Client:  AB Kelių priežiūra

Problem Statement: 

Road inspections usually relied on manned vehicles that covered large sections of the road network. This approach is not only labor-intensive and inefficient but also has a considerable environmental impact, as frequent trips by these vehicles led to high CO2 emissions.

AB Kelių priežiūra, Lithuania’s state-owned company responsible for road maintenance and repair, sought to modernize its approach to road maintenance. The organization aimed to reduce carbon emissions, optimize resource allocation, and increase overall efficiency. With a growing focus on sustainable infrastructure, AB Kelių priežiūra turned to EasyFlow.tech, an AI-based technology provider, to implement a solution for predictive road maintenance that would minimize environmental impact.

Results: 

• Reducing CO2 emissions by 90%.
• Enhancing resource utilization.
• Streamlining maintenance efforts for greater precision and efficiency.

AI Solution Overview:

EasyFlow.tech, together with the state-owned road maintenance company AB Kelių priežiūra and UAV provider Thrust, has launched the GreenBee project, an initiative for road maintenance in Lithuania. 

The GreenBee project uses unmanned aerial vehicles (UAVs) equipped with sophisticated sensors and high-resolution cameras to tackle challenges in road upkeep. This UAV-based road inspection method enables thorough and efficient data collection from the air, eliminating the need for traditional ground vehicles.

With advanced AI and Computer Vision capabilities, EasyFlow.tech quickly processes UAV-captured data to pinpoint road infrastructure issues accurately. This approach not only speeds up inspections but also improves precision, reducing human errors and inconsistencies.

GreenBee’s use of UAV and AI technologies marks a major advancement in sustainable road maintenance. By relying on accurate, AI-driven analysis, maintenance teams can be deployed more strategically—ensuring they are sent exactly when and where needed. 

References: 

1. AI Road Maintenance Implementation to Reduce CO2 Emissions: Case Study. https://easyflow.tech/ai-road-maintenance/ 

Industry: Road Maintenance

Vendor: EasyFlow.tech

Client: AB Kelių priežiūra

Publication Date: 2024

The post Implementing AI in Road Maintenance to Lower CO2 Emissions appeared first on StopCorruption.AI.

By providing timely, tailored, and localized support, the AI network has helped foster a more inclusive and efficient entrepreneurial ecosystem.

Client: 

The Finnish government

Problem Statement: 

Finland boasts a dynamic and expanding entrepreneurial scene, with strong digital adoption and a focus on innovation. Despite this, many Finnish entrepreneurs struggle with complex regulatory frameworks, limited resources, and a lack of tailored support. To address these challenges, the Finnish government wanted an AI solution which offers personalized guidance, regulatory insights, and practical advice, empowering entrepreneurs and small business owners across Finland.

Results: 

• A single place to get customer’s questions answered.
• Virtual agents can answer frequently asked questions, anticipate needs and automate routine tasks.
• Employees can focus on more complex cases that need human support.

AI Solution Overview:

The Finnish Immigration Service partnered with Accenture, which then brought in a startup called boost.ai for further development. This collaboration soon expanded to include the Finnish Patent and Registration Office and the Finnish Tax Administration. Within just three months, these organizations built one of the world’s first AI-powered Virtual Agent Networks—a system of AI-driven virtual assistants offering a more integrated and streamlined service for entrepreneurs looking to establish their business in Finland.

This innovative “Living Service” features three AI-enabled virtual assistants: one focused on immigration matters, another on business patents and registrations, and a third on tax-related inquiries. While each assistant is highly effective individually, together they create a unified and seamless user experience.

The Virtual Agent Network unites various organizations across sectors to deliver a comprehensive, proactive, and user-friendly service for both citizens and businesses.

References: 

1.  AI-driven Virtual Agent Network for entrepreneurs. https://www.accenture.com/us-en/case-studies/public-service/ai-powered-virtual-agent-network

Industry: Public Services

Vendor: Accenture

Client: The Finnish government

Publication Date: 2024

The post AI-Powered Virtual Support Network for Finnish Entrepreneurs appeared first on StopCorruption.AI.

