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BIG DATA IN BANKING AND SECURITIES

Big data is a big buzzword in the technology industry. It describes a large and ever-growing volume of data that can be difficult to process using traditional database tools. As a result, big data has played a significant role in various industries, such as retail, manufacturing, health care, and transportation. Ensure you check previous articles I’ve written on the role big data has played in revolutionizing other economic sectors. This article will highlight the role of big data in banking and securities.

History and development

Big data has been around for quite some time. We can trace the origins of big data back to the early days of computing. Large companies started collecting large amounts of data to track their customers’ behavior. However, it was not until recently that big data began to play a significant role in the banking and securities industry.
In 2011, big data in the banking and securities industry gained attention when two of the largest banks in the world, JPMorgan Chase and Bank of America, announced their plans to invest in big data. At that time, JPMorgan Chase estimated that it could save up to $US10 billion a year by using big data analytics to improve its operations. Bank of America also saw the potential of big data and decided to invest in a big data team to help it improve its customer service.

Since then, big data has been growing rapidly in the banking and securities industry. A study by IDC found that global spending on big data solutions is expected to reach $US48.6 billion by 2019, up from $US14.8 billion in 2015. In addition, their recent studies suggested that worldwide spending on big data and business analytics (BDA) solutions is forecast to reach $215.7 billion this year, an increase of 10.1% over 2020. The main drivers of this growth are the need for real-time analytics, the increasing use of big data in cloud computing, and the growing popularity of big data as a source of business value. In the banking sector, big data has played a critical role as digital finance and transactions rates are growing exponentially annually.

Current role of big data in banking and securities

The use of big data in banking and securities is revolutionizing the sector. Here are some examples:

Fraud detection and prevention:

Big data in banking and securities is essential for early fraud detection and prevention. Banks and other financial institutions are using big data analytics to identify patterns in customer behavior that may indicate fraud. They also use big data to track financial transactions across different channels and identify suspicious activities. As a result, banks can detect financial crimes before they happen by monitoring and identifying patterns and trends in client transactions. The most common financial crime banks deal with is money laundering.

The estimated amount of money laundered globally in one year is 2 – 5% of global GDP, or $800 billion – $2 trillion in current US dollars. Money laundering has increased with new technologies that facilitate it in the modern-day. As a result, it has become a critical issue that has attracted the attention of global powers and international bodies such as Interpol. Experts have suggested that money laundering has been a key reason why terrorism and trafficking continue to rise annually.

Banks have invested an enormous sum of money in anti-money laundering software to counter money laundering. By 2025, experts project that the anti-money laundering software market will reach $2.77 billion. In addition, most countries have enacted laws with harsh penalties and fines for businesses found guilty of money laundering. From 2008 to 2017, financial institutions lost about $321 billion globally through penalties for being non-compliant with standardized regulations, assuaging money laundering, funding terrorism, and manipulating the market. Therefore, big data has been crucial for banks in the early detection of fraud. As a result, banks have suffered fewer financial losses.

Customer service.

Big data in banking and securities has been vital in improving customer service. Banks are using big data to understand their customers better and provide them with better customer service. For example, they collect data on customer demographics, spending habits, and preferences. Then, they use this data to create customer profiles to target marketing campaigns and personalize the customer experience.

A better customer experience has been crucial for banks when other financial institutions such as SACCOS have arisen. There has also been an increase in banks, especially in the developing world. Banks also offer additional services such as overdrafts and credit and debit cards. As a result, they are still the preferred financial institution. So, better customer service increases their competitive advantage.

Risk management

Big data in banking and securities has been vital in risk management. Banks use big data to identify and assess risk factors to make more informed decisions about lending money and investing in new ventures. Banks are using big data analytics to track financial trends and identify correlations between different factors that may lead to increased risk. Many financials went under after the global financial crisis of 2008. As a result, many people lost their hard-earned money, homes, and other investments. Poor risk analysis contributed to the crisis. It has therefore become crucial for financial institutions to improve their risk assessment.

Product development.

Big data in banking and securities has also influenced innovation in the sector. Banks are using big data to develop new products and services. For example, they collect data on customer needs and preferences and use it to design products that are more likely to be successful.

The future of big data in banking and securities

Big data in banking and securities is growing at a rapid pace. Here are some of the trends that we are seeing and will continue to see:

The use of big data in cloud computing:

More banks are moving their big data applications to the cloud for faster access to data and better scalability. This allows banks to take advantage of the power of big data without having to invest in their infrastructure. Cutting infrastructural costs has been crucial when more companies are embracing remote workspaces.

The use of big data for predictive analytics:

Banks are using big data to predict future events, such as financial crises and stock market crashes. They can identify patterns that may indicate upcoming problems by analyzing past data. This will save more banks from liquidating. Eradicating poverty is one of the United Nations Sustainable Development Goals. By reducing the chances of financial institutions going under, the more the world is closer to eradicating poverty.

The use of big data for competitive analysis:

Banks are using big data to track their competitors’ activities and gain a competitive advantage. They are collecting data on pricing, product offerings, and marketing campaigns. This allows them to stay ahead of the competition and make better decisions about their strategies.

The use of big data in mobile banking.

More banks are offering mobile banking services that allow customers to access their account information and conduct transactions from their smartphones or tablets. In addition, the banking and securities sector is using big data to develop mobile apps that are more user-friendly and provide a better customer experience.

The use of big data in compliance and risk management:

Banks will continue to use big data to meet the increasing demands of regulators for improved transparency and accountability. As criminals get more intelligent, so will the software used to track them. This will help them stay compliant with government regulations and avoid costly penalties.