Achievement Unlocked: Inc. 5000 List!

Thrilled to announce our placement on the 2023 Inc. 5000 list! The Inc. 5000 is a ranking that highlights the fastest-growing private companies in the United States. It’s a reputable list and we’re pleased to be part of it. We are not just growing, but we’re keeping pace with some of the most rapidly developing companies in the country.

We would like to express our gratitude to everyone who has played a role in our journey so far. This includes our dedicated team, partners, and our customers who trust us with their enterprise needs.

Let’s continue to #MakeSomeNoise!

Adopting Agile Practices: A Key to Modernizing Legacy Systems

The digital revolution has necessitated that organizations adapt and evolve to maintain a competitive edge. A key aspect of this involves the modernization of legacy systems, particularly in Federal government organizations. These organizations grapple with aging infrastructure that struggles to meet modern standards of efficiency, performance, and security. 

In fact, each year, the Federal government spends over $100 billion on IT, with approximately 80 percent of this budget allocated toward the operation and maintenance of existing IT investments, including these aging legacy systems. 

Agile practices, known for their flexibility and efficiency, can act as a catalyst in this modernization process. Agile methodologies are an approach to project management, predominantly in software development, where tasks are divided into small phases of work and reassessed through frequent iterations or sprints. By prioritizing flexibility and customer feedback, Agile methods enable teams to quickly adapt to changes, ensuring the end product meets the evolving needs and expectations of the customer. Here’s more about the crucial role Agile plays in maintaining and then revitalizing these legacy systems.

Agile Practices and Legacy System Modernization

In 2019, the US Government Accountability Office (GAO) conducted an assessment of federal legacy systems and identified ten critical systems that require urgent modernization. Some of these systems have origins dating as far back as the 1970s. In total, the inventory included 65 identified systems, a significant portion of which still rely on legacy programming languages like COBOL. While these legacy systems have demonstrated dependability over time, they present a range of formidable challenges that need to be addressed, including the following:

  • There are escalating maintenance costs associated with outdated technologies and a scarcity of resources with expertise in maintaining these systems.
  • The inflexibility of legacy systems makes it difficult to adapt to changing business requirements. 
  • Compatibility issues also arise when attempting to connect these systems with newer platforms and applications.

To tackle these challenges, Agile methodology offers a flexible and iterative approach to project management and software development. It emphasizes adaptability, customer collaboration, and responsiveness to change. Applying Agile principles to the modernization of legacy systems can yield several benefits. By adopting Agile practices, organizations can introduce a continuous improvement mindset, enabling regular upgrades and enhancements to these systems. This iterative approach allows for the identification and resolution of issues in a more timely manner, leading to optimized functionality and improved performance.

Role of Agile Practices in Facilitating System Upgrades

In the year 2020, an examination of federal agency expenditures revealed that approximately $29 billion was attributed to the expenses associated with maintaining legacy IT systems. These legacy systems require regular upgrades to keep pace with technological advancements and evolving business needs. Agile methodologies have emerged as a crucial factor in facilitating these upgrades, with continuous integration and delivery being core tenets of Agile practices.

A notable example illustrating the benefits of Agile implementation in the government sector is the Federal Aviation Administration’s (FAA) Navigation system update. The FAA leveraged Agile practices to incrementally introduce improvements to the system, thereby minimizing disruptions while enabling regular feedback and adjustments. This iterative approach allowed the FAA to enhance the system’s functionality in a controlled manner while ensuring its alignment with ever-evolving industry standards. With Agile methodologies, the FAA successfully navigated the complexities associated with system upgrades, delivering a more robust and efficient Navigation system for aviation stakeholders.

Enhancing System Maintenance through Agile

Incorporating Valuable Feedback

One of the key advantages of Agile methodologies in legacy system maintenance is the emphasis on continuous feedback. By actively engaging stakeholders, including end-users and system administrators, in the maintenance process, government organizations can gather valuable insights and identify areas for improvement. Agile practices encourage regular and open communication, enabling quick identification of issues and prompt resolution. This iterative feedback loop allows for continuous improvement and ensures that the maintenance efforts are focused on addressing the most critical concerns.

