, in-person, phone, e-mail, mail/fax, other, or unknown) during a reporting period. The “outreach” submission channel may be included in the “other” submission channel commencing with 2016 Quarter 3. IAPs include Medi-Cal or subsidized Qualified Health Plans (QHPs) offered through Covered California. This dataset is part of the public reporting requirements set forth in the California Welfare and Institutions Code 14102.5(1)(A).

The Car Damage Dataset contains labeled images of damaged automobiles designed for developing and training machine learning models to detect and classify car damages. It is particularly useful for applications in insurance fraud detection and automated claims assessment.

The "Eyes on the Ground'' project is a collaboration between ACRE Africa, the International Food Policy Research Institute (IFPRI), and the Lacuna Fund, to create a large machine learning (ML) dataset that provides a close-up view of smallholder farmer's fields based upon previous work within the Picture Based Insurance framework.

In order to help farmers across Africa manage agricultural risk, ACRE Africa utilizes image data to settle insurance claims and carry out loss assessment. ACRE reviews smartphone pictures of insured crops sent in by farmers to verify whether a farmer’s crops werelooking at damaged,s and whether this damage was related to weather, pests and diseases, oras well as man-made factors such as fire, to evaluate an insurance claim and determine appropriate compensation.

Evaluating images for thousands of insured smallholder farmers to verify insurance claims is however time-consuming, and this often slows down claims settlement. ACRE Africa is therefore looking at artificial intelligence to automate claims settlement, building on the training data that ACRE Africa and IFPRI produced with support from the Lacuna Fund.

Since most claims are related to drought, this challenge will ask participants to predict drought damage from smartphone images of crops taken in the past. The Eyes-on-the-Ground project has already successfully trained models to predict drought damage in the first two seasons, but those models did not transfer well into the third season on which data are available.

We therefore invite you to improve on the existing solutions and improve the accuracy at which your model predicts drought damage across multiple seasons. The extent of drought damage can take on values of zero (in case there is no drought damage, and no insurance payout should be made), or a positive value (in case there is drought damage, and an insurance payout proportional to the amount of damage should be made).

We thank CGIAR Research Initiative on Digital Innovation for technical and financial support that made this Making the impossible, possible.le, possible.le, possible.

This dataset provides a comprehensive view of underwriting guidelines, including acceptance criteria, risk appetite, declination reasons, referral triggers, authorization limits, and automated underwriting rules by line of business and risk segment. It enables insurers to standardize, analyze, and automate underwriting processes, ensuring compliance and consistency across products and segments.

Intagible-Assets Time Series for Cognizant Technology Solutions Corp Class A. Cognizant Technology Solutions Corporation, a professional services company, provides consulting and technology, and outsourcing services in North America, Europe, and internationally. It operates through four segments: Financial Services; Health Sciences; Products and Resources; and Communications, Media and Technology. The company provides services including artificial intelligence (AI) and other technology services and solutions, consulting, application development, systems integration, digital engineering, quality engineering and assurance, application maintenance, infrastructure, security, cloud, internet of things, enterprise platform services, and business process services and automation. It also offers AI-led automation, which includes advisory, and process and IT automation solutions designed to simplify and accelerate automation adoption; business process outsourcing services, which help deliver business outcomes including revenue growth, increased customer and employee satisfaction, and cost savings; and Cognizant Moment, a digital experience service that uses AI to reimagine customer experiences and engineer strategies aimed at driving growth. In addition, the company develops, licenses, implements, and supports proprietary and third-party software products and platforms; and develops industry-specific products and services. It offers solution to healthcare providers and payers, life sciences companies, banking, capital markets, payments and insurance companies, manufacturers, automakers, retailers, consumer goods, travel and hospitality, communications, media and entertainment, education, information services, and technology companies, as well as businesses providing logistics, energy, and utility services. The company was incorporated in 1988 and is headquartered in Teaneck, New Jersey.

