What is Big Data Analytics?
Today from all the transactions we carry out online, a large amount of data called big data is in the flow. Companies need to analyze this data and make some decisions. Manually classifying this data and making sense of it would waste a lot of time and money, so we can say that it is almost impossible. Thanks to big data analytics, data that have different sizes and different structures can be analyzed with different techniques, and it makes the use of data easy and fast. Now let’s take a look at big data analytics, the role of big data analytics in fraud detection, and some of the problems that data analytics creates in fraud detection.
Why is Big Data Analytics Important?
In this age where the amount of data has reached an incredible size, we constantly hear about big data analytics in every business and every system installed. So what is the importance of big data analytics for companies?
Big data analytics facilitates the analysis of large amounts of data. Instead of making decisions by analyzing large amounts of data over a long period, much faster decisions can be made with this data processing with certain techniques. Some foresight may be required for the decisions to be made and at this point, big data analytics makes it easier to make predictions. Thanks to big data analytics, we can better understand customer requests. For example, the most visited sites, products, and the most sold products can be identified, and the data obtained from a survey on consumer opinions can be analyzed. So that appropriate products and services can be developed and improved.
In addition, this processed and analyzed data can be used to develop algorithms that will reduce the workload or time spent in the company. With determined patterns, it protects the company from harm by detecting anomalies in the data. Although big data analytics is now used in every field, one of the most important fields today is the banking and fintech sectors.
The Importance of Big Data Analytics in Terms of Fraud Prevention:
As online purchase, payment, and money transfer transactions increase, the risks of fraud that may occur through these transactions also increase. It was very difficult for companies to process and analyze the huge amount of data that emerged from these transactions and use it in fraud detection. At this point, we come across an indispensable facilitating tool: big data analytics for fraud detection. Using big data analytics in some points of fraud detection provides many advantages.
One of the most important points when detecting fraud is to take action quickly. It may take a long time to identify the suspicious ones among this large number of irregular data resulting from transactions.
Some transactions may be perceived as suspicious by misinterpretations as a result of these long analyses. During this evaluation process, there will still be a need for people, namely a manual workload, to analyze the data and check for suspicious transactions or misinterpretations.
To protect the company and customers from harm, it is necessary to draw up rules based on this data and look at past fraudulent activities, so that we can establish systems that can prevent possible damages and frauds that may occur.
All these mean more cost, time, and manual work. Big data analytics plays the biggest helping role in solving these issues. Using data analyzed with techniques in big data analytics can provide:
- Low costs
- More accurate and precise detections
- Optimized workflows and efficiency of systems
- Better services to customers
In addition, data mining and machine learning made by big data analytics are used in fraud analytics. These tools enable the implementation of payment fraud analytics, financial fraud analytics, and insurance fraud detection analytics.
What are the Common Problems in Big Data Analytics in Fraud Detection?
We mentioned the importance of big data analytics in detecting fraud. Although it makes it easier to detect fraud, it can also bring some problems with it. Some of these problems can be listed as:
Unrelated or Insufficient Data
The data from the transactions may come from many different sources. In some cases, false results can be obtained in fraud detection due to insufficient or irrelevant data. Detection can be based on the inappropriate rules used in the algorithm. Because of this risk of failure, companies may be hesitant to use big data analytics and machine learning.
High Costs
Big data analytics and fraud detection systems may cause some costs such as the cost of software, and hardware systems, the cost of components used for the sustainability of these systems, and the time spent.
Dynamic Fraud Methods
As technology develops, fraud methods develop at the same pace. In order to catch this speed and detect fraud, it is necessary to constantly monitor the data and give rules to the algorithms with new and accurate data analytics.
Data Security
While processing the data and making decisions with this data analytics system, the security of the data is also a problem to be considered. That means the security of data should be checked.
Solutions To Big Data Analytics Problems
It is necessary to separate unnecessary data by processing complex data coming from many channels with certain analyses and big data analytics. This organized, prepared data is given to the algorithms. These algorithms ensure that fraudulent transactions are detected and quick action is taken.
Monitoring access to this data, reports, and alarms from a single tool with easy and visualized dashboards prevents wasting money and time. Even if you pay for this tool in the first place, invest in it, it will provide much more benefit than what is paid to you in the long run by preventing fraudulent transactions detected with these tools.
In conclusion, an engineering system should be established to analyze big data and manage and control its analytics. It is necessary to ensure data security by including cyber security experts. Most importantly, it will provide many benefits to use software such as Formica, which will provide features such as data processing, analysis, inference, and alarming in the field of fraud within the company and will prevent time and effort spent by helping analysts and engineers.
