Big Data in Finance: Everything You Need to Know

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Digitization in the finance sector has allowed new technologies such as advanced analytics, big data, the cloud, machine learning, and artificial intelligence to not only penetrate but also transform how financial organizations are operating and competing in the market.

Large financial organizations are embracing these technologies to meet consumer demand, improve profit, minimize loss, and execute digital transformation. Although most organizations are storing vast amounts of new and valuable data, not all of them know how to maximize its potential.

As the sector rapidly progresses toward data-driven optimization, organizations must respond to these changes accordingly so that they can have competitive advantages and discover new market opportunities. Below, we explain everything you need to know about big data in finance.

What Is Big Data in Finance?

What-Is-Big-Data-in-Finance

Not long ago, the term “big data” was mostly confined to the technology sector. Nowadays, it is considered a business imperative. The term big data in finance refers to massive, diverse, and complex sets of data that can be utilized to provide insights into business challenges for financial services.

Big data is increasingly leveraged by financial organizations to transform their organizations, their processes, and the entire industry. The finance sector as a whole generates a lot of data, both in structured and unstructured forms.

Structured data is data managed within a financial organization to provide meaningful insights into decision-making. Unstructured data exists in various sources. It is increasing in volume and can be utilized to offer significant analytical opportunities.

The value of data, especially in the financial sector, relies heavily on how it is collected, processed, stored, and analyzed. The problem is, legacy systems cannot handle a massive amount of data efficiently. Understandably, more and more analysts are adopting cloud data solutions.

Cloud data solutions not only cut costs of hardware that is necessary to process big data, but it also improves flexibility and scalability and allows for a more actionable and efficient approach to analytics and big data.

How Does Big Data Transform the Finance Sector?

How-Does-Big-Data-Transform-the-Finance-Sector

Now you know more about big data in finance. But how exactly does big data transform the finance sector? Big data transforms the sector in many ways. For example,

Customer analytics

Financial organizations use customer analytics to understand their customers better. With analytics, organizations can provide better service to their customers.

By understanding customer needs and preferences, financial companies can anticipate future customer behaviors, improve their products, improve customer satisfaction, take advantage of new technologies and channels, as well as generate sales leads.

Using big data in finance, financial organizations can deliver customer-centric products and services, thus quickly seizing market opportunities.

Real-time stock market insights

Big data is completely transforming how stock markets around the world are functioning. And with that, how investors are making their decisions. Utilizing machine learning, which finds patterns in vast amounts of data, computers can make accurate predictions and decisions and execute trades quickly.

When information about the best possible prices and current trends are available in real time, analysts can make smart decisions and minimize manual errors caused by biases and behavioral influences.

Using big data in finance, traders can gain highly optimized stock market insights, which in turn allow them to maximize their portfolio returns.

Risk management and fraud detection

There is also risk management and fraud detection. Mitigating management and combating fraud have been a part of the finance sector from the beginning. But with big data in finance, financial organizations can do them better and more efficiently.

For example, banks can now access data in real time. This, in turn, can help them identify fraudulent activities. If there are any suspicious transactions, the bank can notify the customer immediately and block the transactions.

Similarly, an insurance company can access data from criminal records, past claims, telephonic conversations, social media, etc. while processing a claim. If there is anything suspicious, the company can flag it for further investigation.

Accurate risk analysis

Not too long ago, performing accurate risk analysis seemed like an insurmountable task. Today, it becomes much easier, thanks to unbiased machine learning. 

Leveraging big data, organizations can make calculated big financial decisions with better accuracy as variables like economy, business capital, as well as customer segmentation are taken into account.

Applications of Big Data in Finance

Applications-of-Big-Data-in-Finance

What are the applications of big data in finance sector? There are many. The following are some examples of the applications of big data in finance.

1. Forecast financial trends

Big data in finance can be utilized to forecast financial trends. Using vast amounts of data, a financial organization can be aware of a positive trend and pick it up to have a competitive edge over its competitors. Similarly, the organization can also forecast a negative trend and not follow it.

In addition, big data analytics provides organizations with meaningful and actionable insights.

2. Analyze potential risks

Big data in finance makes it possible for financial organizations to analyze potential risks effectively and efficiently. Using big data analytics, an organization can be aware of the potential risks that it has.

Combined with machine learning algorithms, big data can provide the organization with insights about risky investments, thus preventing potential bad financial decisions.

