How is Data Analytics Redefining the Banking Sector?
Over the years, it has been observed that customer experiences (CX) in the banking sector are built on loyalty, trust, and personalized services. How banks distinguish CX depends on customer data management. This is the reason advanced data analytics has become the need of the hour. With the use of AI and data analysis technology, vast datasets can be used to derive and leverage insights to enable strategic decision-making.
Data and analytics solutions can be pivotal in managing operational efficiency, improving risk management, and enhancing customer experiences. This is because of the industry's very nature since banks have access to sensitive customer data. In addition to storing data securely, banks are also required to process it securely and more effectively, which may not be possible only with human efforts. Let's read in detail how data analytics technology has been transforming the banking sector lately.
Customer Profiling and Personalization
Banks looking to get a holistic understanding of customers and their preferences leverage advanced analytics for customer profiling. Financial organizations or banks can better comprehend customers' preferences, needs, and motivations based on information like existing banking products, credit score, and household composition. This data can help tremendously in customer profiling, which can further help personalize customers' experiences.
Banks are also heavily investing in approaches to measure customer sentiment for the institution as well as individual products/services. The idea is to create not just personalized products befitting customers' preferences but also to create personalized and memorable experiences.
Managing Credit Risk and Collection
Data analytics can help banks at various stages, including credit risk and collection. Small businesses and even larger banks often face the challenge of credit defaults. The data analysis model can help mitigate risk to a great degree with the help of financial statements and credit score analysis. Customers' profiles can be analyzed automatically as soon as an application comes in, allowing the requested amount to be put in perspective against the borrower's repaying capacity.
At the same time, collaterals' worth can be calculated, enabling more effective decision-making and decreased vulnerability to credit risk. As a result, a proactive approach to data analysis can make risk assessment faster and reduce defaults, late payments, and overall financial strain. Moreover, advanced data analysis algorithms can enable real-time evaluation of data and identification of transaction patterns and anomalies that may lead to potential fraud, thus alerting the financial organization to take preventive measures.
Improving Operational Efficiency and Reducing Cost
Data analytics help streamline internal operations by optimizing resource allocation as well as complete processes. It helps identify bottlenecks and areas for improvement in workflows, enabling more effective and streamlined operations. This allows for significant cost reductions. For example, advanced analytics insights may uncover functions that can be automated, like profile assessment of credit seekers. Employing professionals to confirm applicants' creditworthiness can waste time and resources. Analytics-driven insights can tell exactly where and how to automate a process like this, improving overall efficiency while saving costs.
Meeting Compliance and Regulatory Demands
While innovation is typically at the top of the technology agenda, it is banking institutions' responsibility to create a compliant environment and fulfill regulatory demands. This is where the role of analytics comes into play.
Many banks still have a separate team for each regulatory framework, which is clearly an ineffective approach. Analytics combined with automation and AI technology have proven to be revolutionary. Thousands of transactions and data records can be analyzed daily to identify compliance risks and potential regulatory threats. Banks can use a single source of financial business data or daily reporting data based on rules, with the ability to trace back to each transaction without any specific human efforts. Simplifying processes through automation and using analytics to manage all data centrally is a game-changer in creating sustainable banking experiences.
Unlocking the Power of Data with Tech Mahindra's Advanced Analytics Solutions
Tech Mahindra offers advanced analytics and analytics-driven solutions to help businesses achieve new standards of operational efficiency and customer experiences while driving down costs. Our advanced analytics are designed to handle rule-based transactions and back-office tasks so employees can contribute to creating business impact. With 360-degree digital transformation solutions, including cognitive automation, robotics, AI, and analytics, we aim to bring innovation to the banking and financial services vertical. With services like modernization and transformation of customer data platforms, data acquisition, master data management, metadata management, data assessment, and others, we are enabling clients across the globe to leverage the latest technology to empower banks and financial organizations.
