Banks are getting Digital and are redefining their customer facing and internal processes from customer acquisition to service to middle and back offices. Digitalization is fundamentally and structurally reshaping banks from perspectives of marketing, selling, product development, customer service, operations, credit and even risk management. Three core mechanisms that are driving this Digital journey are Applications (primarily Mobile), Data & Analytics and IoT/Connected Devices. ‘Mobile’ and ‘Data Analytics’ re-imagined as AI remain top priority items for banks. For example, in a recent survey by Gartner, 32% of banks indicated that the purpose of going Digital is to attract new customers and upsell/cross-sell to customers. About 47% indicated improved customer relationships and enhanced user experiences as the purpose of going Digital. Our solution provides a framework to accelerate the Digital adoption of banks through its Data Processing, Analytics and Application Integration capabilities.

Innominds Solution for 3600 View of Customers and Cross-Sell/Upsell

A 360 degree view of customers in the past has been mainly about getting all transactions and customer data into a data store or enterprise data warehouse to be able to view data in a holistic way to get a unified view of all of customer’s activities, transactions and experiences. It also means uniquely identifying customer through master data management and other data governance processes. However, true 360-degree view means more than just unifying the data and entails getting deeper insights into customer’s preferences and intent and then be able to influence them for desired outcomes.

Our solution for cross-sell and upsell allows banks to unify customer data from various channels including Transaction Processing Systems, CRM, Mobile, Web, Social Media and even IoT to come up with insights and specific recommendations for cross-sell and upsell.


Customer Data Connectivity

Can connect to all of customer data from multiple sources including CRM, Transaction Systems, Mobile, Web, Clickstream and Social Media data without copying or moving the data through its Data Virtualization framework. iFusion can then run its federated query across all of the sources to retrieve data on demand and prepare the analytics datasets

Real-Time Predictions

Integrates to a marketing or sales application to provide the predictions in real time or on batch mode and establishes a closed loop system to verify the performance of the predictions and automatically refine the model

Optimum Storage & Data Management

Provides an efficient Storage and Data Management with tiered storage as per the retention policy of the bank

Train Predictive Models Through ML

Provides a canvas to train Predictive Models on the data through Machine Learning such as Credit Modelling, Risk Modelling, Response Models for Direct Marketing, Channel Management and Marketing Mix Models

Data Transformation

Runs a range of data processing on the dataset to transform and prepare the data for Machine Learning. iFusion comes with several data preparation algorithms and can also utilise a ETL transformation job thereby leveraging a bank’s ETL investments

Allow Combination of Data

Allows unstructured or semi-structured data to be combined with structured data through its feature extraction on text, logs, images and even videos

iFusion 3600 View of Customers and Cross-Sell/Upsell

iFusion Analytics dramatically makes analytics in a bank scalable and reliable while lowering the cost of development, deployment and ongoing management associated with large volumes of data. iFusion enables a variety of analytics on customer data both in real time and in batch mode while allowing the streaming data to be combined with historical, external and other data. iFusion Analytics’ patent-pending technology yields a robust, high-speed and extensible cross-sell/upsell solution.

Vector Smart Object

iFusion Supports a Variety of Customer Analytics that Includes:

  1. Cross-sell and upsell using the performance data in customer’s product such as credit card or checking account to then do a credit model and/or customer lifetime value model. Subsequently, it is combined with customer contextual data to score for interest in a given product. This can be viewed as a Next Best Action for the customer given a certain activity.
  2. New Digital product development and attraction models for features/attributes based on customer preferences.
  3. Customer experience improvement including onboarding, support, service requests, etc.

iFusion scales out on large data processing being capable to process several Terabytes of datasets on which ML has to be performed. iFusion Analytics’ purpose-built data store does not rely on Relational Database Management System (RDBMS) technology and is suited to store unstructured, semi-structured and unstructured data in a variety of formats including an optimized columnar format for temporal or time series data. This results in timely and cost-effective processing, as well as, high performance reporting and analysis. Finally, having access to years of data online also eliminates labour-intensive archive restoration processes.

About iFusion Analytics

iFusion Analytics’ patented, scalable and distributed platform comes with ‘out of the box’ rich analytics algorithms. It collects poly-structured data from heterogeneous sources and federated data stores. It cleans data, curates data and makes the data ready for Data Analysts and Data Scientists to accelerate insights and build solutions at reduced cost.

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