3. Automate core tasks

Big data also makes automation of core tasks possible. With automation, an organization can manage any financial task faster and perform better. Associates, analysts, and managers can do trivial tasks with more efficiency.

An added benefit of automation of core tasks is increased customer satisfaction as customers are able to perform tasks digitally and will get online assistance.

4. Improve path to purchase

Big data can be used to improve the path to purchase. However, legacy tools are not capable of offering solutions for massive, disparate data. Not to mention they often have limited flexibility in terms of server deployment.

To make it possible, big data must be combined with cloud-based data management tools. With these tools, financial organizations can improve the path to purchase for customers, among other things.

5. Customer segmentation and targeted marketing

If a financial organization is to reach and understand its customers effectively, it must segment them into categories based on needs, likes, dislikes, socio-economic status, and so on.

After customer segmentation is done, the organization can then develop products and/or services for each segment and market them accordingly.

The thing is, these are quite difficult to do in traditional ways. Fortunately, big data can make it happen.

6. Fraud detection and prevention

Many financial organizations use big data in finance to prevent and detect fraud. These organizations are utilizing machine learning to identify suspicious consumer spending behaviors and patterns in real time.

With the information big data provides them with, banks can act immediately and effectively, thus reducing losses not just for businesses but also for consumers.

4 Challenges of Big Data in Finance

4-Challenges-of-Big-Data-in-Finance

As good as it is, big data also comes with challenges that financial organizations must tackle. Here are the 4 main challenges of big data in finance.

1. Regulatory requirements

One of the biggest challenges of big data in finance is regulatory requirements. If a financial organization wants to utilize big data technology, it has to meet stringent regulatory requirements in the industry.

For example, the Fundamental Review of the Trading Book, which demands accelerated reporting and regulate access to critical data.

2. Data quality

Financial organizations want more than just storing data. They want to use it. The problem is, not all data is equal. After all, data comes from various sources, which means it may not always agree, particularly when it comes to quality.

The good news is that data management solutions can ensure the data is accurate, usable, and most importantly, secure. Meanwhile, real-time analytics tools provide organizations with access, speed, and accuracy, thus helping them to derive meaningful insights.

3. Data security

With the rise of data breaching, financial organizations must take necessary steps to ensure data security. Organizations can use big data management tools to make sure that data is protected and secure and detect suspicious activity.

4. Data silos

Data in the financial sector can come from many sources. For example, emails, enterprise applications, or employee documents, among many other things.

Combining and reconciling big data needs data integration tools, using which organizations can simplify data storage and access. This challenge can be tackled using big data and cloud solutions.

As more financial organizations adopt these solutions, more people will realize how beneficial big data solutions are in the finance sector.

Getting Started

Getting-Started

Large financial organizations have paved the way toward big data adoption. They have shown us how real big data solutions are.

While the application and maturity of big data in financial organizations vary from one another, they are all driven by the same question: “How can data solve our business problems?”

Whether the main issue is operational optimization, improving business processes, or customer experience, there are certain steps that organizations must take if they want to embrace data-driven transformation.

Here’s how to get started with big data in finance.

1. Define a data strategy

First and foremost, define a data strategy. And to define a data strategy, you need to define a business goal. A comprehensive data strategy will span not just across all departments within the organization, but also across the network of partners.

So, rather than focusing on short-term and temporary fixes, examine where your organization’s data is growing and heading.

2. Choose the right platform

Once the data strategy is defined, the next step is to choose the right platform. The needs of each financial organization are not the same. The good news is that there are a lot of cloud data platforms to choose from. In other words, you will find platforms that cater to your organization’s needs.

When choosing a platform, make sure that it is both scalable and flexible. Scalability and flexibility are important because they allow an organization to gather as much data as needed while processing said data in real time.

Besides scalability and flexibility, there is also security. Make sure that the platform is secure. Better yet, choose a platform that specializes in it.

3. Start with a problem

Big data in finance has a myriad of capabilities. That said, you should start with a business problem. After identifying and tackling the problem, your organization can then expand from one solution to another. Basic use cases of big data can be built upon with ease and then expanded over time.

Big data continues to transform the financial sector. Many financial organizations are adopting big data in order to maintain their competitive edge. Utilizing both structured and unstructured data, organizations can discover new market opportunities and have competitive advantages.

Big data in finance has many applications. For example, financial trends forecast, analyze potential risks, and automate core tasks, just to name a few. But as good as it is, big data in the sector also poses challenges that need to be tackled. 

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