Challenges of Big Data in Finance

In this blog, we will discuss some of the challenges of using big data in finance.

Having huge amounts of information can be extremely useful to companies and their customers. But, things can go wrong.

One interesting report found that Target identified a teenage girl as pregnant based on her shopping habits while using her loyalty card. Target used this information to send a mailer to her home recommending maternity clothes.

The father complained about this ‘mistake’, but later found out that Target knew the truth before he did!

This story highlights that the data being collected by large companies including financial institutions is very personal, and it’s not always obvious how it’s going to be used.

An even bigger concern is when data is compromised. Data breaches are so common that we don’t even hear about most of them. Check out this visualization of the biggest data breaches in the last few years.

There are literally hundreds, and with names like Apple, JP Morgan Chase, and the US Military on the list, there is a big reason for concern.

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Algorithms Can Be Wrong

Back in 2013, many trading companies were using sentiment analysis to monitor news on Twitter. Unfortunately, many of them were taking tweets a little too seriously.

After a hack to Associated Press’s Twitter account, a fake tweet went out reporting that the White House had been bombed and that Barack Obama had been injured.

The algorithms jumped on this news, and within seconds $130bn was wiped off the US stock market. The stocks made a recovery afterward, but it’s a scary reminder that relying on inaccurate information can have huge consequences.

Legal Issues

Compliance

Regulating bodies in all countries have caught on to the fact that market data is important. They realize that ensuring big data technology is used safely and responsibly is vitally important to the safety of citizens and the economy.

Since the financial crisis of 2007/2008, the financial industry all over the world has seen an exponential increase in the amount and detail of structured and unstructured data they need to report to authorities.

This is to protect citizens and prevent another financial collapse caused by poorly understood financial products.

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Third-Party Data and Privacy

It makes sense that financial companies want to combine their own collections of information with data from other firms. This allows them to get a deeper understanding of customers and trends – and gain a competitive advantage.

For example, a life insurance company might want to look at your purchase history from your local supermarket’s loyalty program. This would help them learn more about your lifestyle and calculate a more accurate premium.

There are strict legal requirements when sharing information about people, especially personal information. Financial services companies need to comply with these rules and stay transparent about what they are doing with our data.

Big data combined with artificial intelligence technology helps businesses improve their business management and development procedures to a great extent. If you are planning to gauge the benefits of machine learning algorithms running on big data for your fintech product, you are planning to make a very fruitful investment.

The challenges of big data in finance are real but there are ways to overcome them. There are software tools that assist in big data formatting, management, integration, etc.

The important part is to partner with experienced data science professionals and software developers who have experience in using big data tools like Hadoop, NoSQL, etc.

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DevTeam.Space can help you here with their data engineering developers who are experienced in cutting-edge software development technologies.

Write to us your initial fintech and big data development requirements via this link and one of our technical managers will get back to you for further discussion.

1. What are the top challenges posed by big data in financial services?

Data cleaning, improving data quality, maintaining data security, and complying with data with regulatory standards are some of the prominent challenges of using big data in the finance industry.

2. How to overcome the challenges of big data in finance?

Challenges of big data in the fintech sector can be overcome by using the latest data management and storage technologies like Hadoop and NoSQL, data integration tools, incorporating regtech technologies for better security and compliance, partnering with skilled data engineers, etc.

3. How financial market use big data analysis?

Internal and external data helps finance products/services in multiple ways, offering personalized users’ experience, better predictive analytics for intrusions and fraud detection, risk management, identifying frauds on time, managing online identities, etc.