Did you know that everyday 2.5 quintillion bytes of data are produced? Today almost half of the world's adult population avails of digital banking. As per Markets and Markets the size of the global big data market will increase from $138.9 billion in 2020 to $229.4 billion by 2025 at a CAGR of 10.6%. Customer interactions, financial transactions and other forms of activity generate a lot of data in banks. The existing systems and processes are unable to handle the volume, velocity, and variety of data. Big data is the solution to this challenge faced by banks.  Thus, banks across the world are looking at leveraging the latest big data technologies to remain relevant and competitive in the market. The worldwide big data analytics in banking market was worth $307.52 billion in 2023 and projected to be worth $745.16 billion by 2030. The market will grow at a CAGR of 13.5% from 2024 to 2030.

What is Big Data?

Big data refers to large and varied data that is difficult to handle and analyze using conventional methods. The data arrives at great speed and in a variety of formats. The 4 V’s of big data are as follows. 

• Volume

This refers to the huge quantities of data. That can range from terabytes to petabytes.

• Velocity

This is the rate at which data flows in and requires real-time processing

• Variety

This is the different forms of data. The latter can be in the form of text, image, audio, or video.

• Veracity

This refers to the accuracy as well as reliability of data. 

How Big Data is Changing Banking

• Analyzing Stock Prices

Big data takes into account different parameters such as human resources, impact on environment as well as social reputation. Thus, banks are able to make better investment choices.

• Superior Customer Service

Virtual assistants using AI are instances of the role of big data in banking. The former take care of client queries as well as give timely reminders of important dates.

• Superior Lending Process

Certain banks employ big data along with social media to find out the credit risk of giving loans to applicants. Thanks to big data analytics banks can make superior lending decisions.

• Superior Employee Productivity

Certain banks use big data tools to track performance metrics such as employee productivity as well as metrics such as client acquisition as well as retention.

• Superior Cybersecurity

Big data solutions trace out fraud as well as avert internal risks. Banks use big data technology to prevent fraud in real-time.

• Automation of Business Processes

Big data solutions are estimated to have the ability to automate up to 30% of bank operations. Automation brings down risk of employee error as well as saves a lot of money.

• Superior Management of Fraud

Big data solutions track client spending and are able to trace unusual types of spending thus averting unauthorized transactions. The former is also useful in cases of identity fraud. By using big data solutions, the banking industry is able to ramp up security levels.

• Leverage Client Feedback

Big data solutions go through client feedback and segregate queries, comments as well as concerns. The former empowers banks to address client feedback promptly driving up trust as well as loyalty among clients.

• Client Segmentation

This step is important for targeted marketing campaigns. Big data categorizes clients into different segments based on factors such as income, risk appetite, credit card usage, demographics, and others. As such they can offer products and services tailored to each segment and which is closer to their demands as well as expectations.

• Hyper-Personalization

Big data analyzes client data for factors such as financial position, how they shop, how they invest and others. This enables banks to target each client with personalized offerings. As a result, client satisfaction is ramped up. Big data also helps banks predict client churn and thus take steps to prevent that from happening.

Implementation of Big Data in Banks

• Identify Goals

Be clear about what the bank desires to attain by using big data. Goals could range from ramping up efficiency, preventing frauds or superior client experience.

• Create a Roadmap

 This should list out the steps to implement big data adoption and integrate big data with the bank's existing systems.

• Acquire Data Storage and Processing Capacity

Have scalable storage infrastructure to handle the enormous amount of data coming from banks. Buy powerful computing resources able to analyze as well as process large amounts of data. Use cloud platforms to manage the available data.

• Consolidation of Data

Integrate data coming from different sources to a single platform. This includes third-party data as well as data coming from social media.

• Select the Best Tools

Select a big analytics tool which is high performing as well as compatible with the bank's present infrastructure. The tool should be user friendly and be able to be used by employees with little or no technical knowledge.

• Recruiting and Training

Be aware of the skills required to use big data analytics. Next assess the knowledge of the bank's human resources. Identify skill gaps that need to be bridged. Organize training to get the staff well versed and up-to-date with the latest big data tools, technologies, and methodologies. If necessary, recruit data scientists as well as data engineers with relevant experience as well as expertise.

