The world became digital. More and more people around the globe turning to online services. Customers use online banking to manage their financial activities and do their transactions via a mobile app, patients share their health data, users share bank accounts while subscribing to the streaming platforms and buyers fill in personal contacts to make a purchase. We all share our data every day. Due to this technological progress, the number of cybercrimes, frauds, hackers attacks, threats, and vulnerabilities of organizations has increased. Thus, not only financial institutions, but every industry must apply comprehensive fraud prevention techniques to protect their customers’ personal data.
An average attack may cost an organization millions. In this article, we are discussing security measures and how business has to conduct fraud management.
Importance of Fraud Monitoring Analytics with Big Data Analytics and Machine Learning
Fraud data analytics is a multidisciplinary approach that combines quantitative sciences to predict and prevent online frauds. Including the use of big data analysis techniques, business intelligence, data mining, machine learning, and AI. This can help organizations in different industries detect suspicious behavior, and apply mitigation of fraudulent activity in real-time.
The process of fraud prevention analytics includes gathering and storing relevant data and mining it for discrepancies. Gained insights that can allow managing potential threats before they occur as well as develop a proactive fraud detection and prevention environment.
There are a couple of reasons why the level of fraud is growing.
- Businesses don’t pay proper attention to security while moving to the public cloud and increasing IoT devices usage.
- After the Pandemic started and companies switched to remote mode, people started using their personal devices (laptops, phones) to access corporate information.
- A Home Wi-Fi network can’t ensure the same level of cybersecurity as an office environment. But also, employees often neglect antivirus, use weak passwords, apply dangerous online tools, etc.
Companies all across the world use data analytics to detect fraud. Deutsche Bank uses big data to identify money laundering, prevent credit card fraud, etc. UK mobile virtual network operator Lebara identified a scam and block SIM cards that were used by fraudsters to call their victims. A healthcare insurance company Santéclair also applies anti-fraud systems and advanced algorithms to detect scams three times more effectively.
Benefits of fraud monitoring analytics
The advantage of using fraud detection analytics is that it can handle a huge amount of data at once. And also it
- helps you identify the area that can be affected by the fraud the most
- fixes weaknesses in the system or business flow
- saves time by automating the repetitive tests
- searches all transactions automatically for fraud indicators
- merges and compares data from different systems
- calculates the impact of fraud
- uncovers new patterns, trends, fraudulent schemes, and scenarios that traditional approaches can’t detect
- speed up analysis and eliminates manual work
Methods of fraud monitoring analytics
To create a very powerful fraud detection system, use the combination of the following methods.
Ad-Hoc
Ad-Hoc is the process of discovering fraud by using a hypothesis. You can test software to find out if it’s capable of resizing dangerous actions e.g. fraudulent transactions.
Sampling
It’s one of the mandatory methods of fraud detection. It is most effective where there is a lot of data population involved. On the other hand, sampling may not be able to fully control fraud detection as it takes only a few populations into consideration. Fraudulent transactions do not occur randomly therefore an organization need to test all the transactions to effectively detect fraud.
Competitive or Repetitive Analysis
This method creates and sets up scripts that use Big Data to run against the big volume of data to identify the frauds over a specified time period. Analysis helps to improve the efficiency of companys’ fraud detection processes.
Analytic techniques
These usually identify abnormal frauds by using statistical parameters and classifying the data and transactions. Look at high and low values and find out the anomalies that are most of the time the indicators of scams.
Gaps
This method finds out the missing sequential data. For example, if you have purchase orders which are issued by the company in sequential order and if anything is missing you can easily find out. This is an easy method and it will work out great if used correctly.
Check how we can assist you with secure banking solutions
How to Use Big Data and Machine Learning for Fraud Prevention and Monitoring Analytics
In order to continue reading the full article, please visit our blog.
Big Data Analytics is a growing field, and many companies are interested in it.
Businesses have understood that they are wasting a golden opportunity by not collecting and analyzing the data they receive from their customers and visitors using Big Data.
It is not just that Big Data as a technology is trend, there are various trends in Big Data as well that are doing the rounds and catering the best piece of the cake to the businesses based on various industries.The growth of big data market is expected to be phenomenal, and according to a report by Frost and Sullivan, the data analytics market is expected to grow at a CAGR of 29% to $40 billion by 2023.This explosive growth has prompted many Big Data Analytics Firms to come up with great solutions.
Having a leading company providing the best Big Data Services to solve your issues and to gain the immense benefits that big data analytics offers can help your business go a long way.Before you dive into the exciting world of Big Data, it is essential to know some basics.Types of Big Data AnalyticsDescriptive AnalyticsDescriptive analytics deals with summarizing raw data and converting it into a form that is easily digestible.
Also, by using descriptive analytics, one can easily infer in detail about an event that has occurred in the past and derives a pattern out of this data.One of the most crucial data analytics, descriptive data analytics helps in revealing critical information about a business.
It is impossible to create ideal Business Intelligence tools and dashboards without conducting robust descriptive analytics.Descriptive analytics helps in addressing some fundamental questions of data analytics (4Ws One H).
The exponential growth of global data that started a decade ago is not going to show any signs of slowing down anytime soon.
Instead of slowing down, it gets increased further through the internet, media files, text messages, social networks, and online searches.
The IoT sensors and devices further add to this share of data, making the Future of Big data something to look forward to.With the world being powered by big data right now, it is making the companies more reliant on the Big Data Experts, with the ability to make the best use of the potential of data processing.With the data volumes reaching unforeseen heights, what does the Future of Big data look like?
Read on to know what Way2Smile's experts have to say about the Future predictions of Big Data.Learn more at Big Data Predictions for 2020-2025.
Big Data Analytics has opened a treasure trove of opportunities for businesses all around the world.
As we know, our present computing system uses binary numbers 0 and 1.
Google gave a set of calculations to Sycamore, and the processor gave the results in 200 seconds, an astonishing feat considering that the same set of calculations would have taken 10,000 years for the fastest supercomputer in the world.
It will help in identifying underlying patterns and taking faster and better decisions related to water management, traffic management, disposal of municipal services, and in other areas.
The human emotions are measured using a variety of sensors and AI, which include gyros, high-speed video cameras(to detect the facial expressions), Accelerometers, Audio, Heart rate Sensors, Skin conductance Sensors, to name a few.
This presents a massive opportunity for companies providing Big Data as a Service (BDaaS).
