How the Banking and Financial Services Sector Utilizes Big Data In Their Operations
Sun, April 18, 2021

How the Banking and Financial Services Sector Utilizes Big Data In Their Operations

Banks are leveraging advancements in data collection, as well as different methods of analytics to help them learn and understand more about their clients and customers than ever before / Photo by: dolgachov via 123RF

 

Volumes of data are generated and handled within the banking and finance industries, according to Kamalika Some of finance news platform Fintech News. These industries also generate a digital footprint “backed by data.” Financial services are using big data analytics to draw business insights, store data, and boost scalability. Presently, technology has enabled banks to work hand-in-hand to harness the data to make intelligent decisions. 

Banks are leveraging advancements in data collection, as well as different methods of analytics to help them learn and understand more about their clients and customers than ever before, as reported by the staff of financial trading and business news site Finance Magnates. 

Harnessing Big Data

After the Great Recession of 2008 drastically affected global banks, big data analytics enjoyed popularity in the financial sector for a decade. Banks “needed to ensure different means which were feasible to analyze technologies” such as relational database management systems Hadoop and RDBMS for their business gains. This occurred when banks started to digitize their operations. 

The business gains were made possible as existing data analytics practices simplified the monitoring and evaluation of vast amounts of customer data like personal and security information. As banks began to trust technology to handle customer data and transactions, the overall service level offered also improved. 

By using big data, banks can use a customer’s transaction information to monitor their behavior in real-time. This allows banks to provide “the exact type of resources needed” anytime, improving the banking sector’s overall performance and profitability. Financial and banking data will be one of the core foundations of big data. If banks are capable of processing this data, they immediately have an edge against other financial institutions. 

The business gains were made possible as existing data analytics practices simplified the monitoring and evaluation of vast amounts of customer data like personal and security information / Photo by: gopixa via 123RF

 

The Pillars of Big Data In Banking and the Financial Services Industry

1. Variety

This refers to the different types of data processed. Banks have to constantly deal with various types of data from transaction details to risk assessment reports and credit scores. 

2. Volume

This refers to the space that the data will consume. Giant financial institutions such as China Construction Bank Corporation, BNP Paribas, JPMorgan Chase, etc. generate terabytes of data every single day. 

3. Velocity 

It indicates the speed “of adding data to the database.” Considering that banks work on volumes of data, it’s not surprising for them to process more than 1,000 transactions. 

How Big Data Can Be Utilized By Banks and Financial Services
 
1. Improved Customer Service

There are a plethora of financial institutions in the market, making it difficult for customers to decide which banks they should transact with. Hence, customer experience becomes a deciding factor. Banks can use big data to present a customized analysis of each customer, thereby improving their offerings and services

2. Personalized and Enhanced Marketing

Big data is used to target customers based on their individual spends. Banks can analyze customer behavior on social media through sentiment analysis. This can help banks create “credit risk assessment and offer customized products to the customer.” 

Fintech company Endor combines AI and big data to enhance results for sales teams. It also uses AI “to build an encrypted data prediction system based on human behavior called ‘social physics.’” The system formulates automated predictions inexpensively and efficiently, yielding valuable insights to boost sales. Big data has the potential to exponentially improve a financial service’s sales and marketing efforts, as well as enhance the quality of engagement with consumers. 

Banks can use big data to present a customized analysis of each customer, thereby improving their offerings and services / Photo by: imagesbavaria via 123RF

 

3. Fraud Prevention

Fraudulent transactions cost financial institutions billions in losses each year. Banks need to catch fraud as quickly as possible to address it and safeguard the security of their accounts and their bottom line. Human behavior comprises a large component of fraud. Individual spending and deposit patterns and seasonal fraudulent behavior are valuable in preventing and predicting fraudulent transactions in the future. 

Systems that incorporate big data can detect signs of fraud and analyze them in real-time using machine learning, enabling banks to accurately anticipate illegitimate transactions or customers. Big data also allows financial institutions to discern patterns and actions that stand out or deviate from typical consumer habits.  

4. Risk Management

When applied in the banking sector, Big data provides insight into potential lenders and borrowers. For example, an applicant’s demographic and geographic details, social media friend list, and the like are valuable details that can help banks make more informed decisions. 

By harnessing big data, financial institutions can improve lending practices and asses risks more accurately. To illustrate, German company Kreditech has been analyzing and gathering large data sets to evaluate the creditworthiness of individuals since 2013. Kreditech even makes loans with their assessments. 

The UOB bank in Singapore unveiled its big data-driven risk management system to drastically reduce the time it took to calculate value at risk. UOB bank might take 18 hours to complete the process, but the system finished the computation in just minutes. Big data is slowly making its way, however, these examples prove how banks and companies integrate big data analytics in their operations. 

Banks and other financial institutions rely heavily on data. With big data analytics, collecting and interpreting data will be quicker and more efficient. Moreover, it can also curb fraudulent transactions and offer personalized marketing to customers. If financial services incorporate big data, they immediately gain an edge against other institutions.