By soliciting loads and loads of data, financial services company can work on the new data driven business opportunities. To begin with the same, you need to create a solid data management that supports the analysis Big Data as well as enterprise data. Once you have built the foundation, you can execute machine learning algorithms for working on automated decision making and data driven process optimization. This helps in generating insights that lead to better customer experiences, enhance operational efficiency and drive sales. Here are the practical uses of data analytics financial services.
- Enhance the customer experience and drive growth
Offer customized services on the basis of customer satisfaction, buying history, preferences, and demographics to comprehend their needs. Suggest the next best product for purchasing with the help of deep insights to correctly gather leads into segments on the basis of their profile and needs. Make the most of these insights to cross sell and up sell which can be stimulated at the right time and with the right channel. You can also automate personal finance management to give customers a glimpse of their finances and advice. Determine investment opportunities on the basis of customer risk profiles and the funds available, use previous spending information to learn the trends, encourage re-mortgaging and better customer savings habits.
- Optimize risk controls and business outcomes
Offer early warnings with the help of liability analysis for determining the potential risks. You can work with customers for liability management and limit bank exposure. You can also predict the risks for loan delinquencies and suggest proactive maintenance strategies by segregating delinquent borrowers and determining self-cure customers. With the help of this, banks can customize collection strategies and enhance on time payment rates. You can also enhance collection and recovery rate with the same. Apart from that, you can predict the risks for individual customers and suggest retention strategies to enhance customer loyalty. Determine at risk customers and take prompt action to retain them. Detect frauds if any etc.
- Automate business processes
With this you can determine credit risk with the help of automated real time credit decisions on the basis of age, address, income, loan size, guarantor, rating, job experience, and transaction history. The customer complaint management system makes the use of the data from multiple channels to learn why customers complain, find dissatisfied customers, seek the root causes for the same, and respond to them ASAP. To learn more about the same or to explore more insight on cloud analytics, visit the website.