Data, Process and Scenario Analytics: An Emerging Regulatory Line of Offence
Data, Process and Scenario Analytics: An Emerging Regulatory Line of Offence
Dr. Ramendra K Sahoo, Director in KPMG’s Financial Risk Management.
From retail transactions to trading, from sports to doctor’s office, no doubt that we generate, use and process a big chunk of data. Whether it’s a financial services or a sports league, the operating models are fast changing.
As a society we are less concerned with the collection and processing of information, while focusing more how we can make sure that the right processes and timely set of actions are carried out with right set of data and information.
Whether large or small decisions, there is a shift in paradigm, how we act and use our information to arrive at our final outcome. Not only there is an appetite to collect and process a large amount of data, but also a growing demand to establish an effective measurement and metrics assessment. As a last step we are looking for scenarios with probabilistic chance of their occurrence, which can bootstraps the analytical framework and our line of act. In other words,
there is a change in paradigm with a strong emergence of line of offence, which uses right data along with an optimal set of processes and well defined set of scenarios to become more proactive at every decision point.
Beginning with a data and analytic framework, we explore few upcoming and emerging trends, which make sufficient in roads within the corporate world to establish as an emerging analytical framework.
• Establishment of text and natural language processing as main stream tools and techniques
The big data techniques are no more limited to the corporate and consumer data and information. The natural language processing, voice and text analyses are fast becoming part of the mainstream risk and compliance tools for proactive enablement and as a standard frame of analytical reference. The science of mining patient records, doctor’s prescriptions are targeting financial services and regulations to automate and process the changing regulatory needs at a faster pace.
• Mining business processes to bring in transformations to best practices
The data collection, validations within organizations are very much dependent on the underlying policies, procedures and best practices. Coming up with right and good set of data and information is imperative, as there is a shift in focus towards establishing optimized best practices, policies and procedures; often through the use of emerging tools and techniques (e.g. process mining, sentiment and outcome analysis etc.) Underlying business processes and best practices very much decide how the data needs to be collected, analyzed and outcomes to be used within decisions. As collection, validation and establishment good data becomes imperative, there is more focus on how to mine and operationalize optimized set of processes, which can be socialized to become the best practices.
• Rapid scenario development and mobilization
With the abundance of computing power, simulation and modeling of a wide range of scenario are becoming more common. As consistency and transparency becomes prevalent from measurement and metrics standpoint, establishment of scenarios are fast becoming the frame of reference for any possible deployment. As a result, predictive modeling is fast graduating to a level where a well data and optimized process centric best practices can be operationalized for rapid scenario development and mobilization.
In a nutshell, whether it is financial institutions or retail service provider, they are undergoing fundamental changes and transformations, with a number of proactive measures and more geared towards an emerging paradigm of line of offence.
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