Toronto, Canada, September 22, 2022 – Datametrex AI Limited (the "Company" or "Datametrex") (TSXV: DM, FSE: D4G, OTC: DTMXF) is pleased to report that it has received a Purchase Order ("P.O.") from Lotte Card Co. ("Lotte Card") on September 16, 2022, for approximately $500,000 CAD, which is the fifth largest credit card company in Korea. "Datametrex is thrilled to receive another P.O. for half a million dollars from Lotte Card. Continuously expanding our technology services internationally to one of the most prominent organizations in Korea is an achievement. We are proud to prove our land and expansion strategy", said Marshall Gunter, CEO of the Company. Is a financial service company which provides credit card and lending business in Korea.

Machine learning can reduce false positive and quickly detect credit card fraud. Using traditional methods to detect instances of credit card fraud slows down the process of resolving such issues. The application of machine learning in banking promises to find quicker and accurate solutions for all kinds of financial institutions. The advent of digitization in banking has introduced several cybersecurity-related issues in such finance-based organizations. For example, reported financial fraud had increased by 104% in the first quarter of 2020, compared to Q1 2019.

The COVID-19 pandemic has arguably been our era's greatest threat to humanity and the global economy.16 South Korea's first confirmed case was in January 2020, followed by an outbreak in the city of Daegu in mid-February. However, South Korea quickly and effectively contained the pandemic and became an exemplar for other countries.3 While many policies and initiatives contributed to South Korea's successful response to the coronavirus pandemic, digital technology was at the core of the endeavors.12 As part of its 3T strategy (test, trace, and treat) for coping with COVID-19, South Korea deployed a software system that traces the contacts of infected patients and disseminates the information in a matter of minutes.19 The COVID-19 Contact Tracing System CCTS) was first released in March 2020 to the Korea Centers for Disease Control and Prevention (KCDC)--a government agency responsible for advancing public health--and was then rolled out nationally in early April 2020. The system greatly contributed to reducing the number of daily new confirmed cases from 909 on February 29, 2020 to 7.42 on average between April 29 and May 5. According to a recent Columbia University study,13 both South Korea and the U.S. confirmed their first case of coronavirus on the same day. However, as of March 2021, South Korea's total confirmed cases are less than 10,000 and its proportional mortality rate is 50 times smaller than that of the U.S.7 The CCTS helped public healthcare officials to make informed decisions and helped keep the public aware of high-risk places where there had been exposure to coronavirus. Information provided by the CCTS enabled citizens to avoid hot spots and plan outdoor activities accordingly.

Machine learning and artificial intelligence might be the future of everything in the Fintech sector. Generally, integration of AI improves results since the technology applies methods derived from common aspects of human intelligence but is beyond human scale. In this context, AI empowers business processes by providing a deeper understanding of customer needs. The adoption of technology in this sector has substantially made banking easier. People can now carry out major bank-related tasks online, mainly from any device that has an internet connection.

There is a problem with artificial intelligence and that is because it is artificial. No amount of coding can replace human intelligence and more than that, intuition; the ability to see the bigger picture and to make the correct assumptions and decisions from a myriad of factors. Hence the major concerns that the overhyped driverless automobile will never really become a reality. Let us look at a much simpler example, something that happened to me today and that has left me totally frustrated, with the amount of time wasted and the sheer incompetence of the technologists and the lack of understanding of business. A close relative indicated that a certain Apple accessory would be a suitable gift for a coming birthday and given that the closest Apple Store to where I live is some 50 km distant (not to mention all the restrictions relative to the pandemic), I sat myself down to order the item online.

There are few bigger targets for cyber criminals than credit card companies. Which is why the U.S. alone had over 270,000 reports of credit card fraud in 2019, double the 2017 rate. So what's a credit card company to do? Use artificial intelligence to sniff out fraud and block it. "We believe at American Express that we have the world's largest and most advanced machine learning system in the financial services industry," American Express' VP of risk management Anjali Dewan told me recently on the TechFirst podcast. "And these models are ... monitoring 100% of these transactions and returning 8 billion credit and fraud risk decisions in real time."

Credit card frauds are a "still growing" problem in the world. Losses in frauds were estimated in more than US$27 billion in 2018 and are still projected to grow significantly for the next years as this article shows. With more and more people using credit cards in their daily routine, also increased the interest of criminals in opportunities to make money from that. The development of new technologies puts both criminals and credit card companies in a constant race to improve their systems and techniques. With that amount of money at stake, Machine Learning is surely not a new word for credit card companies, which have been investing on that long before it was a trend, to create and optimize models of risk and fraud management.

SparkCognition, a leading industrial artificial intelligence (AI) company, is pleased to announce that Japanese AI and Fintech company, MILIZE Co., Ltd. will offer Japanese financial institutions fraud detection and anti-money laundering solutions. These solutions will be built using the automated machine learning software of SparkCognition. With the enormous increase of online payment, internet banking, and QR code payments, illegal use of credit cards is on the rise. However, there are not many Japanese companies that have introduced advanced solutions for fraud detection that currently exist internationally. In addition, financial authorities and institutions around the world are expected to report strengthened measures against money laundering in August 2020. As a result, taking these steps against money laundering has become an urgent management issue in Japanese financial institutions.

I previously worked on designing some problem sets for a PhD class. One of the assignments dealt with a simple classification problem using data that I took from a kaggle challenge trying to predict fraudulent credit card transactions. The goal of the problem is to predict the probability that a specific credit card transaction is fraudulent. One unforeseen issue with the data was that the unconditional probability that a single credit card transaction is fraudulent is very small. This type of data is known as rare events data, and is common in many areas such as disease detection, conflict prediction and, of course, fraud detection.

How did I get to be all of those things? I wish I could say "I did well in school" or "I always loved computers." But the reality is, in middle/high school, I was bored stiff. At 16, I dropped out of school to begin an illustrious career in office cleaning. The odds were stacked against me entering the computing industry for many reasons. Being female, thanks to a decades-long initiative by the British government to keep women out of computing.