Law Enforcement & Public Safety


How U.S. Weapons Ended Up Hitting Hospitals in Yemen

NYT > Middle East

Visual Investigations Latest Video 10:45 How U.S. Weapons Ended Up Hitting Hospitals in Yemen Visual Investigations Latest Video 5:44 The U.S. Blamed Maduro for Burning Aid to Venezuela. Visual Investigations Latest Video 8:57 How an Elite Nigerian Unit Killed Dozens of Protesters Visual Investigations Latest Video 6:52 A Black Driver, a Marijuana Bust and a Body Camera That Turned Off Visual Investigations Latest Video 8:32 Killing Khashoggi: How a Brutal Saudi Hit Job Unfolded Visual Investigations Latest Video 1:31 The Bomb Suspect's Van Is Covered With Stickers. Visual Investigations Latest Video 7:07 How a Gang Hunted and Killed a 15-Year-Old in the Bronx Visual Investigations Latest Video 1:48 How a C.I.A. Drone Base Grew in the Desert Visual Investigations Latest Video 1:49 How Surveillance Cameras Tracked Two Russian Hit Men Visual Investigations Latest Video 3:04 How the Drone Attack on Maduro Unfolded in Venezuela Visual Investigations Latest Video 5:44 The U.S. Blamed Maduro for Burning Aid to Venezuela. The U.S. Blamed Maduro for Burning Aid to Venezuela. Visual Investigations Latest Video 1:31 The Bomb Suspect's Van Is Covered With Stickers.


Introduction to Anomaly Detection using Machine Learning with a Case Study

#artificialintelligence

A common need when you are analyzing real-world data-sets is determining which data point stand out as being different to all others data points. Such data points are known as anomalies. This article was originally published on Medium by Davis David. In this article, you will learn a couple of Machine Learning-Based Approaches for Anomaly Detection and then show how to apply one of these approaches to solve a specific use case for anomaly detection (Credit Fraud detection) in part two. A common need when you analyzing real-world data-sets is determining which data point stand out as being different to all others data points.


Machine Learning in Anti-Money Laundering

#artificialintelligence

The IIF surveyed 59 institutions (54 banks and 5 insurers) on their exploration and adoption of Machine Learning techniques in Anti-Money Laundering. While the detailed version of our resultant report is limited in its distribution to the regulatory community and those 59 firms, a short-form summary report has also been prepared for public distribution. This study covers the particular purposes of application in the AML space, as well as which types of specific techniques are in scope, firms' maturity in adopting, benefits, challenges and model governance. Our findings indicated that the application of machine learning techniques in AML is spreading quickly across the industry, driven by a dedication to build a stronger and more effective defense system against illicit activity. Significantly, none of the 59 surveyed firms were pursuing machine learning as a means to reduce staff, but rather to gain greater and faster insights that can be made available for their trained AML analysts.


FRT 35: Machine Learning in AML

#artificialintelligence

Sarah Runge, Global Head of Financial Crime Compliance Regulatory Strategy for Credit Suisse, joins us on this week's episode of FRT to discuss the benefits and challenges of applying Machine Learning in Anti-Money Laundering and Countering Terrorism Financing (AML/CTF). Sarah highlights the potential that enhanced analytics hold to strengthen the defense mechanisms against financial crime. Today's framework and practices lead to inefficiencies that can make one lose sight of the bigger picture. However, we also explore how technology cannot (and should not) replace the human element and vigilance in a financial institution's safeguarding measures. It should be seen as a way to empower analysts and focus their resources on the cases that need their attention the most.


