If we think about most valuable data, then we can say Banks and financial Institution have the most fruitful and sensitive data. A great deal about individual and group behaviors. What’s more, this intelligence can be presented in a timely and usable form to marketing, product development, customer service, and other teams to help them make better decisions—faster—to improve customer experience, reduce risk, and grow profit.


Analyzing very large amounts of customer data in real-time and automatically sending alerts when a pattern changes or we can send offers when a pattern falls in a specific designed category.

We can design our information block we call them Indicators in SIFT, in such a way which can indicate about fraud which can reduce the risk of loss.

Real-time visibility and analysis of customer behavior over time can increase sales, maximize customer profitability, and improve retention by providing actionable intelligence to reach the right customer, at the right time, through the right channel, with the right offer.

More specifically, as banks are demonstrating, customer data can be used to:

  • Maximize profitability by identifying which customers should be migrated to which more appropriate products, channels.
  • Provide greater insight of a customer behavior and how they evolve over time.
  • Predict the likelihood of customer attrition and trigger proactive intervention at a specified threshold.
  • Provide up-to-the-minute tracking and trend analysis of product usage and channel preferences.
  • Assess the potential for profit, as well as risk, before offering a product.
  • Identify high-value customers and personalize offers, services, and rewards that match their preferences

Over the years, bank stakeholders have made considerable investments in data infrastructure and projects to leverage customer data. Now it’s time to capitalize this data Ocean.


In contrast, consider the potential unique insights that could be gained through timely analysis of a bank’s existing customer checking account and credit card data. A complete picture of customer financial behavior emerges,

  1. Where money comes from and how it is being spent. Which customers keep a high balance in their checking account?
  2. Which pay off their entire credit card balance every month? Which checking accounts fall to near-zero between paycheck deposits?
  3. Which customers consistently pay only the minimum on their credit card balance?

In SIFT, we can create some indicators that can help us to understand the basic expense behavior of the customer for e.g:

The SIFT Indicator can be:

CUSTOMER_EARNING_SOURCE and the possible values can be SALARIED, BUSINESS_MAN, PENSIONER and there can be multiple sub categories for these.

Another Indicator can be AVERAGE_BALANCE, these Indicators contains an important information and will be used for sending offers from SIFT.






Add the ability to track and compare patterns of customer behavior over time, and it’s possible to glean intelligence that could be applied in multiple ways to increase revenue and improve profitability by sending appropriate offers. Data could be used to identify and target certain types of savers or spenders with specific campaigns.


Customer analysis should be based on behavioral patterns, not just balances, SIFT can help to understand this behaviour and help the customer(Banks) for taking appropriate actions.

Customer transactions and behavioural trending and analysis can provide important clues to a strengthening or weakening relationship.

We can design our Indicators with the basic information of a banking customer and with the help of these Indicators we can trigger offers on the relative behaviour for e.g:

Elementary data can be: Name, Age, Address, Gender, Occupation, Marital Status, Phone number, Email ID, Monthly Income(Salary), Products.

We can design our Sift indicator for Credit card Information:

START_DATE : 22/10/2018,
EXPIRY_DATE : 21/10/2021,

And with this small example we can create a trigger for current month offer is being opted or not.There can be so many other possibilities like:

What can be our data for triggering condition?

Monthly income, expenditure behaviour, Average account balance and DBR Rating, Customer Age, Marital status.

What can be our triggering condition?

We can also monitor the customer transaction behaviour and try to sell many banking products.

Possible products:

  • If customer is using his ATM/Debit card frequently for purchasing petrol, we can offer Petroleum Credit Card.
  • If customer travels abroad frequently we can offer forex card.
  • If customer is transferring money frequently to a particular account, we can offer a joint account.
  • On the basis of customer age and average balance in account we can offer for Home Loan, personal loan etc.
  • We can also offer him insurance on the basis of his transaction for medical expenditure.
  • On the basis of average balance, we can offer Fixed deposit, Recurring deposit account or SIP.

With the above examples, we can see that in Sift we can offer the most appropriate product to a customer and “that is great actually” 🙂 .

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