Oracle FLEXCUBE Machine Learning Unlock the potential of data

In today’s data driven world, it is imperative for a bank to drive better predictability, insights and decisioning to stay competitive.

Oracle FLEXCUBE Machine Learning enables a bank to unlock the true potential of data with capabilities that enable better predictability and consistency of outcomes, enhanced insights and improved decision making and help it establish competitive advantages.

Today, technologies like Artificial Intelligence and Machine Learning are being deployed across all touch points, including branches. We foresee a future where these technologies will complement the bank’s physical network and help our employees add value to our customers

Nitin Chugh, Digital Banking, HDFC Bank

Existing business intelligence capabilities grapple to unlock the true potential and value of data and struggle to offer predictions and insights that are increasingly essential for a bank to compete in a data driven industry and economy.

Artificial Intelligence and Machine Learning (ML) which are at the forefront of technological disruptions currently sweeping the banking industry, now offer a bank the ability to improve predictability, insights and decision making. Advances in machine learning, have significantly improved the capabilities and robustness of underlying algorithms to address established and evolving business use cases. These capabilities can help a bank maximize the business and monetary value of the extensive volumes of data sets it holds, and help establish competitive advantages, not just over peers, but also over a range of new age digital competitors.

Oracle FLEXCUBE’s approach to machine learning stems from decades of domain expertise and global industry exposure. The cumulative insights collated over years of delivering core banking solutions have laid the foundation for the Oracle Machine Learning Framework - a robust ML model framework designed to work in tandem with Oracle FLEXCUBE.

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The Oracle FLEXCUBE Machine Learning Framework is designed to help a bank jumpstart the use of machine learning capabilities. The framework is designed to work in tandem with existing Oracle FLEXCUBE installations and leverages existing data structures, data generation routines and ready integration with the Oracle database allowing for a faster ‘lift-off’. The framework is built to leverage Oracle’s powerful back end infrastructure which offers scalability to handle large datasets and processing requirements.

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ORACLE FLEXCUBE MACHINE LEARNING FRAMEWORK USE CASES

Customer Attrition

Oracle FLEXCUBE’s Machine Learning Framework offers a bank the capability to analyze data points like historic transactional data, account balance data, service requests etc. and learn and identify hidden behavioral patterns to help identify customers at risk of attrition.

The framework can then be used to build predictive insights into a foreseeable future period – defined as the ‘window of intervention’ which provides a bank the ability to engage with high risk customers and maximize positive outcomes.

Customer Segmentation

Oracle FLEXCUBE’s Machine Learning Framework includes the capability to learn in an un-supervised manner and categorize customer profiles with certain behavioral characteristics into clusters.

This segmentation offers a bank deeper insights into customers who exhibit similar transactional behavior such as risk appetite, risk response, openness to new opportunities and product exposures, investment patterns etc. These insights can help a bank improve targeted marketing and engagement efforts to customer segments and sub segments.

Customer Lifetime Value

Oracle FLEXCUBE’s Machine Learning Framework enables a bank to predict a more accurate lifetime value of a customer using historical transaction data and revenue data. This lifetime value, when viewed in association with the revenue of the segment to which the customer belongs to, helps a bank identify the relative positioning of the customer in the value hierarchy and help it better focus efforts and engagement on high value customers.

AI in Financial Services

Approximately USD 10 billion will be invested in AI by financial institutions by 2020.

KPMG

A bank that runs its operations on Oracle FLEXCUBE generates data which has typically been used for business intelligence and regulatory reporting. Now, with Oracle FLEXCUBE’s Machine Learning Framework, a bank can leverage the predictive capabilities of machine learning within the Oracle FLEXCUBE application to unlock new business value from data.

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The Oracle FLEXCUBE Machine Learning Framework enables a bank to unlock the true potential of data and drive competitive advantages with capabilities that enable better predictability and consistency of outcomes, enhanced insights and automated decision making. Intelligent and data driven, the framework leverages machine learning algorithms and data sets to focus on real world banking use cases and business issues while offering quantifiable value.

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Value from AI

50% - the potential incremental value from AI over other analytic techniques in the banking industry.

McKinsey Global Institute - Notes from the AI Frontier

THE ORACLE FLEXCUBE MACHINE LEARNING ADVANTAGE

Designed for Business Value

The Oracle FLEXCUBE Machine Learning Framework is designed to maximize business value. The Framework offers factory shipped use cases and functions which allow a bank to jumpstart the use of machine learning and unlock value from its data sets through improved predictability of outcomes and quantifiable business value.

The framework also offers a bank the flexibility to incrementally introduce additional use cases according to its strategic as well as its operational and business requirements.

Built to Reuse

The Oracle FLEXCUBE Machine Learning Framework reuses existing Oracle FLEXCUBE data structures and data generation routines without the need for any re-alignment. Existing data can be harnessed by a bank to enable faster implementation, roll out and accelerated realization of benefits.

Additionally, the framework provides ready integration with the Oracle database and is designed to inherently leverage its backup and recovery mechanisms enabling operational and cost efficiency for a bank.

Architected to Support Scale

The Oracle FLEXCUBE Machine Learning Framework supports the co-habitation of data and algorithms which enables the processing of millions of rows of data, allowing a bank to scale machine learning across larger data sets and thereby unlock greater value. The framework’s parallel execution of data and tasks provides a bank with greater processing power per second, ensuring high performance even at large scale.

The framework also offers a bank the freedom to leverage open source R packages, thereby offering multiple options and flexibility in extending machine learning capabilities.

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“91% of surveyed bank employees said they believe cognitive/AI technologies will empower or support employees, rather than replace them.”

— Deloitte

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