Article | July 13, 2021
We are living in the age of Big Data, and data has become the heart and the most valuable asset for businesses across industry verticals. In the hyper-competitive market that exists today, data acts as a major contributor to achieving business intelligence and brand equity. Thus, effective data management is the key to accelerating the success of businesses. For effective data management to take place, organizations must ensure that the data that is used is accurate and reliable. With the advent of AI, businesses can now leverage machine learning to predict outcomes using historical data. This is called predictive analytics. With predictive analytics, organizations can predict anything from customer turnover to forecasting equipment maintenance. Moreover, the data that is acquired through predictive analytics is of high quality and very accurate. Let us take a look at how AI enables accurate data prediction and helps businesses to equip themselves for the digital future.
BIG DATA MANAGEMENT
Article | July 13, 2021
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 | July 13, 2021
Data has settled into regular business practices. Executives in every industry are looking for ways to optimize processes through the implementation of data. Doing business without analytics is just shooting yourself in the foot.
Yet, global business efforts to embrace data-transformation haven't had resounding success. There are many reasons for the challenging course, however, people and process management has been cited as the common thread.
A combination of people touting data as the “new oil” and everyone scrambling to obtain business intelligence has led to information being considered an end in itself. While the idea of becoming a data-driven organization is extremely beneficial, the execution is often lacking. In some areas of business, action over strategy can bring tremendous results.
However, in data governance such an approach often results in a hectic period of implementations, new processes, and uncoordinated decision-making. What I propose is to proceed with a good strategy and sound data governance principles in mind.
Auditing data for quality
Within a data governance framework, information turns into an asset. Proper data governance is essentially informational accounting. There are numerous rules, regulations, and guidelines to make governance ensure quality.
While boiling down the process into one concept would be reductionist, by far the most important topic in all information management and governance is data quality. Data quality can be loosely defined as the degree to which data is accurate, complete, timely, consistent, adherent to rules and requirements, and relevant.
Generally, knowledge workers (i.e. those who are heavily involved in data) have an intuitive grasp of when data quality is lacking. However, pinpointing the problem should be the goal. Only if the root cause, which is generally behavioral or process-based rather than technical, of the issue is discovered can the problem be resolved.
Lack of consistent data quality assurance leads to the same result with varying degrees of terribleness - decision making based on inaccurate information. For example, mismanaging company inventory is most often due to lack of data quality. Absence of data governance is all cost and no benefit. In the coming years, the threat of a lack of quality assurance will only increase as more businesses try to take advantage of data of any kind.
Luckily, data governance is becoming a more well-known phenomenon. According to a survey we conducted with Censuswide, nearly 50% of companies in the financial sector have put data quality assurement as part of their overall data strategy for the coming year.
Data governance prerequisites
Information management used to be thought of as an enterprise-level practice. While that still rings true in many cases today, overall data load within companies has significantly risen in the past few years. With the proliferation of data-as-a-service companies and overall improvement in information acquisition, medium-size enterprises can now derive beneficial results from implementing data governance if they are within a data-heavy field.
However, data governance programs will differ according to several factors. Each of these will influence the complexity of the strategy:
Business model - the type of organization, its hierarchy, industry, and daily activities.
Content - the volume, type (e.g. internal and external data, general information, documents, etc.) and location of content being governed.
Federation - the extent and intensity of governance.
Smaller businesses will barely have to think about the business model as they will usually have only one. Multinational corporations, on other hand, might have several branches and arms of action, necessitating different data governance strategies for each.
However, the hardest prerequisite for data governance is proving its efficacy beforehand. Since the process itself deals with abstract concepts (e.g. data as an asset, procedural efficiency), often only platitudes of “improved performance” and “reduced operating costs” will be available as arguments. Regardless of the distinct data governance strategy implemented, the effects become visible much later down the line. Even then, for people who have an aversion to data, the effects might be nearly invisible.
Therefore, while improved business performance and efficiency is a direct result of proper data governance, making the case for implementing such a strategy is easiest through risk reduction. Proper management of data results in easier compliance with laws and regulations, reduced data breach risk, and better decision making due to more streamlined access to information.
“Why even bother?”
Data governance is difficult, messy, and, sometimes, brutal. After all, most bad data is created out of human behavior, not technical error. That means telling people they’re doing something wrong (through habit or semi-intentional action). Proving someone wrong, at times repeatedly, is bound to ruffle some feathers.
Going to a social war for data might seem like overkill. However, proper data governance prevents numerous invisible costs and opens up avenues for growth. Without it, there’s an increased likelihood of:
Costs associated with data. Lack of consistent quality control can lead to the derivation of unrealistic conclusions. Noticing these has costs as retracing steps and fixing the root cause takes a considerable amount of time. Not noticing these can cause invisible financial sinks.
Costs associated with opportunity. All data can deliver insight. However, messy, inaccurate, or low-quality data has its potential significantly reduced. Some insights may simply be invisible if a business can’t keep up with quality.
As data governance is associated with an improvement in nearly all aspects of the organization, its importance cannot be overstated. However, getting everyone on board and keeping them there throughout the implementation will be painful. Delivering carefully crafted cost-benefit and risk analyses of such a project will be the initial step in nearly all cases.
Luckily, an end goal to all data governance programs is to disappear. As long as the required practices and behaviors remain, data quality can be maintained. Eventually, no one will even notice they’re doing something they may have considered “out of the ordinary” previously.
Article | July 13, 2021
In today’s digital revolution, the realm of data is growing at an unprecedented rate and will continue to rise as businesses will leverage more smart technologies or devices. However, maintaining and processing these myriad amounts of data require massive computing power and the knowledge to use it. Moreover, companies these days are utilizing data to make data-driven decisions and this pursuit of data-driven decision-making can make them to seek out data science.