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Keys to extract value from the data analytics life cycle

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There is a tremendous opportunity to obtain insights from all activities that make up the analytics life cycle. Value is not limited to the end results produced from data analyses. In financial services, regulatory mandates driving transparency and financial objectives requiring accurate understanding of customer needs have heightened the importance of data analytics to unprecedented levels.

The role of data analytics in business
Across industries, the applied use of data to inform business decisions has become a foundational, if not critical, element of business. In financial services, capital itself is effectively deployed to banking activities — from lending to product and service design, through the optimization provided by institutionalized data analytics. Mathematical models that forecast customer profitability, segmentation models that simulate credit losses driving regulatory capital, scorecards that assist in credit originations, models that simulate balance sheet impacts resulting from risk factor changes, and scenario-based models that project potential impacts from broader operational or environmental risks are now commonplace. More and more, predictive analytics that take forms such as key performance indicators and key risk indicators have become the norm.

Certain global institutions are pioneering applications of analytics to automate the cognitive processing of text-based audit reports while increasing consistency over human capacity. Others are exploring causal relationships between the risk event, audit and indicator data to reveal predictive insights. Analytics can be a formidable, competitive advantage in any function, whether used to reveal insights into revenue generation, day-to-day operations or risk management. Leading institutions are using data analytics at the enterprise level to increase effectiveness of decision-making, which can yield significant financial returns.

Systematic data analyses and modeling processes can provide valuable insights as resulting outcomes, and the value of analytical expertise — along with an experienced-practitioner intuition — in supporting business decisions, and managing the inherent data and methodological limitations.
 
Download the PDF for the full article; read about performing data analyses; structuring the problem; selecting data, analytics and algorithms; typical analytic methods; and interpreting and implementing the results.

Support your future with a risk management infrastructure
Increasingly, real-time data will need to be integrated across organizational functions — e.g., linking audit, risk and finance — to optimize decision-making and risk management. Watch the video for more information.