Operating-Expenses Time Series for Cognizant Technology Solutions Corp Class A. Cognizant Technology Solutions Corporation, a professional services company, provides consulting and technology, and outsourcing services in North America, Europe, and internationally. It operates through four segments: Financial Services; Health Sciences; Products and Resources; and Communications, Media and Technology. The company provides services including artificial intelligence (AI) and other technology services and solutions, consulting, application development, systems integration, digital engineering, quality engineering and assurance, application maintenance, infrastructure, security, cloud, internet of things, enterprise platform services, and business process services and automation. It also offers AI-led automation, which includes advisory, and process and IT automation solutions designed to simplify and accelerate automation adoption; business process outsourcing services, which help deliver business outcomes including revenue growth, increased customer and employee satisfaction, and cost savings; and Cognizant Moment, a digital experience service that uses AI to reimagine customer experiences and engineer strategies aimed at driving growth. In addition, the company develops, licenses, implements, and supports proprietary and third-party software products and platforms; and develops industry-specific products and services. It offers solution to healthcare providers and payers, life sciences companies, banking, capital markets, payments and insurance companies, manufacturers, automakers, retailers, consumer goods, travel and hospitality, communications, media and entertainment, education, information services, and technology companies, as well as businesses providing logistics, energy, and utility services. The company was incorporated in 1988 and is headquartered in Teaneck, New Jersey.

Ebit Time Series for Cognizant Technology Solutions Corp Class A. Cognizant Technology Solutions Corporation, a professional services company, provides consulting and technology, and outsourcing services in North America, Europe, and internationally. It operates through four segments: Financial Services; Health Sciences; Products and Resources; and Communications, Media and Technology. The company provides services including artificial intelligence (AI) and other technology services and solutions, consulting, application development, systems integration, digital engineering, quality engineering and assurance, application maintenance, infrastructure, security, cloud, internet of things, enterprise platform services, and business process services and automation. It also offers AI-led automation, which includes advisory, and process and IT automation solutions designed to simplify and accelerate automation adoption; business process outsourcing services, which help deliver business outcomes including revenue growth, increased customer and employee satisfaction, and cost savings; and Cognizant Moment, a digital experience service that uses AI to reimagine customer experiences and engineer strategies aimed at driving growth. In addition, the company develops, licenses, implements, and supports proprietary and third-party software products and platforms; and develops industry-specific products and services. It offers solution to healthcare providers and payers, life sciences companies, banking, capital markets, payments and insurance companies, manufacturers, automakers, retailers, consumer goods, travel and hospitality, communications, media and entertainment, education, information services, and technology companies, as well as businesses providing logistics, energy, and utility services. The company was incorporated in 1988 and is headquartered in Teaneck, New Jersey.

Techsalerator’s Business Technographic Data for Germany: Unlocking Insights into Germany’s Technology Landscape

Techsalerator’s Business Technographic Data for Germany provides a detailed collection of information vital for businesses, market analysts, and technology vendors seeking to understand and engage with companies operating in Germany. This dataset offers a comprehensive view of Germany’s technology landscape, tracking and analyzing data related to technology stacks, digital tools, and IT infrastructure across German businesses.

Please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

Top 5 Most Utilized Data Fields

1. Company Name: This field lists the names of German companies included in the dataset. Identifying these companies allows technology vendors to target their offerings effectively and helps analysts assess technology adoption trends across diverse sectors in Germany.

2. Technology Stack: This field outlines the specific technologies and software solutions utilized by companies, such as ERP systems, cloud platforms, and cybersecurity solutions. Understanding a company’s technology stack helps in identifying their operational needs and potential opportunities for technology providers.

3. Deployment Status: This field indicates whether certain technologies are in use, planned for deployment, or under consideration. Monitoring deployment status is key for vendors to assess market readiness and levels of technology adoption across German companies.

4. Industry Sector: This field classifies companies by industry sectors like automotive, manufacturing, IT services, and financial services. Segmenting companies by industry enables a tailored approach, allowing vendors to align their offerings with specific sector trends in Germany.