By leveraging AI, the company increased cases of reports by more than 100% within two years.

Client: 

Sinch is an international communications platform specializing in mobile customer engagement. Headquartered in Sweden, it employs over 4,500 people across more than 60 countries. The company was established in 2008.

Problem Statement: 

As its workforce and global presence expanded, Sinch identified the need for an efficient, streamlined whistleblowing solution to enable quick and secure reporting of ethical concerns across the organization. Their previous manual whistleblowing system was inefficient and not scalable, resulting in delayed responses and insufficient data analysis. To address this, Sinch sought a whistleblowing platform within the EU that ensured GDPR compliance, while also prioritizing user-friendliness and cost-effectiveness.

Results: 

• Increased cases of reports by more than 100%.
• Enhanced company’s ability to oversee and address issues effectively. 
• Possibility for employees to report concerns without fear of losing their anonymity.
•  A 2-month time to roll the platform out to all 4500 employees.
• Reduced time needed to handle and address whistleblower reports.

AI Solution Overview:

SpeakUp is a whistleblowing and case management platform that utilizes AI to enhance its capabilities. It incorporates AI to analyze reports, identify trends, detect anomalies, and streamline case management workflows. AI can assist in categorizing reports, prioritizing cases based on urgency, and identifying potential patterns of unethical behavior. This helps organizations manage compliance issues more efficiently and effectively, ensuring timely responses and action.

Sinch adopted the SpeakUp platform, an advanced whistleblowing solution powered by AI. SpeakUp provided Sinch with:

• AI-Powered Report Management: SpeakUp’s AI-enabled platform helped Sinch automate the categorization and prioritization of whistleblower reports, allowing the compliance team to focus on the most urgent cases first.
• Enhanced Anonymity and Accessibility: Employees could easily report incidents through SpeakUp, which offered secure, anonymous channels, increasing employee trust and the overall reporting rate.
• Data Insights and Predictive Analysis: The platform’s AI capabilities enabled Sinch to analyze trends across reported incidents, uncovering patterns and potential areas of risk that required attention. This empowered Sinch to proactively address issues before they escalated.

References: 

1. How Sinch increased misconduct reporting by more than 100%. https://www.speakup.com/case-studies/sinch

Industry: Tele- and cloud communications

Vendor: SpeakUp

Client: Sinch

Publication Date: 2024

The post How AI Platform Helped to Streamline Company’s Whistleblowing System appeared first on StopCorruption.AI.

By leveraging advanced natural language processing and ML techniques, JPMorgan Chase has significantly improved the efficiency, accuracy, and consistency of their document review processes to identify potential fraud.

Problem Statement: 

JP Morgan Chase & Co. is one of the largest financial services firms in the world, offering a broad range of services including investment banking, asset management, and consumer banking. 

With a vast amount of legal documentation generated daily, the need for efficient analysis and management of contracts became apparent.

Legal departments typically manage an overwhelming volume of contracts and documents, which can be time-consuming and prone to human error. The manual review of contracts can lead to delays in decision-making, compliance risks, and potential financial losses. JP Morgan faced the challenge of improving efficiency in contract analysis while maintaining accuracy and compliance with legal standards.

Results: 

• Reduced time required to review legal documents from 360,000 hours annually to mere seconds. 
• Reduced operational costs. 
• Ability to to manage a larger volume of contracts and agreements without needing a proportional increase in manpower. 
• Legal professionals can focus from repetitive tasks to more analytical and strategic roles.
• A near-zero error rate.

AI Solution Overview:

JPMorgan Chase developed an AI tool called COIN (Contract Intelligence), which harnesses artificial intelligence to make the legal document review process more efficient. COIN is specifically designed to analyze and extract important data from legal documents, particularly commercial loan agreements, a task that traditionally required around 360,000 hours of manual work each year.

