Predictive Analytics in Banking

Predictive analytics helps organizations use their data to make better decisions. This is done by arriving at reliable, data driven logical conclusions about the current and future events. This is achieved by using a variety of data mining, statistical, game theory, machine learning techniques to make the predictions.

Predictive analytics enables the organisations customer focused finding their business issues proactively in real time and addressing them at the right time to get the best outcomes.

Application of Predictive Analytics solutions in the banking industry include, Cross Sell and Upsell, Customer Retention, Segmentation, Application, Fraud detection, Account transaction management, Collections, and Cash/liquidity planning.

Predictive Analytics in Banking- Modelling

Predictive Modeling in Banking

Predictive Analytics in Banking- Solutions

1.Cross Sell and Upsell :

Cross selling is risky in banking and if the customer doesn’t like the additional product being sold, then the customer relationship with the client could be disrupted. Traditionally some of the retail bankers are adverse to the risk. The Predictive Analytics in Banking solutions helps the banks to identify the risks and manage the cross selling and upsell effectively.

2.Customer Retention :

By analysing the helpdesk transcripts, logs and activities the predictive analytics in banking solution helps in identifying the customers who probably are going to leave and look for other service provides.

Predictive Analytics in Banking

3.Segmentation:

Effective segmentation of customers and modeling for value building for the top customers.

4.Application:

Can use the risk models to identify potential fraud in applications for account / line of credit and mortgage.

5.Fraud detection :

To identify fraud in transactions.

6.Account transaction management:

Identify potential issues with the data of management of account.

7.Collections :

Enables the banks to model the customers to segments where there is high provability default.Model different approaches for collection management and for identifying these as high risk scenarios.

8.Cash/liquidity planning:

Efficient cash/ liquidty planning for ATM's and Banks.

Predictive Analytics Software

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More Information on Predictive Analysis Process

Predictive Analytics Process Flow

For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.