5. Geographic Location: This field provides details on the geographic location of the companies’ headquarters or main operations in Germany. Regional insights are crucial for understanding local technological trends and targeting the most relevant markets.

Top 5 Technology Trends in Germany

1. Industry 4.0 and Smart Manufacturing: Germany is at the forefront of the Industry 4.0 revolution, with companies adopting automation, robotics, and IoT technologies to enhance efficiency in manufacturing. This trend is particularly strong in the automotive and industrial sectors.

2. Cloud Computing and Hybrid Solutions: German companies are increasingly leveraging cloud technologies for greater flexibility and scalability. The shift towards hybrid cloud solutions, balancing public and private cloud environments, is especially prominent in the financial and healthcare sectors.

3. Cybersecurity: With stringent data protection regulations like GDPR in place, cybersecurity has become a top priority for German companies. There is a growing focus on threat detection, data privacy, and compliance with EU regulations to safeguard sensitive information.

4. Artificial Intelligence (AI) and Machine Learning: AI is rapidly transforming key industries in Germany, from automotive to healthcare. Companies are investing in AI to optimize processes, improve decision-making, and develop innovative products and services.

5. Sustainability and Green Technologies: Germany’s commitment to sustainability is driving investments in renewable energy, energy-efficient technologies, and circular economy practices. Companies are focusing on reducing carbon footprints and developing eco-friendly solutions.

Top 5 Companies with Notable Technographic Data in Germany

1. Siemens: A global leader in industrial manufacturing and digital solutions, Siemens is heavily invested in Industry 4.0, automation, and smart infrastructure. The company plays a crucial role in advancing Germany’s technological landscape.

2. SAP: As one of the world’s leading providers of enterprise software, SAP is at the forefront of cloud computing, data analytics, and digital transformation solutions. Its extensive product suite is used by businesses across industries in Germany and worldwide.

3. Volkswagen Group: A major player in the automotive sector, Volkswagen is embracing AI, automation, and sustainability initiatives. The company is integrating smart manufacturing techniques and electric vehicle technologies into its operations.

4. Deutsche Telekom: One of Europe’s largest telecommunications providers, Deutsche Telekom is a key player in Germany’s 5G network rollout and digital infrastructure development, driving connectivity innovations across industries.

5. Allianz: As a leading financial services provider, Allianz is leveraging cloud computing, AI, and cybersecurity solutions to enhance its digital transformation and provide better services in the insurance and financial sectors.

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Minority-Interest-Expense Time Series for Cognizant Technology Solutions Corp Class A. Cognizant Technology Solutions Corporation, a professional services company, provides consulting and technology, and outsourcing services in North America, Europe, and internationally. It operates through four segments: Financial Services; Health Sciences; Products and Resources; and Communications, Media and Technology. The company provides services including artificial intelligence (AI) and other technology services and solutions, consulting, application development, systems integration, digital engineering, quality engineering and assurance, application maintenance, infrastructure, security, cloud, internet of things, enterprise platform services, and business process services and automation. It also offers AI-led automation, which includes advisory, and process and IT automation solutions designed to simplify and accelerate automation adoption; business process outsourcing services, which help deliver business outcomes including revenue growth, increased customer and employee satisfaction, and cost savings; and Cognizant Moment, a digital experience service that uses AI to reimagine customer experiences and engineer strategies aimed at driving growth. In addition, the company develops, licenses, implements, and supports proprietary and third-party software products and platforms; and develops industry-specific products and services. It offers solution to healthcare providers and payers, life sciences companies, banking, capital markets, payments and insurance companies, manufacturers, automakers, retailers, consumer goods, travel and hospitality, communications, media and entertainment, education, information services, and technology companies, as well as businesses providing logistics, energy, and utility services. The company was incorporated in 1988 and is headquartered in Teaneck, New Jersey.