By implementing COIN, JPMorgan Chase has drastically transformed this process, reducing the time and resources needed. The platform automates the review of certain types of contracts using AI with minimal human oversight. Running on a private cloud, COIN employs image recognition technology to compare clauses and identify key differences. In its initial phase, COIN extracted about 150 relevant data points from business credit agreements in seconds, eliminating the need for extensive manual labor.

The AI system processes around 12,000 commercial credit agreements annually, not only cutting costs but also greatly minimizing the risk of human error. COIN is so accurate that it achieves an error rate close to zero, a level of precision that would be extremely difficult to reach through manual reviews.

References: 

1. How JPMorgan Chase’s COIN is Revolutionizing Financial Operations with AI. https://medium.com/@the_AI_ZONE/how-jpmorgan-chases-coin-is-revolutionizing-financial-operations-with-ai-120a2938dab7
2.  J.P Morgan – COiN – a Case Study of AI in Finance. https://superiordatascience.com/jp-morgan-coin-a-case-study-of-ai-in-finance/
3. Case Study: JPMorgan Chase’s Contract Intelligence (COiN) platform for Document Analysis. https://www.linkedin.com/pulse/case-study-jpmorgan-chases-contract-intelligence-coin-jorge-chirinos-qcyje/
4. Banking on Artificial Intelligence: How JP Morgan Uses AI to Lead the Banking Industry. https://www.atliq.ai/banking-on-artificial-intelligence-how-jp-morgan-uses-ai-to-lead-the-banking-industry/

Industry: Financial Services

Vendor: JP Morgan Chase & Co.

Publication Date: 2024

The post How Bank Has Automated the Document Review Process to Identify Potential Fraud with AI appeared first on StopCorruption.AI.

Using AI-driven solutions, courts can automate the transcription process, significantly reducing the time needed to produce accurate records. 

Client: 

Estonian Courts

Problem Statement: 

Traditional manual transcription during court hearings is slow, labor-intensive, and prone to human error, making it difficult to keep up with the high volume of court cases. 

While speech recognition technology has been around for years, it is only with recent advancements in ML that it has become feasible to produce accurate, automated transcripts. This innovation greatly streamlines the preparation of court documents by reducing the time and effort required. In response to these developments, the Ministry of Justice of Estonian launched the creation of AI solution, a custom-built speech recognition solution tailored for court use. 

Results:

• Enhanced efficiency and accuracy in transcribing Estonian court hearings.
• High-quality transcripts generated within seconds.
• Optimized use of time and resources.
• Simplified administrative processes.
• Freed up court staff to concentrate on more critical tasks.

AI Solution Overview:

Tilde collaborated with CGI Estonia to create Salme, a custom automatic speech recognition (ASR) solution designed specifically for Estonian court hearings. Salme enables real-time and offline transcription of court proceedings at all levels by converting speech into text.

Tilde focused on developing the ASR system, which handles transcription in both real-time and offline modes. CGI contributed by designing the Windows Presentation Foundation (WPF) user interface and integrating the audio recording system with Tilde’s ASR technology. Additionally, CGI managed the integration of Salme with Estonia’s Court Information System through X-Road®—an open-source platform that ensures secure and unified data exchange between organizations. This integration facilitated the seamless and automated transfer of information.

The speech recognition model was fine-tuned using more than 800 hours of transcribed audio and a textual dataset of over 800 million words, provided by the client. Since its launch, Salme has been successfully deployed across all national and regional courts in Estonia.

References: 

1. Estonian Courts shift to automated transcription with Salme. https://tilde.ai/asr-solution-for-estonian-courts/

Industry: Legal Services

Vendor: CGI Estonia and Tilde

Clients: Estonian Courts

Publication Date: 2024

The post Estonian courts are using AI transcription services appeared first on StopCorruption.AI.

