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In the past decade, big data has become increasingly important in various industries - and the finance industry is no exception. Big data can be used in a number of ways to help financial startups, from customer segmentation to fraud detection. However, some potential drawbacks to using big data in finance exist. This blog post will take a closer look at application of big data in finance, and its pros and cons.

What Is Big Data?

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. A data set is so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. Big data can be characterized by the 5V's: volume, velocity, variety, veracity, and value.

5V's of Big data

Pros and Cons of Big Data for Finance

As the world becomes more digitized, so does the finance industry. Transactions that used to be conducted in person or over the phone are now being done online, and this shift has created a need for new ways to collect and analyze data.

Overall, big data for finance has both advantages and disadvantages. Its advantages include detecting financial risks and opportunities and creating more personalized financial products and services. However, big data also has disadvantages, such as potential misuse and privacy concerns.

Pros of big data:

  • Opportunities to make better decisions
  • Improving productivity and efficiency
  • Reducing costs
  • Enhancing customer experience
  • Fraud detection

Cons of big data:

  • Compliance costs
  • Cost and infrastructure issues

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How Does the Finance Industry Use Big Data?

Big data for finance refers to petabytes of structured and unstructured data that can be used to predict customer behavior and create a strategy for banks and financial institutions.

Big data and finance are generally used as the product of FinTech industry. Distinctive examples of FinTech in our daily lives are mobile payment applications, cryptocurrency, and blockchain. In the future, the range of FinTech services is predicted to further reshape the market with the help of artificial intelligence and machine learning and make FinTech products an integral part of our digital lives.

According to Mordor Intelligence, Big Data analytics in the banking industry is expected to grow at a CAGR of 22.97% between 2021 and 2026.

Application of big data in finance is wide. The finance industry has been using big data for a while now to make better investment decisions, detect financial fraud, develop new products and services, improve customer service, and manage risk. Let’s take a look at how big data is used in each of these areas.

Better investment decisions: In the past, analysts would have to go through mountains of paper documents to find trends that could indicate where investment might do well. Today, with big data, all that information can be fed into computers which can then identify patterns much faster than humans can. This allows analysts to make better-informed decisions about where to invest money.

Detecting financial fraud: Big data and finance are also interconnected in detecting financial fraud. By looking at patterns in transaction data, banks and other financial institutions can identify suspicious activity much sooner than they would have been before. This helps them protect their customers’ money and saves them money in terms of investigation costs.

Developing new products and services: Banks and other financial institutions always look for new ways to better serve their customers. Big data in financial industry can help develop new products and services by better understanding customer needs and wants. For example, a bank might use big data to understand what kinds of products its customers are interested in and then develop new products that meet those needs.

Improving customer service: Another way big data is used in the finance industry is to improve customer service. By understanding what customers say about their experiences with a particular bank or financial institution, companies can identify areas where they need to improve. Additionally, by tracking customer interactions, companies can identify when a customer is likely to leave and take steps to prevent that from happening.

Managing risk: Finally, big data is also being used by the finance industry to manage risk. By understanding the risks associated with certain investments, banks, and other financial institutions can avoid making bad decisions that could lose them money. Also, they can identify potential problems early on by monitoring market trends and taking steps to mitigate those risks.

Financial institutions can now also use big data in finance sector for new use cases, e.g., the generation of new revenue streams with the help of data-driven offers, personalized customer recommendations, increases in efficiency, more security, and better customer service. Many financial institutions are already making good use of big data and are getting immediate results. As you can see, big data applications in finance are enormous. Let’s analyze customer experience improvements with big data.

How Can Big Data Help Financial Startups With Customer Experience?

Big data is one of the most promising new tools for the finance industry, as it can provide insights into customer behavior and trends. Use of big data for banking and finance sector allows us to segment customers and target them with specific products and services. Additionally, big data can help financial startups to identify risk factors and to develop strategies to mitigate these risks.

There are numerous ways how professionals can use big data in financial industry. First, organizations need to ensure that they have adequate security measures in place to protect customer data. Second, they must ensure that they are using data ethically and in a way that complies with all relevant regulations. Finally, they need to be aware of the potential for bias in algorithms and take steps to avoid this bias when developing models.

Despite these considerations, big data use in finance can be a powerful tool for financial startups. It can provide otherwise unavailable insights, help startups understand their customers better, and develop more targeted products and services when used correctly.

It is essential for a company not only to ask itself what it can still get out of the business but to ask itself honestly, "Why should our customers continue to choose us?" Have we reached our goal yet? And if not, have we made any progress towards getting there?” When technology is positioned correctly - as the link between people and people and technology - the customer experience finds its rightful place: at the heart of the business.

If you want to inspire and retain customers in the long term, you have to put yourself in their shoes. Need to see and understand what they need to do to get what they want from their bank. Every station on the entire customer journey counts. Because only companies that know their many milestones can interact with every customer at every point so that they reach their respective goals as easily and quickly as possible. And that is the secret of positive customer experiences. Big data in finance sector allows organizations to guarantee such a customer experience and boost it.

Famous Big Data Cases

Role of big data in financial industry is outstanding. So, there are many use cases of these technologies worldwide.

  1. Increased revenue and customer satisfaction. Organizations like Slidetrade have been able to apply big data solutions to develop analytics platforms that predict customer payment behavior. By gaining insight into the behavior of their customers, an organization can reduce payment delays and generate more cash while improving customer satisfaction.
  2. Acceleration of manual processes. Data integration solutions can scale as business requirements change. Access to a complete picture of all transactions every day allows credit card companies such as Qudos Bank to automate manual processes, save IT staff time, and offer insight into customers' daily transactions. Improved purchase path. Legacy tools no longer offer the solutions needed for large, siled data and often have limited flexibility in the number of servers they can deploy across industries.
  3. Fraud elimination. Big data use in finance also ensures predictive analysis tools to distinguish between legitimate and fraudulent activity, and many forward-thinking organizations are already using this approach. For example, Alibaba Group has built a fraud risk management system that uses real-time big data processing. The system analyzes large amounts of consumer data in real-time and finds fraudulent transactions.

How Can Axon Help You With Your Plans?

At Axon, we work on FinTech solutions to ensure the best customer experience for your organization. Banks and financial institutions can make their customer communication much more targeted, cost-effective, environmentally friendly, and effective in the long term.

In order to give every customer a positive experience when dealing with their bank or any financial institution, it is not enough to filter vast amounts of data according to abstract categories and force customers into a grid. Instead, it is about understanding customers and the current state of their customer experience.

Axon’s cloud-based solutions speed up the analysis of financial data by integrating enterprise data, managing its quality, and governing it.

Learn more about our expertise in FinTech!

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