Using previously considered ‘static’ and ‘stale’ data to provide valuable insights helped create Big Data in Banking, as every transaction and stored piece of data suddenly became of value. Furthermore, if we consider the amount of pre-existing data and new data generated daily, the amount of information banks have to gain insights from is truly incomprehensible. Banks have leveraged big data to improve many important services, features, and processes. It can help banks identify bad loans, customer profiling, market trends, and more. In banking, other industries and sectors have adopted the practice of using big data to drive business growth. Nevertheless, here are some of the best practices businesses can adopt to ensure they leverage big data in the best way possible.
The Uses of Big Data in Banking
Customer Profiling: Big data offers banks detailed information on customers’ banking history and transactional patterns. Banks can use this information to plan personalized content strategically targeting their various customer demographics. This helps banks build trust and better serve the specific needs of different customer types, all based on their customer data and customer history. Various types of customers require different products and services, and Big Data in Banking helps banks optimize the identification of these customers.
Fraud Detection: Fraud is a major risk for banks. Big data and business intelligence tools are of great help to any bank. It helps their risk management department in tracking spending and other behavioral patterns. Banks are liable to face defaults and fraud from individuals experiencing financial stress. Big data and business intelligence software allow banks to identify high-risk clients so that they can employ standard practices and strategies to mitigate the situation.
Lending Decisions: Loans are a crucial financial service offered by banks to individuals. Determining whether a loan candidate will be able to repay the loan amount offered is an important need for banks. It was due to the high number of defaulters that triggered the 2008 financial crisis. Since then, big data analytics has helped banks select creditworthy individuals. These analytics allow banks to dive deeper into a customer’s spending habits and nature, creating a holistic picture that allows banks to assess risky candidates accurately. Big data in banking allows banks to make smart, well-informed lending decisions daily.
Matter of Compliance: Big data and business intelligence software make it easier for banks to keep records and meet regulation standards. These tools allow banks immediate access to necessary updates and procedures, creating a compliance process of ease and efficiency. In the past, dealing with compliance was extremely difficult for banks. Software tools and big data allow banks to get all the necessary compliance information and structure in a seamless and easy way to manage.
Cyber-security: Due to the high volume and nature of transactions in the financial industry, it is especially at risk regarding cyber-security threats and issues. Additionally, customer confidentiality and financial data protection are also a duty of banks, making it essential to have up-to-date, proficient cyber security systems. Big data and artificial intelligence tools allow banks to set up necessary measures to combat all threats. Financial fraud and embezzlement are extremely common, and big data can help banks analyze trends and patterns. Big data also provides banks with necessary insights that can help government agencies tackle threats like financial terrorism.
CONCLUSION
Big data in banking has grown exponentially since the 2008 crisis. It plays a role in helping banks better serve customers and assess customer risk and security risks. Big data has become a feature across all industries. Various players use it in various ways that best serve their business needs.