Over the past decade, data analytics has become a buzzword, with many organizations incorporating some form of data science into their operations. Banking institutions are no exception in this regard as the industry is highly data driven. Banking institutions dealing with large volume complex data can rely on data cleansing companies to convert their documents into any required format. The increasing interest in the use of data analytics in the banking industry is due to the improvements in the area of technology, client expectations and general market structure and behavior that have been occurring in this sector. Application of data analytics has helped the banking industry optimize processes, streamline operations and gain insights on new markets, thus improving efficiency and competitiveness. In fact, many banks are working on improving their data analytics, mainly to give them an edge over competition or to predict emerging trends that can affect their businesses.
Why Do Banking Institutions Need Data Analytics?
Generally, most people or clients tend to build a trustworthy relationship with banks and financial institutions. In fact, these trustworthy relationships with banks are based on several factors like loyalty, trust and delivery of top-quality services. The increasing sophistication of banking services and products has triggered the need for more effective decision-making tools to enable better decisions from data insights. Nowadays, simply viewing documents and data figures alone cannot influence banking businesses anymore. The banking sector needs to utilize its data for analysis and better decision-making. Analysis of key financial data can help better determine different ways on how to maximize profits and improve business relationships and customer service. This is the key point where the significance and need of data analytics arises. Evaluating documents and transactional data will help create a better picture of the banking business and its level of operations.
How Are Banking Institutions Utilizing Data Analytics?
Banking institutions are mainly utilized in demand, supply and risk management. Today, banks and financial institutions are using data analytics in a more focused way. Banks utilize data analytics for on-time payment, issuing credit cards and getting customer response regarding new schemes introduced by the bank. In addition, the technology is also used to keep track of security aspects with a predictive approach rather than a reactive approach. Even though data analytics is becoming common for banks and financial institutions, it is still fairly new. It is not yet a standard practice, and is not always used in the same way by every bank and financial institution. For instance, many people think that credit scores are static, unchanging numbers, but they are actually dynamic, constantly evolving as new information comes in or as old information is updated. Below discussed are some of the key points on how banks and financial institutions use data analytics to manage risk –
- Fraud Detection – Fraud detection is important for banks and financial institutions. Data analytics can be used to manage risk instead of simply detecting fraud. Analytics can be used to identify and rate individual customers who are at risk of fraud and then apply different levels of monitoring and verification to those accounts. Analyzing the risk of the accounts enables banks to know what to prioritize in their fraud detection efforts.
- Credit Risk Analysis – Banks and financial institutions use analytics to manage the risk associated with the loans they process. Data analytics can be used to monitor key financial data such as – customer credit score, credit card utilization, amounts owed on different credit cards (total debt) and amounts owed on different types of credit (total debt/total credit) – they collect on individual customers. Credit risk analysis is the analysis of historical data to understand a borrower’s creditworthiness or to assess the risk involved in the processing of a loan. The results of the analysis help banks and financial institutions evaluate their risks and those of their customers.
- Risk Modeling for Investment Banks – Risk modeling is the process of simulating how a portfolio of assets (stocks, bonds, futures, options, etc.) or a single asset (such as an interest rate) moves in response to different scenarios. If done correctly and consistently across all assets, risk modeling can reduce the portfolio’s overall risk and improve its performance. For instance, if a bank wants to do an investment banking transaction, they would need to consider certain aspects such as the expected returns, amount and probability of risk involved and importance of a transaction (when compared to other alternatives). Risk models are used in many fields with financial institutions to describe how risky things are, what is likely to happen, and how much it will cost to mitigate risk.
- Operational and Liquidity Risk – Typically, operational risk is used to describe the potential for loss due to actions taken by the business. Operational risk encompasses risks that are specific to an individual financial institution. On the other hand, liquidity risk is more macro in nature, including issues such as interest rate fluctuations, changes in foreign exchange rates, and changes in the value of other financial instruments (like bonds). The operational risks are possible losses that result directly from risks associated with day-to-day operations of the institution (such as fraud, theft, computer security breaches, or error in judgment or incompetence at an executive level).
Data analytics is being increasingly used in banking and financial institutions. Banks that invest in data analytics have noticed significant boost in efficiency, reduction in costs and rise in fraud detection rates. The analysis of data can be made easier and more efficient with the support of professional data cleansing services. Combining these datasets to work together will provide a rapid increase in revenue and improve the customer experience.
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The significant ways in which banks and financial institutions are using data analytics to manage demand and supply will be discussed in our next blog.