Resource Efficiency with Incremental Development

An Agile approach promotes iterative development, which is particularly beneficial for legacy system maintenance. Instead of attempting large-scale and time-consuming updates, Agile encourages breaking down maintenance tasks into smaller, manageable increments. This approach leads to a more efficient allocation of resources and minimizes the risk of disruptions or system downtime. By continuously delivering incremental updates, government organizations can ensure that high-priority tasks are addressed promptly, reducing the impact on system performance and reliability.

Agile Transformation in USCIS Electronic Immigration System

As an illustration, the U.S. Citizenship and Immigration Services (USCIS) implemented Agile methodologies in the ongoing evolution of their Electronic Immigration System, with various teams collaborating to deliver these methodologies. TechSur helped implement Agile practices for USCIS on one of the later delivery teams. This project as a whole focused on the digital transformation of two high-traffic services: the Form I-90 application for replacing a permanent resident card and the USCIS Immigrant Fee Payment platform. Together with other team members, TechSur facilitated the phased launch of these services, following Agile principles.

Within the Agile framework, daily releases of system updates and enhancements were used to execute the project. Actively engaging with users, seeking their valuable feedback, and conducting consistent usability tests to identify areas for improvement all led to project success. By actively participating in these processes, USCIS realized constant system improvements, prompt problem-solving, and enhanced user satisfaction. 

Overcoming Resistance and Barriers to Agile Adoption

Implementing Agile methodologies in government settings can sometimes encounter resistance, primarily driven by factors such as a culture of rigidity, fear of change, or a lack of Agile expertise within the organization. However, there are notable examples of successful Agile implementation, including the U.S. Department of Defense (DoD). The DoD encountered challenges during its transition to Agile practices but overcame them effectively.

Overcoming resistance to Agile implementation in government settings necessitates a multifaceted approach. Building Agile awareness among the stakeholders and decision-makers is crucial. This involves educating them about the principles, benefits, and potential outcomes of Agile methodologies, enabling them to understand its relevance and potential positive impact on government operations.  Organizations can foster a culture of collaboration and adaptability while encouraging stakeholders to embrace change and actively participate in Agile processes.

Investing in Agile training and coaching can equip government employees with the necessary skills and knowledge to adopt Agile practices successfully. Training programs may cover Agile frameworks, project management techniques, effective communication, and team collaboration. Comprehensive training enables government organizations to empower their workforce to embrace Agile principles and methodologies, enabling them to adapt to changing requirements and deliver projects more efficiently.

Conclusion

The drive towards digital transformation mandates the modernization of legacy systems, particularly in Federal government organizations. With an iterative, flexible approach, Agile practices offer a powerful mechanism to drive this modernization. By facilitating regular system upgrades, enhancing maintenance processes, and overcoming adoption barriers, Agile can significantly boost these systems’ performance, efficiency, and longevity.

As we look towards the future, the importance of Agile practices will only increase, promising more efficient, effective, and adaptive government organizations. Consequently, adopting Agile practices should be prioritized to fully leverage their potential in revitalizing legacy systems and transforming governmental digital infrastructure.

Ready to embrace Agile for legacy system modernization? Trust TechSur as your Agile transformation partner for future-proof government solutions.

 

Using Machine Learning to Flush Out Money Launderers

Money laundering continues to pose a significant challenge, necessitating innovative approaches to combat this illicit activity. Globally, the estimated annual amount laundered falls within the range of 800 billion to 2 trillion dollars

Remarkably, the United States alone contributes at least $300 billion to this total, signifying its responsibility for a substantial portion, ranging from 15% to 38%, of the annual global money laundering volume. Hence, financial institutions are increasingly turning to machine learning and advanced customer risk-rating models to strengthen their defenses against money laundering. 

This article explores the benefits of adopting machine learning algorithms in identifying and flushing out money launderers. Government agencies play a crucial role in combating financial crimes. They can enhance their capabilities and safeguard the financial system by leveraging these technologies.

