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 | April 30, 2020
In the present complex and volatile market with data as a nucleus, analytics becomes a core function for any enterprise that relies on data-driven insights to understand their customers, trends, and business environments.
In the age of digitization and automation, it is only sensible to make a move to analytics for a data-driven approach for your business. While a host of sources including Digital clicks, social media, POS terminal, and sensors enrich the data quality, data can be collected along various stages of interactions, and initiatives were taken. Customers leave their unique data fingerprint when interacting with the enterprise, which when put through analytics provides actionable insights to make important business decisions.
Table of Contents:
Business Analytics or Business Intelligence: The Difference
Growth Acceleration with Business Analytics
Business Analytics or Business Intelligence (BI): The Difference
Business Intelligence comes within the descriptive phase of analytics. BI is where most enterprises start using an analytics program. BI uses software and services to turn data into actionable intelligence that helps an enterprise to make informed and strategic decisions.
It’s information about the data itself. It’s not trying to do anything beyond telling a story about what the data is saying.
- Beverly Wright, Executive Director, Business Analytics Center, Georgia Tech’s Scheller College of Business
Some businesses might use BI and BA interchangeably, though some believe BI to be the know-how of what has happened, while the analytics or advanced analytics work to anticipate the various future scenarios.
BI uses more structured data from traditional enterprise platforms, such as enterprise resource planning (ERP) or financial software systems, and it delivers views into past financial transactions or other past actions in areas such as operations and the supply chain. Today, experts say BI’s value to organizations is derived from its ability to provide visibility into such areas and business tasks, including contractual reconciliation.
Someone will look at reports from, for example, last year’s sales — that’s BI — but they’ll also get predictions about next year’s sales — that’s business analytics — and then add to that a what-if capability: What would happen if we did X instead of Y.
- CindiHowson, research vice president at Gartner
A subset of business intelligence (BI), business analytics is implemented to determine which datasets are useful and how they can be leveraged to solve problems and increase efficiency, productivity, and revenue. It is the process of collating, sorting, processing, and studying business data, and using statistical models and iterative methodologies to transform data into business insights. BA is more prescriptive and uses methods that can analyze data, recognize patterns, and develops models that clarify past events, make future predictions, and recommend future discourse.
Analysts use sophisticated data, quantitative analysis, and mathematical models to provide a solution for data-driven issues. To expand their understanding of complex data sets, and artificial intelligence, deep learning, and neural networks to micro-segment available data and identify patterns they can utilize statistics, information systems, computer science, and operations research.
Let’s discuss the 5 ways business analytics can help you accelerate your business growth.
READ MORE: HOW TO OVERCOME CHALLENGES IN ADOPTING DATA ANALYTICS
Growth Acceleration with Business Analytics
1. Expansion planning
Let’s say you’re planning an expansion opening a branch, store, restaurant, or office in a new location and have accumulated a lot of information about your growing customer base, equipment or other asset maintenance, employee payment, and delivery or distribution schedule. What if we told it is possible to get into a much detailed planning process with all that information available? It becomes possible with business analytics. With BA you can find insights in visualizations and dashboards and then research them further with business intelligence and reports. Moreover, you can interact with the results and use the information to create your expansion plan.
2. Finding your audience
You’re right to examine your current customer data but you should also be looking into the customer sentiments towards your brand and who is saying what, and in what parts of the region. Business Analytics offers social media analysis so you can bring together internal and external customer data to create a profile of your customers, both existing and potential. Thus, you have prepared an ideal demographic, which can be used to identify people that are most likely to turn to your products or services. As a result, you have successfully deduced the area that offers the most in terms of expansion and customer potential.
3. Creating your business plan
The real-time interaction with your data provides a detailed map of the current progress as well as your performance. Business Analytics solutions offer performance indicators to find and forecast trends in sales, turnover, and growth. This can be used in the in-depth development of a business plan for the next phase of your thriving franchise.
4. Developing your marketing campaign
With Business Analytics, you’re capable of sending the right message to the audience most eager to try your product/service as part of a marketing campaign. You’re empowered to narrow down branding details, messaging tone and customer preferences, like the right offers that will differentiate you from the other businesses in the area. Using BA, you have gained a competitive edge by making sure you offer something new to your customers and prospects. It enables you to use your data to derive customer insights, make insight-driven decisions, do targeted marketing, and make business development decisions with confidence.
5. Use predictive insights to take action
With analytics tools like predictive analytics, your expansion plans are optimized. It enables you to pinpoint and research about the factors that are influencing your outcomes so that you can be assured of being on the right track. When you can identify and understand your challenges quickly and resolve them faster, you improve the overall business performance resulting in successful expansion and accelerated growth.
READ MORE: WHAT IS THE DIFFERENCE BETWEEN BUSINESS INTELLIGENCE, DATA WAREHOUSING AND DATA ANALYTICS
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 | March 30, 2020
Most businesses do not have contingency or business continuity plans that correlate to the world we see unfold before us—one in which we seem to wake up to an entirely new reality each day. Broad mandates to work at home are now a given. But how do we move beyond this and strategically prepare for—and respond to—business implications resulting from the coronavirus pandemic? Some of our customers are showing us how. These organizations have developed comprehensive, real-time operational intelligence views of their global teams—some in only 24-48 hours—that help them better protect their remote workforces, customers, and business at hand.