Financial organizations like banks and insurance companies are always in the process of creating new records for new clients, or doing new deals. This adds up to the data assets of the organization, creating Big Data and the need for data cleansing services as well as advanced data analytics. The valuable information extracted via analytics can be used to track client behavior in real time, boost overall performance and profitability, and move ahead in the growth cycle.
There are three crucial areas which financial institutions should focus on to attain maximum benefit from advanced data analytics.
- Optimal customer experience: No matter how big or small the financial organization is, the main objective is customer satisfaction which plays a primary role among other data related activities. Today’s customers have high expectations about the interaction between them and the financial organization and their buying journey is intricate and non-linear. So financial organizations must understand customer preferences and motivation. To get a good view of the customers, companies need a central data hub that contains details of all customer interactions including basic personal data, transaction history, browsing history and so on. McKinsey research shows that data helps to make better marketing decisions which can increase marketing productivity by 15 to 20 percent i.e. $200 billion given the average annual global marketing spend of $1 trillion per year. Data-fuelled analytics will help to empower the BSFI (banking, financial services, and insurance) sector with customer insights and also help to create customer segmentation. For this, extra investment into the organization’s infrastructure may be needed alongside input and coalition between people across multiple functional areas of the enterprise. Even so, a customer-oriented culture along with the ideal processes and infrastructure will ensure increased conversions and revenue.
- Optimal employee engagement: Just like customer experience, employee experience is also important in a financial organization. It helps to track, analyze and share employee performance metrics. Applying data analytics to employee performance helps to identify and acknowledge not only the top performers but also those who are struggling. With the right tools and analytics, you can measure everything including individual performance, team spirit, interaction between departments and thus improve the overall work culture in the organization. Employees need not spend more time on manual processes and can focus more on higher level tasks.
- Optimization of all operations: Big data technology can improve the predictive power of risk models, provide more extensive risk coverage and generate significant cost savings by providing more automated processes and precise predictive systems with less risk of failure. Many areas in risk management can be managed using big data, including fraud management, credit management, market and commercial loans, operational risks and integrated risk management. Systems enabled with Big Data can identify fraud signals, analyze them in real time using machine learning and accurately predict illegitimate users or transactions. It also offers the ability to provide a global vision of different factors and areas related to financial risk.
This is the age of Big Data. Data cleansing services provided by data experts will help in extracting core data regarding customers, employees and other aspects important to an organization. Data experts can also help the organization use its data and work in a direction that brings maximum benefits, both internally and externally. They can help the financial industry clearly understand their specific needs related to customers as well as the organization and provide services in a timely manner, cost-effectively. The financial sector has started implementing Big Data on a large scale, and there is no doubt that the sooner they start using Big Data practices, the better are their prospects when it comes to keeping abreast with competitors in this digital era.