 

The Power of Machine Learning in Money Laundering Detection

Despite a high imprisonment rate of 91.1% for money laundering offenders, a staggering 90% of money laundering crimes go undetected. To address this challenge, the implementation of machine learning (ML) proves crucial. ML algorithms and advanced data analysis techniques help government agencies detect and prevent money laundering. They identify complex patterns and anomalies in vast financial datasets. Authorities can leverage ML to enhance capabilities, strengthen the fight against money laundering, and ensure a safer financial system.

 

1. Simplified Model Architecture

Machine learning enables organizations to simplify the architecture of their customer risk-rating models. Machine learning models utilize detailed, behavior-focused data to develop advanced algorithms, offering greater flexibility and adaptability to evolving trends. These models outperform traditional rule-based and scenario-based tools, continually enhancing their performance over time. According to McKinsey, a prominent financial institution experienced significant improvements by transitioning from rule-based approaches to machine learning models, achieving up to a 40 percent increase in the identification of suspicious activities and up to 30 percent efficiency gains. This highlights the substantial benefits of leveraging machine learning in combating illicit financial activities. Additionally, a streamlined approach like this enhances operational efficiency and reduces false positives, allowing agencies to focus their resources on high-risk cases.

 

2. Improved Data Quality

Effective money laundering detection relies on high-quality data. Machine learning techniques enable organizations to enhance data quality through automated data cleansing and validation processes. By leveraging these capabilities, government agencies can ensure the accuracy and reliability of their data. Subsequently, this can lead to more precise risk assessments and better-informed decision-making.

 

3. Statistical Analysis and Expert Judgment

A prevalent obstacle in transaction monitoring and anti-money laundering (AML) processes is the generation of a significant number of suspicious activity alarms. It is estimated that only a mere 1-2% of these alerts actually represent genuine threats, leaving the remaining 98% categorized as false positives. 

In contrast, machine learning uses expert judgment with statistical analysis, offering a powerful combination of human expertise and data-driven insights reducing the number of false positives by a significant degree. By incorporating statistical analysis into the risk-rating models, government agencies can utilize both quantitative and qualitative factors to identify potential money laundering activities. 

 

4, Continuous Customer Profiling and Behavioral Analysis

Machine learning algorithms allow for continuous customer profiling, taking into account evolving behaviors and patterns. Government agencies can monitor and analyze customer behavior in real-time to detect anomalies and deviations indicative of money laundering activities. As a result, this dynamic approach ensures that risk assessments remain up-to-date and adaptable to changing circumstances.

 

5. Harnessing Network Science Tools

Machine learning, coupled with network science tools, empowers agencies to uncover intricate money laundering networks and identify key nodes within these networks. This enables government agencies to gain valuable insights into the structure and dynamics of money laundering operations by analyzing complex relationships and connections. This knowledge aids in proactive investigations, targeting not only individual actors but also the broader networks involved.

 

Conclusion

Machine learning algorithms offer immense potential for government agencies in their fight against money laundering. Adopting these advanced techniques, agencies can streamline their detection efforts, improve data quality, and harness the power of statistical analysis and behavioral profiling. Embracing machine learning empowers government agencies to stay ahead of money launderers, protect the integrity of the financial system, and preserve public trust.

Discover how advanced ML algorithms can revolutionize your money laundering detection efforts. Get in touch with TechSur to learn more and stay one step ahead in safeguarding against financial crimes.

Defense Against Government Fraud Using Data Analytics

Government fraud is a major concern for federal agencies, contributing to immense monetary losses and compromising public confidence. However, the introduction of contemporary data analytics technologies has become a powerful instrument in the battle against fraud and abuse of information. This article investigates data analytics’ transformational potential and its application to federal government entities. These agencies may dramatically improve their capacity to detect, prevent, and mitigate fraudulent actions by using the power of big data analytics, protecting public monies, and enhancing overall governance.

 

The Growing Threat of Government Fraud

According to recent research, illicit activities constitute a large amount of government expenditure, resulting in immense fiscal losses each year. Based on Federal Trade Commission data, consumer-reported fraudulent activity surpassed $8.8 billion in losses last year, a significant increase of over thirty percent over the previous year. 

