By Çağlar Şahin
What are Data Analytics, Big Data, and Artificial Intelligence?
In data analytics, we collect, store, categorize and create insights by conducting analysis processes. Our main goal is to improve the processes by analyzing the collected data.
Big data is data that is rapidly increasing in volume with ever more diversity. This situation is expressed as big data volume, speed, and diversity. Today, financial transactions, payment transactions, GSM operator transactions, data collected by IoT devices, blogs, microblogs, climate and other sensors, logs, etc. are all data sources for big data.
In an ideal approach, artificial intelligence is an artificial operating system with characteristics of human intelligence. It is expected to exhibit higher cognitive functions or autonomous behaviors, such as perception, learning, connecting multiple concepts, thinking, reasoning, problem solving, communication, inference, and decision making. This system should also be able to generate reactions from its own thoughts (agent artificial intelligence) and physically express these reactions.
The high volume and diversity of data that are created every day in payment systems lead us to look into alternative methods such as researching usage and payment habits and researching new methods in data storage.
The Benefits of Artificial Intelligence, Big Data and Data Analytics
Since big data is entirely based on the analysis of real data, it facilitates correct decision making in many different areas, such as reducing costs and increasing productivity, reaching targets in a limited time, saving labor, and developing products that meet expectations.
While AI uses algorithms to automate the processing of data, big data is used to store and organize large amounts of information. Artificial intelligence that combines the two is needed to reveal patterns and insights from data that cannot be spotted by the human eye. AI can be used to improve decision making and to automate processes, resulting in faster and more effective outcomes.
The Use of Artificial Intelligence, Big Data, and Data Analytics in Payment Systems
Insights are generated and reported on the data produced by the applications used in payment systems and the data from gateways. With the use of business intelligence applications in reporting data, users outside of the IT team have the opportunity to create their own reports. Management cockpits can be created so that senior management can be presented with context-appropriate insights that will increase the company’s competitive edge. By storing the data that feeds these systems in central data warehouses, the burden on operational applications from integrations is alleviated, and end-user and management access to fast and consistent data is provided. Thanks to these data warehouse structures, we can store the data that is planned to be stopped for a certain period of time according to the needs of applications, with lower costs.
We can use large data structures, which are formed with the data of intensive transactions and logs in payment systems, to reduce the risk of fraud, increase customer satisfaction, provide cost savings, predict future payment trends, personalize payment methods, and provide a competitive advantage to our customers with the help of various algorithms.
In payment systems, the following benefits can be provided with Artificial Intelligence applications:
Competition for customers continues. Now, the customer experience overview is clearer than ever before. Big data allows you to collect data from social media, web visits, call logs, and other sources to enhance the interaction experience and maximize the value offered. Start offering personalized offers, reduce churn, and proactively address issues.
Fraud and compliance
When it comes to security, you’re dealing with a whole team of experts, not just a few scammers. Security infrastructure and compliance requirements are constantly changing. Big data helps you identify data patterns that combine large volumes of information and indicate fraud in order to make regulatory reporting much faster.
Machine learning is currently a hot topic. And data, especially big data, is one of the reasons machine learning is on the agenda. We can now train machines instead of programming them. The use of big data makes it possible to train machine-learning models.
Although operational efficiency is not always on the agenda, it is the field in which big data has the greatest impact. With big data, production, customer feedback, returns, and other factors can be analyzed and evaluated to reduce outages and predict future demand. Big data can also be used to improve decision making in line with current market demand.
Big data can help you innovate by learning about the synergies between people, institutions, legal entities, and processes, and then identifying new ways to use those insights. Use data insights to improve financial and planning decisions. Introduce new products and services by examining trends and customer expectations. Implement dynamic pricing. You have endless possibilities.