Feasible Fraud Detection Methods

Concept of combining methodological rigor and advanced technology.

There are many papers that claim to be useful frameworks for government reform. It seems that every major consulting firm puts one out during major transitions, such as a new Federal Administration. Our paper is worth reading because it addresses three important themes: Data quality and access Employee roles in change Rigor of advanced analytics We hope you read it and share it with your fellow government leaders: " Data-Powered Leadership Reform 2017 "

Architects - those who design buildings - are amazing people. They combine imagination and math to anticipate possible futures. Their work is confused with strategic thinking, where linear plans are needed. In contrast, architects consider the many, to many, to many possibilities throughout a design, and how, in combination, these will make something wonderful. My grandfather was an architect. He designed and oversaw the construction of many buildings in Southern California. As a young kid, I recall walking with him, wearing a hardhat, and watching teams build Fashion Island in Newport Beach. I don't know if he imagined that it would look like the image below. His drawings were daytime pastels, light buildings, few cars, and happy strolling people. I did not spend a lot of time looking at the stacks of blueprints, but I remember seeing incredible details in complex engineering diagrams. I was impressed. Not as much with the thick layers of drawings, but how he was thinking about the future capability he was helping create. And, indeed he was helping, as he spent a lot of time meeting with the building and engineering teams. He fostered inputs, from everyone. He encouraged learning from every direction.

Government Leadership Needs Our Attention "Among the 4 million people who operate, oversee, and audit federal operations, there are nearly 8,000 senior executives, who are the federal leaders responsible for the total cost and value of federal operations." "Each leader has a common responsibility, regardless of their career rank, to (a) refine the operational capability, (b) improve the operational performance, and (c) optimize the operational outcomes." The Times Are Changing 1. President Trump issued an Executive Order [1] that required the Director of the Office of Management and Budget (OMB), Mick Mulvaney, to develop a plan for effective, efficient, and accountable operations throughout the federal government. 2. Mr. Mulvaney issued an order [2] to all federal agencies to develop plans for reforming their operational capability and performance, including the performance of their respective employees. 3. Outside of OMB, think tanks are publishing helpful, detailed government-wide reorganization plans recommending pragmatic cures to longstanding and pervasive problems in the federal government [3] . 4. President Trump issued a Memorandum [4] that established the White House Office of American Innovation (OAI), and OAI is charged with developing policies and plans to improve federal operations and their outcomes. 5. The Government Performance and Reporting Modernization Act (GPRMA) [6] established leadership support from politically appointed executives, namely the offices of Deputy Secretaries. 6. Through President Trump’s next U.S. Chief Performance Officer [7] federal leaders will have access to specialized support in resolving bureaucratization, adopting quality data, and organizing cross-operational inefficiencies. 7. The Digital Accountability and Transparency Act (DATA Act) [8] required all federal spending information to be standardized and structured for open publication and bulk use, which enables the pursuit of operation-specific costs of business. 8. The Data Coalition is pushing for the DATA Act’s full, source-level publication of agency-reported data in a format available for bulk download and analysis by third-parties [70] . 9. The U.S. Government Accountability Office is actively assessing the leadership support in GPRMA [9] , and has an implementation status report due on the DATA Act this November 2017. 10. Federal leaders have a business case for pursuing critical initiatives that support government reform .

Technology worries people. Specifically, the technology that changes work, such as Artificial Intelligence (AI), worries people. The real threat to one being valuable in work, is not technology, it is bureaucratization. Tools can enhance one's value, but stifling, calcifying, stove-piping bureaucracy can dehumanize every employee -- the cog-in-the-wheel problem. What is often misunderstood about technology is that its greatest strength is in making quick analyses, especially complex ones with many potential inputs and outputs (e.g., algorithms). When the analyses are performed and delivered through technology, we call it analytics. Ironically, very few organizations leverage analytics well. They may have an analytic capability for a specific analysis, report, or routine question, but this is just the beginning of what is possible. Analytics can increase in both complexity and effectiveness. For example, descriptive analytics, such as how many patients were seen at a hospital last month, is low complexity and low effectiveness. As the effectiveness increases, so does the complexity. More advanced analytics include accounting measures, diagnostic statistics, predictive forecasting, and simulated prescriptions. At each level, the need for information increases, and the mathematics in support of the analytics is more intense. Even more effective, are automated signals, automated decisions, and then artificial intelligence. These all have similar effectiveness, in that they use embedded analytics to resolve questions as there is an indication of the need for an answer. The question may not even be asked, it is simply required because of the data. What is notable is that these three are increasingly complex, and as a result, expensive. So, the most cost-effective analytics are automate signals and decisions. What this all means in the future of work, is that analytic executives have an incentive to engineer analytic technologies that are full of advanced algorithms, but as these support the work of specialized operators. There is little incentive to adopt artificial intelligence. Too complex, not much more effective. The well-fitted analytics are often called cognitive technologies. The best are those that meet the needs of operators and executives. This future of work may be less layers of managers, thus less bureaucracy. Aplin Labs focuses on these 5 goals in our cognitive technologies: Advanced algorithms to drive dynamic transactions Assessments to pinpoint causes of desired outcomes Automation and augmentation of routine tasks, analyses, and documents Optimized user signals to drive rapid tasks and management functions Anticipate changes in conditional factors (e.g., legal compliance) We see the future of work as a customization of these 5 goals.

Justifying your plan for cognitive technologies