By integrating AI tools, processing speeds improved by 5X without code modifications, and further optimization boosted performance by 20X. 

Client: 

The Internal Revenue Service (IRS) is the federal agency in the United States tasked with collecting taxes and enforcing tax laws. The IRS conducts audits on taxpayers either randomly or after identifying discrepancies in their tax returns.

Problem Statement: 

To tackle tax fraud and identify bad actors, IRS investigators need to sift through decades of data, link individuals to suspicious activities, and trace transactions across multiple layers and steps on a graph. 

One IRS data scientist was assigned the task of analyzing over 3 terabytes of data to detect fraud patterns. However, the available computing resources were inadequate. Even after running the job overnight on a large array of CPUs, it still failed to complete. The team attempted to divide the datasets across different servers, but they had to manually combine the resulting data subsets, making the process cumbersome. Despite their efforts, they couldn’t achieve full visibility for real-time fraud detection.

To address these obstacles, the IRS turned to AI tools, machine learning, and advanced fraud detection applications.

Results:

• 20X increase in speed for running data science experiments.
• Workloads processed 5X faster instantly, without any changes to the code.
• 50% reduction in costs for data science and data engineering workflows.
• Lowered costs and improved protection for taxpayers by effectively preventing fraud and identity theft.

AI Solution Overview:

The IRS has started using advanced AI tools, machine learning, and applications designed to quickly detect fraud and identity theft. The integration of powerful computing infrastructure and software solutions allowed the IRS to easily scale its AI and machine learning operations. By leveraging Cloudera on NVIDIA GPUs, processing speeds increased by up to 5X without requiring any changes to the code. However, there was still potential for further optimization.

Cloudera enlisted a team of NVIDIA data scientists to review the IRS code and found that some tasks involving complex data structures were still being processed on CPUs. NVIDIA developed new code to run these tasks on GPUs, integrating it into Spark’s interface with NVIDIA RAPIDS, an open library for GPU-accelerated data analytics.

When the IRS deployed the updated code on GPUs in a distributed Spark cluster, the performance improved by a staggering 20X. By utilizing Apache Spark and graph analysis, engineering teams built vast networks of nodes and edges. AI bots and machine learning algorithms then analyzed these graphs, enabling investigators to link individuals to institutions and, ultimately, larger networks over extended periods. These insights revealed patterns that helped identify fraud much more quickly.

Data sets that previously took weeks or months to compile and process are now handled in hours or minutes. 

Testing showed a 10X boost in engineering and data science workflows, along with a 50 percent reduction in infrastructure costs. 

With its upgraded computing infrastructure and AI deployment, the IRS is lowering costs and enhancing protection for taxpayers by more effectively preventing fraud and identity theft. 

Building on these advances in data preparation and analytics, the IRS plans to speed up AI inference tasks and use the Spark-GPU infrastructure to address natural language processing and other analytical challenges.

References: 

1. Using AI and Accelerated Computing to Root Out Waste, Fraud, and Theft. https://www.nvidia.com/en-us/case-studies/fraud-detection-applications/

Industry: Public Services

Vendor: NVIDIA and Cloudera

Client: Internal Revenue Service (IRS)

Publication Date: 2024

The post AI to tackle tax fraud appeared first on StopCorruption.AI.

A European law enforcement agency utilizes AI to translate large amounts of evidence.

Client: 

The European Union Agency for Law Enforcement Cooperation (Europol) is the law enforcement body of the EU, tasked with supporting national law enforcement authorities across EU member states. Its mission is to enhance safety in Europe by providing assistance in crime prevention, investigation, and operational coordination among member countries.

Problem Statement: 

The law enforcement agency was tasked with handling large volumes of evidence written in low-resource foreign languages, which lacked sufficient digital tools and data for efficient translation. The agency faced challenges due to the strict timelines for investigations and evidence admissibility, along with a limited number of linguists available to translate the material. These constraints jeopardized their ability to prosecute criminals effectively. To overcome this, they needed a flexible and efficient solution to translate vast amounts of content in low-resource languages within tight deadlines.

