Ever wondered how some companies manage to delight their customers by offering just the right kind of products and services at the right time? Clearly, they have developed some kind of mechanism which helps them analyze a user’s consumption pattern. One of the advanced analytics techniques that uses both historical and new data to forecast activity, trends and behavior is Predictive Analytics.
This unique method applies analytical queries, automated machine learning algorithms and statistical analysis methods to data sets for designing predictive models that place a score on the probability of a particular event happening. Many sectors including banking are using predictive analysis to reliably project future behaviors and trends.
Talking about the banking sector, there is an increasing need to fulfill the expectations of a growing discerning consumer base, given the number of analytic tools available in the market. Banking industry needs to shift from using analytic insights to design exceptional report of past events, to applying this data to cherish remarkable customer experiences and associations based on future needs. And this is where Predictive Analytics comes into picture. It is the future of banking industry. With Predictive Analytics banks can forecast when a customer might look for a specific financial service solution.
So, let us have a look at some of the key areas in banking where predictive analytics can prove to be of value:
Customer First
Changing customer needs and market trends indicate that it is high time banking sector moved away from its siloed approach and focused more on what the customer wants. Predictive Analytics exhibits the power to strengthen the relationship with customers and builds trust, especially at a time when digital-natives are introducing customer-centric digital solutions and are progressively gaining foothold in the financial services industry.
Fraud Prevention
The Banking sector needs to design solutions not only to save their capital, but also to save the identity of their customers. Cyber criminals remain on their toes to grab any single opportunity to commit fraud. Hence, banks need robust tools and intelligent systems to avert such problems. Predictive Analytics is an excellent way to identify instances of frauds that are not very evident and analyze them further.
Application Screening
Application screening process has turned much easier with Predictive Analytics. Bank staff can process applications in bulk in lesser time and increased accuracy. Also, there is almost no probability of excluding important variables during this screening process using predictive analytics.
Customer Retention
With growing competition, it has become extremely tough for banks to retain customers. Predictive Analytics works as a mechanism that can help banks know when an existing customer is looking to switch to its peers, and what could be the probable reason behind it. It is done by a comprehensive analysis of customer’s spending pattern, investment inclination and other interests, service performance, and past service details, among other factors. Once the problem area is identified, banks can take timely measures to retain their customers.
Cross-Selling
Getting a customer is tough and retaining it is even tougher, but if banks have the right plans to grow the share of their consumer’s wallet, cross selling of other products helps. Using predictive analytics, banks can build models and assign scores to customers, to present the probability of the customer looking to buy another product. Eventually, this approach leads to increased revenue per customer.
Conclusion
The above benefits are just a fraction of what actually banks can achieve using Predictive Analytics. However, implementing Predictive Analytics needs high level of expertise and specialized skill sets with statistical methods and the knack to design predictive data models.