The trend offers a devastating insight; if not controlled, the total loss may exceed by the end of 2023. These setbacks have consequences not just on governmental budgets but also on the execution of key services to residents. To combat this persistent issue, federal government entities must use proactive data analytics-enabled solutions.

 

The Promise of Data Analytics in Combating Government Fraud

1. Enhanced Detection Capabilities

Data analytics allows federal agencies to process vast amounts of structured and unstructured data, such as financial transactions, procurement records, and citizen data. By leveraging advanced analytics techniques such as machine learning and anomaly detection, government agencies can identify patterns and anomalies. These indicate fraudulent behavior. This proactive approach empowers agencies to detect fraudulent activities more swiftly, ensuring timely intervention.

Various research papers illustrate that with the increasing diversity of analytical tools, ‌strategic analysis can be done more extensively. A comprehensive examination can be conducted, encompassing various factors such as threats, vulnerabilities, risks, evolving trends in fraud phenomena, market dynamics, demographic aspects, fiscal policies, and the economic trajectory of entities. The analysis will encompass both the internal environment, considering vulnerabilities and institutional capabilities, as well as the external context, evaluating potential opportunities and threats. 

 

2. Real-time Monitoring and Predictive Insights

The Association of Certified Fraud Examiners’ Report reveals that by adopting proactive data monitoring, organizations can effectively reduce their fraud losses by an average of 54% and expedite the detection of scams by half the usual time. With real-time data analytics, federal agencies can monitor transactions and activities in near real-time, enabling prompt identification and prevention of fraudulent behavior. 

Agencies obtain significant predictive knowledge by employing big data analytics, enabling them to prepare for fraud threats and distribute resources appropriately. The preventive strategy reduces monetary losses and serves as a deterrent, thwarting potential scammers and preventing further damage.

 

Practical Implementation Strategies for Federal Agencies

Encouragingly, many government agencies have made notable progress in addressing fraud, waste, and abuse impacts. They use advanced analytics to identify unmeasured losses and enhance prevention and mitigation efforts. While establishing the necessary organizational framework and acquiring the essential skills may present challenges, the successful implementation of these initiatives can yield significant returns on investment, with ratios ranging from 10:1 to 15:1. 

 

1. Building Analytical Capabilities

Federal agencies must make investments in developing strong data analytics features, which include qualified individuals, facilities, and sophisticated analytics technologies. Agencies may guarantee that data analytics conclusions are effectively incorporated into their decision-making procedures by cultivating a culture that values data. Cooperation with third-party collaborators, such as academics and industry professionals, may be extremely beneficial in building and improving analytical capabilities.

 

2. Establishing Cross-Agency Data Sharing

Government agencies often possess fragmented datasets spread across different systems and departments. Establishing mechanisms for secure data sharing and interagency collaboration is crucial to unlock the full potential of data analytics. By integrating data from multiple sources, agencies gain a comprehensive view of fraudulent activities. This helps uncover patterns and networks that may otherwise remain unnoticed.

 

3. Continuous Monitoring and Iterative Improvement

Data analytics initiatives should be treated as an ongoing process rather than a one-time effort. Agencies should establish a feedback loop that incorporates regular monitoring, evaluation, and continuous improvement of analytical models and techniques. By staying abreast of evolving fraud schemes and adapting analytics approaches accordingly, agencies can effectively respond to emerging threats.

 

Conclusion

For federal government agencies combating fraud, waste, and abuse, data analytics is a robust weapon. Agencies can improve their fraud detection, prevention, and response capabilities by leveraging the power of big data analytics. Federal agencies can achieve cost savings, preserve public finances, and strengthen public faith by investing in analytical capabilities. Cross-agency collaboration on data and a commitment to continual improvement are key. Adopting data analytics is not just an opportunity but also an imperative in a rapidly growing threat landscape.

Take your government agency’s defense against fraud to the next level with TechSur. As a trusted partner, we provide advanced technology solutions and expertise in data analytics to empower agencies in combating fraudulent activities and safeguarding public funds. Discover how our cutting-edge tools can enhance your fraud detection capabilities, and visit our website to explore our comprehensive range of services.