Banking Analytics: Role of Analytics for Banking Operations

Digital revolution and technological advances have helped businesses and industries, including the banking industry, to evolve. Digitization, AI & data analytics has impacted all industries, including the banking sector in a positive way. Data analytics can be defined as analyzing raw data to reveal trends and other metrics that otherwise would be difficult to spot in big data streams. The information can then be used to optimize processes to enhance the business and system efficiency.

Banks must make sound financial decisions based on reliable information and analysis to sustain the business, improve revenue, and increase profits. It is equally essential for banks to limit their risk exposure and minimize possible losses and liabilities without restricting growth. In this world of digitization, banking apps, IoTs, websites & more generate a stream of data that can generate reliable, actionable insights that can help refine decision-making. Data analytics is the key to deciphering this massive volume of raw streaming data.

Know how banks can leverage data analytics to make informed decisions & improve banking operations-

1. Customer profiling- Data analytics can help reduce risks by accurately analyzing the data streams from various sources and presenting this data in an easily identifiable form that can enable banks to make informed decisions regarding the profiles of their customers. An accurate picture of customers can be generated using advanced analytics, such as who they are, their motivation, which banking products they have already availed of, and much more. The information generated from the raw data can help identify what banking product the customer needs rather than what is to be sold to them. This level of insight can help banks deliver the right product or service.

2. Omni-channel customer experience- Personalization is an essential part of a customer experience and has taken off as a trend in the banking industry. A Capco study has found that 72% of customers rate personalization as "highly important" in today's financial landscape. It makes customers feel they have been seen, heard & understood, which contributes to a satisfying customer experience.

Personalization has other advantages, like reducing customer churn and increasing sales. According to Forrester, banks that lead in customer experience (CX) have better revenue growth. It offers a better brand preference and retains more customers than those who neglect customer experience.

3. Risk management and mitigation- Data analytics can assist banks in risk management and mitigation in many ways. Using data analytics, banks can segregate customers as per creditworthiness for credit risk management. It helps banks narrow down the target audience for credit products and mitigates bank risk exposure.

Predictive analysis can help banks prevent potential fraud by analyzing customer behavior patterns and flagging any anomalous behavior.

Using data analytics, predictive analysis & other metrics for fraud detection and prevention does not help banks reduce or eliminate risks. Further, it helps mitigate reputational damage and retaliatory action protects customer interests.

4. Reducing operational costs- Reducing operational costs helps increase profitability and efficiency. Data analytics can help identify the weak spots within banks and enable them to deal with these effectively. It also helps in uncovering unnecessary expenses which can be reduced or eliminated. Prescriptive and predictive analysis can help generate strategic recommendations on optimizing existing processes to drive efficiency and lower costs.

5. Growth opportunities- Analytics and other metrics enable banks to engage in targeted sales and marketing campaigns attuned to customer needs. Analytics support up-selling and cross-selling strategies, which can help the sales and marketing teams offer complementary products and services that can deliver value to customers. Analytics can help determine which customers give more business and also understand high-value customer segments that can be approached for sales and marketing initiatives.

6. Reports and tracking- AI and ML data analytics help generate reports free from human error. It can also help track a wide range of variables across various services, actions and products to better cater to customers' changing needs.

Data analytics can produce more accurate, transparent reports from volumes of data that can help operational efficiency and eliminate the need to make adjustments that can prove to be more costly later on.

In conclusion, banks should hire software companies that can leverage the power of data analytics and specialize in crafting exceptional customer experiences to define successful bank strategies. Banks need to integrate the existing infrastructure with flexible, robust, user-friendly data analytics tools. Access to data is valid only if a system is in place to effectively process the data, enabling banks to make informed decisions. This way, banks can focus on meeting meaningful, vital objectives. Data analytics can help the banking industry discover and revolutionize the banking processes for the better.