Banks are always in the process of creating new records for new clients or doing new deals. Typically, there is a never-ending array of forms that need to be completed. These ever increasing piles of documents could mount up until the office is overrun with paper. A practical solution is to convert all crucial data into digital format and this can be efficiently done with the help of data entry services. Today banks, credit unions, lenders and other financial institutions are working hard to adapt to a fully Big Data-driven approach to grow their business and enhance their services.
Employers have started matching Big data with surveys on employee satisfaction to increase productivity. The Bank of America has made its workers wear badges having sensors to gather information regarding social welfare importance for call center employees. Big data can also help analyze customer spending habits and demography analysis, and companies can decide on the optimal times to present offers to their targeted customers. The advancements in communication and technology along with tremendous growth in data and information have given rise to a more informed and empowered consumer. Now, the banking sector must develop an improved customer engagement strategy by really getting to know their customers, providing a multi-channel experience, making a genuine effort for relationship-building, and thereby earning the confidence and trust of customers.
Big Data analytics can help banks reinvent themselves and address the FinTech competition efficiently. Analytics of Big Data involves examining or studying large data sets to identify market trends, customer preferences, data relationships, unknown patterns and other valuable insights that can prove useful. Accurate analytics will help banks make informed decisions and draw inferences so that they can improve their processes and ensure outstanding customer experiences.
There are four crucial areas that banks should focus on to attain maximum benefit from advanced analytics.
- Customer experience: Whether the organization is big or small, the main objective is customer satisfaction which plays a primary role among most data related activities. Today, customers have high expectations about the interaction between them and the bank and their buying journey is complex and non linear. So, financial organizations must understand customer preferences and motivation. To get a good view of the customers, banks need a central data hub that combines all customer interactions including basic personal data, transaction history, browsing history and so on. By studying purchasing history, profile data, social media data, and browsing history, data analytics can enable banks to acquire new customers and retain existing ones. As per McKinsey, 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 helps to empower the BFSI (Banking, financial services and insurance) sector with customer insights and helps to create customer segmentation. This information can be used to develop new products/services and also create further marketing campaigns. For example, established banks in Australia such as Westpac are leveraging the power of data analytics to provide more targeted offers and campaigns to their customers. At present, they have an app with a geolocation service. A customer travelling overseas can turn on this geolocation feature, and the bank would offer her additional foreign exchange or insurance services that suit her needs – this is a good example of how banks can use Big Data to provide a better customer experience.
- Leverage data insights: According to a Frost and Sullivan whitepaper, data analytics insights hold the key to solving issues and capturing market opportunities. Bitcoin and other new technologies are providing consumers with alternatives that may potentially replace banks altogether. Many businesses now manage big data but the pace of business now demand a faster approach. Banks should help their teams understand the data, measure the impacts of their actions and adequately communicate insights. Banks can now use customer information to continually track client behaviour in real time, and provide the exact type of resources needed at any given moment. This evaluation will help increase overall performance and profitability.
- Employee engagement: Employee experience is an important factor in any 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 struggling workers. With the right tools and analytics, you can measure everything right including individual performance, team spirit, interaction between departments and thus improve the overall work culture in an organization. The employees need not spend more time on manual processes and can utilize more time for higher level tasks.
Optimization of 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.
Optimization of 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.Predictive analytics systems can analyze the credit history of customers, loan, credit applications and other data to assess whether the customer will make his/her payments in the future. Banks can train machine learning algorithms to automate many of their processes – this is a very important thing. Artificial intelligence (AI) solutions can bring about remarkable transformation in how banks deal with regulatory compliance issues. According to McKinsey, at least a dozen European banks have already moved from traditional statistical analytic modelling to machine learning. Many of these banks cite increased new product sales of 10%, and churn and capital expenditure down by 20% as a result.
The possibilities offered by Big Data analytics are endless, as any data entry company will agree. Successful usage of Big Data and digitization of business processes rely on the organization’s ability to collect and process all the required data, and utilizing it effectively. Big Data analytics is at present being implemented across various areas in the banking sector; it is helping banks to deliver more value-added services to their customers.
