Use Cases Of Big Data In The Banking Industry

Every day, banking users generate massive amounts of data through hundreds of thousands, if not millions, of individual transactions. This data falls under the category of big data, which is defined as "large, diverse sets of information that grow at ever-increasing rates." To give you an idea of how much information is involved, consider that we generate 2.5 quintillion bytes of data every day! This data has enormous potential for banks and other financial institutions looking to better understand their customer base, product performance, and market trends.

 

But where does all this information originate from? The technology underpinning smartphones, tablets, and the Internet of Things (IoT) has made it easier than ever for customers to use internet resources to contact businesses, research products, purchase stuff, and even execute financial operations. These activities are then used to create customer profiles that can detect trends, predict behaviors, and help banks better understand their customers.

 

Also Read | Big Data In Banking Industry: Benefits, Uses and Challenges

 

Brief about Big Data

 

"Big data" refers to the huge amount of data available to organizations that, due to its volume and complexity, is difficult to manage and analyze using typical business intelligence methods. Big data tools can assist with the volume of data collected, the speed with which that data is made available to an organization for analysis, and the complexity or variety of that data.

 

It can be characterized as data sets that are too large or too complex for typical relational databases to capture, manage, and process with low latency. Big data has characteristics such as high volume, high velocity, and high variety. Because of artificial intelligence (AI), mobile devices, social media, and the Internet of Things (IoT), data sources are becoming more complicated than traditional data sources. Sensors, devices, video/audio, networks, log files, transactional applications, online, and social media, for example, generate various forms of data, much of it in real time and on a massive scale.

 

Big data analytics can ultimately enable better and faster decision-making, model and forecast future outcomes, and improve business intelligence. Consider open source software such as Apache Hadoop, Apache Spark, and the full Hadoop ecosystem as cost-effective, flexible data processing and storage tools built to handle the volume of data being generated today as you build your big data solution.

 

Introduction to Big Data in Banking

 

The banking sector is the engine that powers economies, nations, and organizations. It also produces massive volumes of data every second. Every transaction leaves a trace and generates data that was previously thought to be static and only valuable to auditors for the purposes of accounting and auditing. However, as Big Data technologies in other sectors such as Healthcare began to show its full potential, we began to incorporate such "worthless" and "stale" data into those systems and began to genuinely understand the potential of financial insights that could be used for a variety of purposes. 

 

Back in 2008, Big Data and Business Intelligence technology aided in this endeavor and enabled Banking and Financial Institutions to challenge the status quo, kicking off the rise of Big Data in the Banking Sector. Banks employ Big Data and BI technologies such as Hadoop and RDBMS in all of their processes, changing the face of banking for the better. Big Data has helped transform organizations and institutions all around the world, from digitizing all banking procedures to converting developing countries from cash-heavy transactions to digital transactions.

 

Every day, banks must engage with millions of potential customers, and to do so, they require a large amount of data. Banks must cope with a large amount of prospective data as new consumers arrive. There is no scarcity of data in the financial industry. Big Data has emerged as the banking industry's rescuer. 

 

Financial services firms have altered their business practices thanks to Big Data. Big data reduces the risk of detection of fraud, compliance, and portfolio management. This risk reduction, along with the optimisation of the winning approach, has the potential to provide financial services firms with a considerable competitive edge. Big data has enabled corporations that interface with public markets to develop new strategies that go beyond simple improvements. If financial systems and goods get more complex and convoluted, fraudsters may be able to commit fraud. To protect themselves from fraud and risk, financial institutions must fast transition to big data in order to detect and prevent emerging and intricate fraud schemes. 

 

Also Read | Open Banking: Advantages and Disadvantages

 

Use Case of Big data in Banking

 

Big Data is defined as a collection of structured, semistructured, and unstructured data that may be mined for information and used in machine learning, predictive modeling, and other advanced analytics activities. Big Data processing and storage systems, as well as technology that enable Big Data analytics, have become a standard component of commercial data management architectures. Some of the use cases of Big data in banking are:

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Use cases of big data in banking

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1. Customer Segmentation:

 

Big Data assists financial organizations in profiling consumers, allowing them to cater to individual customers based on their banking history and transactional patterns over the period they have been with the bank. This enables them to create tailored plans and solutions for their clients. This boosts customer experience and helps banks differentiate themselves and keep customers. Banks might also recommend different products to different customers based on their demographics.

