public safety


How Deep Learning is Transforming the Insurance Industry - Appen

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Fraud detection is another important application for machine learning in insurance. With the amount of payment channels on the rise leading to rapid growth in the number of overall transactions occurring worldwide, machine learning algorithms are used to develop automated fraud screening systems that are faster and more accurate than systems that rely on transaction rules combined with human reviews. Machine learning distinguishes between normal and fraudulent behavior, and adapts over time based on variations of fraud patterns in the data. This is the true power of machine learning as compared to traditional analytics methods -- the ability to detect types of fraud that are similar but not identical to existing patterns, as well as the ability to spot completely new types of fraud altogether.


How Deep Learning is Transforming the Insurance Industry - Appen

#artificialintelligence

Fraud detection is another important application for machine learning in insurance. With the amount of payment channels on the rise leading to rapid growth in the number of overall transactions occurring worldwide, machine learning algorithms are used to develop automated fraud screening systems that are faster and more accurate than systems that rely on transaction rules combined with human reviews. Machine learning distinguishes between normal and fraudulent behavior, and adapts over time based on variations of fraud patterns in the data. This is the true power of machine learning as compared to traditional analytics methods -- the ability to detect types of fraud that are similar but not identical to existing patterns, as well as the ability to spot completely new types of fraud altogether.


How AI Can Help with the Detection of Financial Crimes 7wData

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Paige Dickie develops Artificial Intelligence (AI) and digital strategy for Canada's banking sector at the Vector Institute for Artificial Intelligence in Toronto. She began her career in management consulting -- much to the disappointment of her father, an engineer -- because she had earned advanced engineering degrees in biomedical and mechanical engineering. Dickie initially worked at McKinsey, the global consulting firm, helping multinational financial institutions across a range of fields from data strategy and digital transformation to setting up innovation centers. She recently joined Vector to lead what she describes as "an exciting project with Canada's banking industry. It's an industry-wide, sector-wide, country-wide initiative where we have three different work streams -- a consortium work stream, a regulatory work stream, and a research-based work stream."


Robotic Process Automation - Shamrock Solutions Professional Services & Software for Content Management

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RPA technology, sometimes called a software robot or bot, mimics a human worker, logging into applications, entering or consuming data, calculating and completing tasks, then logging out. Use case example: As a virtual account analyst, a software robot takes a bank application form, logs into a credit check site, a background check site, a known terrorist database, and a criminal database, collecting results and snippets and automatically building a background profile on the applicant for final review. IPA is the application of artificial intelligence and machine learning to process technology to create an enhanced solution. The technologies included in an overall solution might be RPA, BPM/Workflow and the use of image recognition and machine learning. UI Path defines 6 distinct categories of Intelligent Automation skills that can be leveraged in RPA shown in the graphic below.


How AI can combat the growing menace of trade-based money laundering

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The menace of trade-based money laundering (TBML) is an increasing, yet often under-reported, financial and reputational risk to banks and a growing concern to governments and regulators. Transnational crime is worth up to $2.2trn each year and much of it is facilitated by various forms of trade-based money laundering. A PWC report stated that 80 percent of illicit financial flows from developing countries are accomplished through trade-based money laundering. With sums of this magnitude, it is not surprising that banks, who are often the (unwitting) facilitators of this illegal activity, are coming under increasing pressure from regulators to take greater action to limit this growing international crime. For banks, TBML, disguised under the huge volumes of legitimate trade, is also extremely difficult to detect.


As Amazon Ring Partners With Law Enforcement on Surveillance Video, Privacy Concerns Mount

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While Amazon takes special care to position its Ring video doorbell product as a friendly, high-tech version of the traditional "neighborhood watch," U.S. lawmakers and privacy advocates are becoming increasingly skeptical. As they see it, Amazon Ring is putting into place few if any safeguards to protect personal privacy and civil rights. Now that Amazon Ring is partnering with hundreds of law enforcement and police agencies around the nation to share surveillance video, these privacy concerns are only mounting. In November, Amazon Ring released new details about its surprisingly extensive partnership agreements with law enforcement agencies. This update is a follow-up to a Washington Post article outlining Amazon Ring's new partnerships with law enforcement.


Global Big Data Conference

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For CISOs looking to leverage the latest and most effective defenses against increasingly relentless cyber adversaries, implementing artificial intelligence and machine learning solutions can feel like the moment when evidence in a crime scene is revealed by a black light. Suddenly, the great lengths taken to conceal criminal activity can be detected and eliminated -- in real time. Without a deeper understanding of the role AI can play in a security strategy, however, it is all too easy to fall into the trap of seeing it as a magical security solution. Not only will this severely limit the very real benefits that AI offers, but it will also make CISOs and their organizations more susceptible to overvaluing their AI in a way that makes them less safe. Similarly, organizations could easily overinvest in an AI that doesn't really provide the security benefits they think they are receiving.


Kolkata Police To Use AI In Crime Detection

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The land of art, literature and culture will now see the government authorities keeping a close eye on the lawbreakers. In a move that will empower the law enforcement authorities, the Kolkata Police is now expanding the footprint of the CCTV cameras AI-powered devices in crime detection. According to a noted news wire, Kolkata Police has already installed 3,000 closed-circuit cameras all across the city. Police Commissioner Anuj Sharma said, "We are expanding it. Recently, you have seen instances of crime detection by analysing the CCTV footage… With the installation of such cameras, catching those indulging in anti-social acts will become simpler."


Cold Case: The Lost MNIST Digits

Neural Information Processing Systems

Although the popular MNIST dataset \citep{mnist} is derived from the NIST database \citep{nist-sd19}, precise processing steps of this derivation have been lost to time. We propose a reconstruction that is accurate enough to serve as a replacement for the MNIST dataset, with insignificant changes in accuracy. We trace each MNIST digit to its NIST source and its rich metadata such as writer identifier, partition identifier, etc. We also reconstruct the complete MNIST test set with 60,000 samples instead of the usual 10,000. Since the balance 50,000 were never distributed, they enable us to investigate the impact of twenty-five years of MNIST experiments on the reported testing performances.


Using CD with machine learning models to tackle fraud

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Credit card fraudsters are always changing their behavior, developing new tactics. For banks, the damage isn't just financial; their reputations are also on the line. So how do banks stay ahead of the crooks? For many, detection algorithms are essential. Given enough data, a supervised machine learning model can learn to detect fraud in new credit card applications. This model will give each application a score -- typically between 0 and 1 -- to indicate the likelihood that it's fraudulent. The banks can then set a threshold for which they regard an application as fraudulent or not -- typically that threshold will enable the bank to keep false positives and false negatives at a level it finds acceptable. False positives are the genuine applications that have been mistaken as fraud; false negatives are the fraudulent applications that are missed.