• Incorporating Feedback

Constantly monitor the performance level of big data solutions. The latter provides insights which should be incorporated to the bank's day to day operations. Upgrades and updates in the domain of big data analytics should be implemented so that the bank is up-to-date as well as retains the competitive edge in the constantly evolving market.

Challenges of Banking Resolved by Big Data

• Data Assuming Larger Proportions

Many banks are already struggling to store, manage, and analyze their existing data. With time the amount of data is getting bigger and unmanageable. One fundamental challenge is to separate the useful data from the useless.

• Increasing Need of Regulatory Compliance

Wherever there is data there are associated risks. A top priority of banks is to keep their data safe. Cybersecurity measures are critical given the increasing incidents and sophistication of cyber-attacks. There is an increasing number of regulations related to the storage and use of banking data.

• Legacy Systems

This type of banking infrastructure was not designed to handle the present size and complexity of data. If not replaced they can make a bank obsolete or uncompetitive. They must be upgraded or replaced. Only big data technologies have the capability to handle modern day banking systems as well as processes.

Examples of Banks Leveraging Big Data

• Goldman Sachs

This is a major global investment bank. The bank is an early adopter of big data to deliver superior client service, be competitive as well as trace good investment opportunities. Thanks to big data tools the bank is able to interpret data from multiple formats.

• JP Morgan & Chase 

The prestigious bank offers a plethora of services such as investment banking and private banking. The former harnesses big data to go through data of client transactions and merge them with other data to determine the creditworthiness of clients. Thanks to cutting-edge big data technologies they are able to identify top purchasers of financial products and services.

Pros of Using Big Data in Banks

• Become More Competitive

Thanks to big data, banks have the ability to make data-driven decisions. Banking institutions get valuable knowledge related to risk factors as well as market trends. The former are thus able to make superior lending as well as investment choices.

• Superior Regulatory Compliance

Big data automates data gathering, analysis as well as reporting. This decreases the incidents of non-compliance and having to dole out hefty penalties. Big data makes consolidated as well as detailed transaction records that simplify the process of proving compliance in case of audits.

• Ramp up Efficiency

Big data can trace unprofitable bank branches, services as well as products upon which the bank can terminate them. The former's automation of banking-related tasks frees staff to focus on more important tasks. As a result, banks are able to raise productivity and efficiency giving them a significant edge over the competition.

• Superior Client Experience

Big data is able to better identify client needs as well as preferences. The bank is able to use these insights to offer each client products and/or services that meet their goals and objectives. This makes the bank’s clients feel valued as well as appreciated. This translates to boosting of client trust as well as loyalty. Banks are also able to upsell as well as cross-sell their existing products and/or services. It is far easier to retain existing clients than acquiring new ones. By leveraging big data, clients retain existing clients as well as are able to onboard new ones. This translates to increased revenues as well as profit margins for the bank.

• Superior Loan Approval

Traditional methods used only credit scores to assess creditworthiness and decide whether to grant a loan or not. Big data uses multiple factors such as income, social media, nature, and volume of financial transactions to determine loan eligibility. The technology also reviews relevant documents in real time so that the loan approval process takes far more time than it used to take before big data usage. As a result, there is reduced risk of loan default and increased profitability for the concerned bank.

The Future of Big Data in Banks

Big data technology is continuously evolving and improving. The increasing trust in this state-of-the-art technology will result in adoption of big data by more banks. AI (Artificial Intelligence) and big data will be used together to enhance banking systems and processes. Technologies such as Blockchain will be used to ensure superior levels of security. Big data will drive up sustainability and keep the environment in mind. The technology will enable banks to serve unserved communities such as those living in remote areas. Big data will enable banks to go from local presence to a global one.

Now that you know how big data is changing the banking industry, it is time to take appropriate action. With expertise in both Big Data and banking, CoffeeBeans is well poised to integrate Big Data technology with the existing infrastructure of your bank. We have a pool of talented professionals who have relevant experience in implementing Big Data in banks. As such they are aware of the challenges in the process and will speedily resolve them. Reach out to us at [email protected] to know how we can help you attain your unique and specific needs and expectations.