Results: 

• Achieved translation speeds of up to 150,000 words per minute, benchmarked across four different languages. 
• Increased translation throughput during peak workloads, ensuring that no end users faced delays and no mission was left unsupported due to resource limitations.
• Ability to process large volumes of evidence much faster, ensuring that critical evidence was translated and reviewed within the required timeframes. 
• Achieved a 30X increase in character throughput, along with a 5X increase in model size, improving both latency and translation quality. 

AI Solution Overview:

When a European law enforcement agency faced the challenge of translating vast amounts of content in low-resource languages under tight deadlines, they turned to LILT’s generative AI platform. Powered by large language models, and using NVIDIA GPUs and NVIDIA NeMo, LILT’s platform enabled faster, large-scale translation of time-sensitive material. This solution helped prevent resource limitations from hindering the agency’s ability to combat crime.

LILT’s platform offers a versatile workflow that empowers non-linguists to autonomously triage documents with machine translation (MT), identifying high-value content and sending bulk translations via API. Professional linguists then focus on translating key evidentiary documents using LILT’s predictive CAT tool. This workflow has been deployed in major operations, contributing to hundreds of criminal arrests and the confiscation of illegal drugs and weapons.

By leveraging NVIDIA A100 Tensor Core GPUs for accelerated model training and NVIDIA T4 Tensor Core GPUs for model inference, LILT achieved a 30X increase in character throughput, along with a 5X increase in model size, improving both latency and translation quality. LILT’s adaptive machine learning models continue to evolve as linguists provide feedback, enabling the platform to keep up with changing colloquial language and diverse content sources such as social media.

NVIDIA NeMo, an end-to-end cloud-native framework, further supports the scalability and efficiency of LILT’s generative AI models, offering an enterprise-ready, cost-effective solution for rapid AI adoption.

References: 

1. AI-Powered Language Translation in Criminal Investigations. https://www.nvidia.com/en-us/case-studies/lilt/
2. Law Enforcement Agency Leverages LILT in Criminal Investigations. https://lilt.com/customer-stories/euro-law-enforcement

Industry: Legal Services

Vendor:  LILT, NVIDIA and AWS

Client: The European Union Agency for Law Enforcement Cooperation (Europol)

Publication Date: 2024

The post AI for Translation in Criminal Investigations appeared first on StopCorruption.AI.

To improve access to information for its employees, the municipality implemented a conversational AI platform. This platform, featuring an AI-powered virtual agent, provides 24/7 support, allowing employees to find answers to a wide range of internal queries, including IT, HR, reporting, and organizational questions, from a knowledge base of over 2,600 topics.

Client: 

Asker municipality, located southwest of Oslo in Norway, employs more than 6,500 people in the public sector.

Problem Statement: 

Asker municipality was looking for a digital solution to facilitate the transition by offering a central repository of information that would be easily accessible and user-friendly for its employees.

Results: 

•  Municipality employees can chat with virtual agent 24/7.
• Ability to find the answers at any time, both during and outside the contact center’s opening hours.

AI Solution Overview:

Asker municipality implemented Boost.ai’s multi-city conversational AI platform to create and deploy an AI-powered virtual agent. This agent, Tore på kontoret (or ‘Tore at the office’ in English), was specifically trained to provide internal employee support. Tore’s knowledge covers a wide range of topics, including common IT and HR inquiries, reporting, organizational questions, and general support services, drawing from a specialized knowledge base of over 2,600 topics.

References: 

1. Asker kommune provides 24/7 support to over 6,500 employees with conversational AI. https://boost.ai/case-studies/tore-pa-kontoret-asker-kommune/

Industry: Human Resources

Vendor: boost.ai

Client: Asker municipality

Publication Date: 2024

The post A Norwegian municipality uses AI virtual agent for 24/7 support for staff appeared first on StopCorruption.AI.