Total-Long-Term-Debt Time Series for Cognizant Technology Solutions Corp Class A. Cognizant Technology Solutions Corporation, a professional services company, provides consulting and technology, and outsourcing services in North America, Europe, and internationally. It operates through four segments: Financial Services; Health Sciences; Products and Resources; and Communications, Media and Technology. The company provides services including artificial intelligence (AI) and other technology services and solutions, consulting, application development, systems integration, digital engineering, quality engineering and assurance, application maintenance, infrastructure, security, cloud, internet of things, enterprise platform services, and business process services and automation. It also offers AI-led automation, which includes advisory, and process and IT automation solutions designed to simplify and accelerate automation adoption; business process outsourcing services, which help deliver business outcomes including revenue growth, increased customer and employee satisfaction, and cost savings; and Cognizant Moment, a digital experience service that uses AI to reimagine customer experiences and engineer strategies aimed at driving growth. In addition, the company develops, licenses, implements, and supports proprietary and third-party software products and platforms; and develops industry-specific products and services. It offers solution to healthcare providers and payers, life sciences companies, banking, capital markets, payments and insurance companies, manufacturers, automakers, retailers, consumer goods, travel and hospitality, communications, media and entertainment, education, information services, and technology companies, as well as businesses providing logistics, energy, and utility services. The company was incorporated in 1988 and is headquartered in Teaneck, New Jersey.

Net-Borrowings Time Series for Cognizant Technology Solutions Corp Class A. Cognizant Technology Solutions Corporation, a professional services company, provides consulting and technology, and outsourcing services in North America, Europe, and internationally. It operates through four segments: Financial Services; Health Sciences; Products and Resources; and Communications, Media and Technology. The company provides services including artificial intelligence (AI) and other technology services and solutions, consulting, application development, systems integration, digital engineering, quality engineering and assurance, application maintenance, infrastructure, security, cloud, internet of things, enterprise platform services, and business process services and automation. It also offers AI-led automation, which includes advisory, and process and IT automation solutions designed to simplify and accelerate automation adoption; business process outsourcing services, which help deliver business outcomes including revenue growth, increased customer and employee satisfaction, and cost savings; and Cognizant Moment, a digital experience service that uses AI to reimagine customer experiences and engineer strategies aimed at driving growth. In addition, the company develops, licenses, implements, and supports proprietary and third-party software products and platforms; and develops industry-specific products and services. It offers solution to healthcare providers and payers, life sciences companies, banking, capital markets, payments and insurance companies, manufacturers, automakers, retailers, consumer goods, travel and hospitality, communications, media and entertainment, education, information services, and technology companies, as well as businesses providing logistics, energy, and utility services. The company was incorporated in 1988 and is headquartered in Teaneck, New Jersey.

Total-Long-Term-Liabilities Time Series for Coforge Ltd. Coforge Limited provides information technology (IT) and IT enabled services in India, the Americas, Europe, the Middle East and Africa, India, and the Asia Pacific. The company offers digital process automation services, including workflow/process management, artificial intelligence (AI) and predictive analytics, RPA, and case management; digital services, such as interactive services, product engineering, enterprise, and intelligent automation; and cloud and infrastructure management services comprising cloud, workplace, cybersecurity, data center, and always on network, as well as service integration services. It also offers cybersecurity services, which include security incident management; vulnerability management; threat intelligence; identity and access management; phishing, analysis, and training; governance & risk management; and workplace security. In addition, the company provides AI and machine learning, business analytics and BI, data engineering and management, D&A Ops, and advisory services. Further, it offers engineering services consisting of product quality, advisory consulting, automation engineering, business assurance testing, enterprise and product testing, AI, and ML infused testing services. Additionally, the company provides business process solutions. Coforge Limited serves insurance, travel, tourism, hospitality, banking and financial services, retail, healthcare, and public sectors. It has strategic alliances with Kong Inc. for cloud API services; and Newgen Software Technologies Limited to enhance digital operations for organizations. The company was formerly known as NIIT Technologies Limited and changed its name to Coforge Limited in August 2020. Coforge Limited was incorporated in 1992 and is based in Greater Noida, India.