How Data Analytics Is Helpful In Banking & Financial Sector

Today we will discuss about how banks and financial institutions use data analytics to overcome issues and challenges such as low revenues, security threats, and heavy workloads in various areas of demand, supply, and risk management. The increasing interest in the use of data analytics in the banking industry is due to the increased changes that have been happening in this sector. Changes in technology, changes in people’s expectations, and changes in market structure and behavior. The advent and application of data analytics have helped the banking industry optimize processes and streamline its operations, thus improving efficiency and competitiveness. Many banks are working on improving their data analytics, mainly to give them an edge against competition or to predict emerging trends that can affect their businesses.

Most of us have a trustworthy relationship with our banks and financial institutions. Our relationships with banks are built on trust, loyalty, and personal service. However, the increasing sophistication of banking services and products has fueled the need for effective decision-making tools to enable better decisions from data insights. Viewing documents and numbers alone is not something that can influence your businesses any more. The banking sector needs to utilize its data for analysis and better decision-making. When you analyze data, you will be able to determine how you can maximize your profits and improve business relationships and customer service. That’s where you need data analytics. Evaluating your documents and transactional data will help you create a better picture of your business and its operations.

Data analytics in the finance and banking sector is mainly used in demand, supply, and risk management. While the traditional approach to analytics in finance and banking was to generate reports and dashboards, today’s banks and financial institutions are using data analytics in a more purposeful way. Banks want to know whether their customers are paying on time. They want to know how their customers use their credit cards. And they want to know whether customers are using certain products with the bank. Also, to keep track of security aspects with a predictive approach than a reactive approach. Though data analytics is becoming common for banks and financial institutions, it is still fairly new. It’s not yet a standard practice, and it is not always used in the same way by every bank and financial institution.

Use Cases of Data Analytics Used by Banking & Financial Offices

Fraud Detection:
While fraud reduction is a common goal for banks and financial institutions, 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 allows banks and financial institutions to know what to prioritize in their fraud detection efforts.

Risk Modeling:
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. When risk modeling is done correctly and consistently across all assets, you can reduce your portfolio’s overall risk and improve its performance. 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. We will look at more such models below.

Operational & Liquidity Risk:
The term “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. In contrast, 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, such as bonds. The operational risks are possible losses that result directly from risks associated with day-to-day operations of the institution, i.e., fraud, theft, computer security breaches, or error in judgment or incompetence at an executive level. Whereas, Liquidity risk is the threat that a bank’s assets will fall below the amount required to meet its liabilities.

AI-Driven Chatbots & Virtual Assistants:
AI-driven chatbots and virtual assistants can help you reduce the amount of time you or your employees spend on your daily tasks. These chatbots and virtual assistants can be used in many situations like: to assist in customer service and knowledge management, to replace manual processes such as emailing or calling rooms, to increase customer engagement through personalized interactions; obtaining knowledge about their clients habits so they can offer them more personalized solutions; providing more meaningful advice on investments; offering advice based on what clients have already invested in; and improving customer relationships through effective marketing campaigns.

Personalized Marketing:
For financial institutions, the main challenge is managing the demand side of the equation. By focusing on their most profitable customers, banks can secure profits from a system that gives them access to a customer they may not have had otherwise. In order for banks to achieve this, they must know who their most profitable customers are. This is where analytics comes into play. Today every banking institution uses a variety of data sources to determine who they should target with marketing messages and offers.

Customer Value Prediction:
Customer lifetime value (CLV) is a term used to describe the amount of money a customer is likely to spend with a bank over the course of their lifetime. This is different from the traditional view on brand value, which refers to how much a customer is willing to pay for a product or service. In order for banks and financial institutions to optimize their business models, they need to consider both customer value measures. Traditional analytics tend to focus on the former while ignoring the latter, which can significantly impact revenue. And it’s not surprising that CLV prediction has become one of the most important tools in understanding customers’ needs and wants.

Recommendation Engines:
Banks and financial institutions are not only looking at how their current customers use the products they offer; they are also focused on how to attract new customers. When it comes to managing the demand side of the equation, banks and financial institutions are using analytics to develop predictive models that take into account individual customer characteristics. Like a credit score, income level, etc., and things like location, which can be used to evaluate consumer behavior patterns. The models provide insight into how different segments of the population behave, which allows for more personalization of products and services.

Data analytics is now a key component for the successful running of financial institutions. It consolidates all the data and reports into a small amount of data. Once you have all your data, you can easily draw conclusions from it.