The AI solution offers a data-driven, transparent approach that allows the local authority to optimize their electric vehicle (EV) charging infrastructure by collaborating with energy providers and suppliers, ensuring efficient, accessible, and fair placement of charging stations for UK citizens.

Client: 

Oxfordshire County Council (OCC)

Problem Statement: 

Recent data indicates a widening gap between the increasing number of EV and the availability of charging infrastructure. As the number of EVs continues to grow, the UK will need additional charging points to keep up with demand. This highlights the need for a new strategy in implementing EV infrastructure to ensure that, as more people transition to electric vehicles, everyone has access to charging stations.

Results: 

• Increased profits from charging stations.
• Improved management of local and national electricity demand.
• Optimization of infrastructure budgets.
• Ability to work closely with energy providers and charging suppliers.

AI Solution Overview:

Mind Foundry collaborated with OCC, leveraging AI to optimize infrastructure budgets and improve power distribution efficiency. Their AI solution integrates geospatial modeling with diverse data sources and advanced uncertainty forecasting to intelligently predict the evolving needs of EV charging infrastructure.

The Mind Foundry Platform delivers critical optimizations for planners, charge point operators, and energy providers, fostering collaboration across departments and enabling deeper integration with AI. With access to a wide array of multi-sectoral data, OCC can visualize and analyze insights to identify EV and energy-related issues within specific geographic areas.

Powered by ML, the platform connects to both live and historical data sources, offering planning capabilities. These forecasts account for immediate demand and long-term strategy, with built-in extensions for monitoring and managing electricity capacity, enabling smart, data-driven resource optimization.

References: 

1. Optimising electric vehicle charging for Oxfordshire Council. https://www.mindfoundry.ai/resources/case-study/optimising-ev-charging
2. How data insights drive Oxfordshire’s EV charging points. https://www.government-transformation.com/innovation/how-oxfordshire-county-council-is-deploying-ev-charging-points

Industry: Public Services

Vendor: Mind Foundry

Client: Oxfordshire County Council (OCC)

Publication Date: 2022

The post Optimisation of electric vehicle charging with AI appeared first on StopCorruption.AI.

An ML solution analyzed real-time data from IoT sensors, allowing a defense unit to predict device failures with over 92% accuracy.

Client: 

The Indian defense unit

Problem Statement: 

The Indian defense unit aimed to lower maintenance costs for their electronic and electrical devices. With years of equipment data on hand, they required a cohesive strategy to effectively utilize this information.

Results: 

• Achieved 92% accuracy in predicting device failures which included weapons as well. 
• Transition of the defense unit from preventive to predictive maintenance.

AI Solution Overview:

For the needs of the Indian defense unit, Eugenie implemented a digital transformation strategy by integrating IoT sensors with existing manual data. The AI-powered platform analyzed large volumes of data, including years of operational logs and original equipment manufacturer (OEM) information. 

Eugenie’s ML solution processed real-time IoT data to generate predictive alerts for potential device failures or decreased efficiency and provided prescriptive alerts with root-cause analysis. 

A user-friendly dashboard was used to monitor real-time device performance, offering early warnings through preemptive alerts.

References: 

1. Eugenie enabled a defense unit to predict device failures with more than 92% accuracy. https://indiaai.gov.in/case-study/eugenie-enabled-a-defense-unit-to-predict-device-failures-with-more-than-92-accuracy
2. Fractal Announces Merger of Eugenie.ai to Bolster AI-Powered Climate Solutions. https://www.prnewswire.com/in/news-releases/fractal-announces-merger-of-eugenieai-to-bolster-ai-powered-climate-solutions-302183085.html

Industry: Defense

Vendor: Eugenie

Client: The Indian defense unit

Publication Date: 2020

The post AI empowered a defense unit to create predictive alerts for potential equipment failures appeared first on StopCorruption.AI.