Article | March 5, 2020
Do you know the real importance of Big Data in the Food Industry? Knowing your audience is important, even fundamental for any kind of business. In this article we will analyze the best practices and the best data-driven strategies (marketing, but not only) for the food industry. Food and Beverage is a large and complex sector that embraces a number of very different players, some of whom are interconnected. The ecosystem includes both small producers and large multinational brands, players who cater to everyone and those who target a specific niche; then there are the distributors, clubs, restaurants both small and large, and retail chains.
Article | February 17, 2020
In recent years, artificial intelligence research and applications have accelerated at a rapid speed. Simply saying your organization will incorporate AI isn’t as specific as it once was. There are diverse implementation options for AI, Machine Learning, and Deep Learning, and within each of them, a series of different algorithms you can leverage to improve operations and establish a competitive edge. Algorithms are utilized across almost every industry. For example, to power the recommendation engines in all media platforms, the chatbots that support customer service efforts at scale, and the self-driving vehicles being tested by the world’s largest automotive and technology companies. Because of how diverse AI has become and the many ways in which it works with data, companies must carefully evaluate what will work best for them.
Article | December 16, 2020
In this article, we will explore different techniques to detect money laundering activities.
Notwithstanding, regardless of various expected applications inside the financial services sector, explicitly inside the Anti-Money Laundering (AML) appropriation of Artificial Intelligence and Machine Learning (ML) has been generally moderate.
What is Money Laundering, Anti Money Laundering?
Money Laundering is where someone unlawfully obtains money and moves it to cover up their crimes.
Anti-Money Laundering can be characterized as an activity that forestalls or aims to forestall money laundering from occurring.
It is assessed by UNO that, money-laundering exchanges account in one year is 2–5% of worldwide GDP or $800 billion — $3 trillion in USD. In 2019, regulators and governmental offices exacted fines of more than $8.14 billion.
Indeed, even with these stunning numbers, gauges are that just about 1 % of unlawful worldwide money related streams are ever seized by the specialists.
AML activities in banks expend an over the top measure of manpower, assets, and cash flow to deal with the process and comply with the guidelines.
What are the punishments for money laundering?
In 2019, Celent evaluated that spending came to $8.3 billion and $23.4 billion for technology and operations, individually. This speculation is designated toward guaranteeing anti-money laundering.
As we have seen much of the time, reputational costs can likewise convey a hefty price. In 2012, HSBC laundering of an expected £5.57 billion over at least seven years.
What is the current situation of the banks applying ML to stop money laundering?
Given the plenty of new instruments the banks have accessible, the potential feature risk, the measure of capital involved, and the gigantic expenses as a form of fines and punishments, this should not be the situation.
A solid impact by nations to curb illicit cash movement has brought about a huge yet amazingly little part of money laundering being recognized — a triumph rate of about 2% average.
Dutch banks — ABN Amro, Rabobank, ING, Triodos Bank, and Volksbank announced in September 2019 to work toward a joint transaction monitoring to stand-up fight against Money Laundering.
A typical challenge in transaction monitoring, for instance, is the generation of a countless number of alerts, which thusly requires operation teams to triage and process the alarms.
ML models can identify and perceive dubious conduct and besides they can classify alerts into different classes such as critical, high, medium, or low risk. Critical or High alerts may be directed to senior experts on a high need to quickly explore the issue.
Today is the immense number of false positives, gauges show that the normal, of false positives being produced, is the range of 95 and 99%, and this puts extraordinary weight on banks.
The examination of false positives is tedious and costs money. An ongoing report found that banks were spending near 3.01€ billion every year exploring false positives.
Establishments are looking for increasing productive ways to deal with crime and, in this specific situation, Machine Learning can end up being a significant tool.
Financial activities become productive, the gigantic sum and speed of money related exchanges require a viable monitoring framework that can process exchanges rapidly, ideally in real-time.
What are the types of machine learning algorithms which can identify money laundering transactions?
Supervised Machine Learning, it is essential to have historical information with events precisely assigned and input variables appropriately captured. If biases or errors are left in the data without being dealt with, they will get passed on to the model, bringing about erroneous models.
It is smarter to utilize Unsupervised Machine Learning to have historical data with events accurately assigned. It sees an obscure pattern and results. It recognizes suspicious activity without earlier information of exactly what a money-laundering scheme resembles.
What are the different techniques to detect money laundering?
K-means Sequence Miner algorithm: Entering banking transactions, at that point running frequent pattern mining algorithms and mining transactions to distinguish money laundering. Clustering transactions and dubious activities to money laundering lastly show them on a chart.
Time Series Euclidean distance: Presenting a sequence matching algorithm to distinguish money laundering detection, utilizing sequential detection of suspicious transactions. This method exploits the two references to recognize dubious transactions: a history of every individual’s account and exchange data with different accounts.
Bayesian networks: It makes a model of the user’s previous activities, and this model will be a measure of future customer activities. In the event that the exchange or user financial transactions have.
Cluster-based local outlier factor algorithm: The money laundering detection utilizing clustering techniques combination and Outliers.
For banks, now is the ideal opportunity to deploy ML models into their ecosystem. Despite this opportunity, increased knowledge and the number of ML implementations prompted a discussion about the feasibility of these solutions and the degree to which ML should be trusted and potentially replace human analysis and decision-making.
In order to further exploit and achieve ML promise, banks need to continue to expand on its awareness of ML strengths, risks, and limitations and, most critically, to create an ethical system by which the production and use of ML can be controlled and the feasibility and effect of these emerging models proven and eventually trusted.
Article | February 25, 2020
Internet of Things, according to congressional research service (CRS) report 2020, is a system of interrelated devices connected to a network and/or to one another, exchanging data without necessarily requiring human to machine interaction.The report cites smart factories, smart home devices, medical monitoring devices, wearable fitness trackers, smart city infrastructures, and vehicular telematics as examples of IoT.