How U.S. Weapons Ended Up Hitting Hospitals in Yemen

NYT > Middle East

Visual Investigations Latest Video 10:43 How U.S. Weapons Ended Up Hitting Hospitals in Yemen Visual Investigations Latest Video 5:44 The U.S. Blamed Maduro for Burning Aid to Venezuela. Visual Investigations Latest Video 8:57 How an Elite Nigerian Unit Killed Dozens of Protesters Visual Investigations Latest Video 6:52 A Black Driver, a Marijuana Bust and a Body Camera That Turned Off Visual Investigations Latest Video 8:32 Killing Khashoggi: How a Brutal Saudi Hit Job Unfolded Visual Investigations Latest Video 1:31 The Bomb Suspect's Van Is Covered With Stickers. Visual Investigations Latest Video 7:07 How a Gang Hunted and Killed a 15-Year-Old in the Bronx Visual Investigations Latest Video 1:48 How a C.I.A. Drone Base Grew in the Desert Visual Investigations Latest Video 1:49 How Surveillance Cameras Tracked Two Russian Hit Men Visual Investigations Latest Video 3:04 How the Drone Attack on Maduro Unfolded in Venezuela Visual Investigations Latest Video 5:44 The U.S. Blamed Maduro for Burning Aid to Venezuela. The U.S. Blamed Maduro for Burning Aid to Venezuela. Visual Investigations Latest Video 1:31 The Bomb Suspect's Van Is Covered With Stickers.


Facial recognition tech prevents crime, police tell UK privacy case

The Guardian

Facial recognition cameras prevent crime, protect the public and do not breach the privacy of innocent people whose images are captured, a police force has argued. Ed Bridges, an office worker from Cardiff, claims South Wales police violated his privacy and data protection rights by using facial recognition technology on him. But Jeremy Johnson QC compared automated facial recognition (AFR) to the use of DNA to solve crimes and said it would have had little impact on Bridges. Johnson, representing the police, said: "AFR is a further technology that potentially has great utility for the prevention of crime, the apprehension of offenders and the protection of the public." The technology maps faces in a crowd and then compares them with a watch list of images, which can include suspects, missing people and persons of interest to the police.


Adopt Artificial Intelligence to improve operational efficiency in financial services sector

#artificialintelligence

The explosion of emerging technologies such as artificial intelligence (AI) is dramatically changing the way businesses operate today. As businesses collect more and more data, the need for solutions to drive true value from that data grows in importance. AI, in conjunction with big data and analytics, can deliver that baseline value and go beyond traditional solutions to find deeper insights. In India, banks are fast moving in this direction and deploying AI-powered chatbots for their operations to gain better insights into their customers' usage patterns, offer customised products, help in detecting fraudulent transactions and improving operational efficiency amongst others. There is no denying that AI helps banks nurture their relationships through better interactions with their customers however, not without challenges.


The 'inner pickpocket' trait inside all of us lets us tell what an object is by touch alone

Daily Mail - Science & tech

Researchers have identified how the human brain is able to determine the properties of a particular object from touch alone, a so-called inner pickpocket trait. This so-called inner pickpocket trait is inherent in all of us, they say, and is the reason a thief can pilfer a handbag and instantly pull out the most valuable item. It relies on the brain's ability to break up a continuous stream of information and turn it into smaller chunks. This manifests itself for professional pickpockets as being bale to interpret the sequence of small depressions on their fingers separate well-defined objects. 'Notably, the participants in our study were not selected for being professional pickpockets - so these results also suggest there is a secret, statistically savvy pickpocket in all of us,' said Professor Máté Lengyel from the University of Cambridge, who co-led the research.


AI and machine learning will throw bigger punches at ad fraud

#artificialintelligence

In a poll conducted by Integral Ad Science (IAS) 69.0% of agency executives said that ad fraud was the biggest hindrance to ad budget growth, compared with more than half (52.6%) of brand professionals who said the same. How much is ad fraud costing advertisers? Nobody knows, but with estimates ranging from $6.5 billion to $19 billion, there's a lot at stake. Marketers are becoming more assertive in their demands for better fraud prevention measures and they are seeking to increase their knowledge of different fraud types – from bots to unauthorised domain reselling – and wider technology adoptions to drive their Marketing strategies overall. Ad tech providers will need to adapt their technology and techniques to meet this demand.


AI and machine learning will throw bigger punches at ad fraud

#artificialintelligence

In a poll conducted by Integral Ad Science (IAS) 69.0% of agency executives said that ad fraud was the biggest hindrance to ad budget growth, compared with more than half (52.6%) of brand professionals who said the same. How much is ad fraud costing advertisers? Nobody knows, but with estimates ranging from $6.5 billion to $19 billion, there's a lot at stake. Marketers are becoming more assertive in their demands for better fraud prevention measures and they are seeking to increase their knowledge of different fraud types – from bots to unauthorised domain reselling – and wider technology adoptions to drive their Marketing strategies overall. Ad tech providers will need to adapt their technology and techniques to meet this demand.