 

2. Fraud detection and prevention:

 

Machine learning, which is powered by massive amounts of data, greatly aids in fraud identification and prevention. Analytics that analyze shopping trends have helped to lessen credit card security issues. When safe and valuable credit card information is stolen, banks can now immediately freeze the card and the transaction, as well as advise the consumer of the security risk.

 

3. Decisions on Lending:

 

Lending is one of the most important decisions in the banking industry. It is critical to choose the correct customer who is both creditworthy and financially sound to pay off debt. Moreover, historically, banks relied on credit rating organizations to assess a customer's creditworthiness, which may not tell the entire story because it evaluated one justification while ignoring others.

 

4. Cybersecurity:

 

Cyber attacks and online financial crimes are exceedingly widespread, and embezzlement is an issue that even the best organizations in the world face. We've seen several large organizations, particularly banks, fall victim to cyber-attacks in which not just money but also client information is stolen.

 

5. Enhanced the efficiency of manual processes:

 

Scalability is a property of data integration solutions that allows them to grow in response to changing business needs. By having access to a complete picture of all transactions, every day, credit card companies may automate mundane procedures, cut IT staff hours, and provide insights into their customers' daily activities.

 

6. Investigate any existing dangers:

 

Big Data analytics in the Big Data finance industry also allows you to be aware of potential dangers to your firm. You might also counsel them about their hazardous conditions. Machine learning algorithms make it simple to identify risky investments. This is an excellent time to avoid making bad financial mistakes and reconsider engaging in a financial disaster.

 

Big Data tools for Banking

 

1. Hadoop: 

 

Hadoop was developed as a pioneering Big Data solution to assist manage massive amounts of organized, unstructured, and semi-structured data. It's a distributed framework for storing data and running applications on commodity hardware clusters. It was almost immediately connected with Big Data when it was originally presented in 2006. Hadoop is composed of four major components: YARN, or Yet Another Resource Negotiator, is a programme that schedules jobs to run on cluster nodes and allocates system resources to them.

 

2. Airflow:

 

Airflow is a massive data system workflow management software that allows them to schedule and run complex data pipelines. It enables data engineers and other users to ensure that all workflow steps are executed in the correct order and that all system resources are available. Airflow is also touted as easy to use: Workflows are written in Python, a programming language that may be used to create machine learning models, send data, and do a variety of other things. These are some important aspects of airflow: a user interface for a web application for visualizing data pipelines, monitoring production status, and diagnosing difficulties. 

 

3. Hive:

 

Hive is data warehouse infrastructure software that reads, writes, and manages large data sets in distributed storage systems using SQL. It was created by Facebook, however it was eventually open sourced to Apache, who continues to develop and support it.

 

Hive is a structured data processing system built on Hadoop. It's used to summarize and analyze data, as well as query massive amounts of data. Despite the fact that it cannot be used for online transaction processing, real-time updates, or queries or operations that need low-latency data retrieval, Hive's developers describe it as scalable, fast, and versatile. 

 

4. Flink:

 

Flink is another Apache open-source project that provides a stream processing framework for networked, high-performance, and always-available applications. It supports batch, graph, and iterative processing and can perform stateful computations over finite and unbounded data streams.

 

Flink can handle millions of events in real time with low latency and high throughput, and it has the following features that allow it to work in all popular cluster environments: three API levels for developing various types of applications. 

 

5. Iceberg:

 

Iceberg is an open table format for managing data in data lakes, which it accomplishes in part by maintaining individual data files in tables rather than folders. Iceberg is an Apache project that is frequently "used in production where a single table can contain tens of petabytes of data," according to the project's website.

 

The Iceberg table format was designed to improve on the layouts found in programmes such as Hive, Presto, Spark, and Trino. Its operations are comparable to those of SQL tables in relational databases. It does, however, permit many engines collecting data at the same time. The following are some additional noteworthy features: The use of concealed data partitioning eliminates the requirement for users to maintain partitions. 

 

The age of big data is rapidly approaching. Organizations must grasp what big data is and how to use it because it has the ability to uncover multiple business opportunities. The advantages and benefits are too important for businesses to overlook. Bringing together various data sources, such as firm data, public data, and social data, would yield even more information.

 

Conclusion:

 

However, financial services firms are still trailing in terms of utilizing big data analytics tools, which represent untapped value creation potential for the banking industry. This must be evaluated from the standpoint of IT (information technology) or the Line of Business (LoB). Big data will have a significant and astounding impact on society, but how society will affect big data remains to be seen