Financial Services to Harness Machine Learning and Artificial Intelligence to Elevate Quality of Data-Driven Decisions -- KPMG LLP
As financial institutions wrestle with an exploding amount of data from a growing number of sources in a variety of different formats, it has become increasingly difficult to ensure that decisions are made based on high-quality information.
The financial services sector is taking a hard look at the role artificial intelligence (AI) and machine learning (ML) can play in monitoring key elements of the data -- such as quality, lineage, metadata, and master reference data -- as it moves through an enterprise data lifecycle management pipeline.
KPMG has developed a solution called Ambient Data Management that leverages AI and ML technology to automate the process of ingesting, profiling and analyzing data to uncover and eliminate anomalies.
The financial services sector is among the most mature users of enterprise systems in the economy. Established institutions often preside over generations of computing platforms that have evolved over the years to accommodate on-premises data centers, private-cloud and public-cloud infrastructures, according to executives at KPMG.
“As enterprises wrestle with an exploding amount of data from a growing number of sources in a variety of different formats, it has become increasingly difficult to ensure that decisions are made based on high-quality information,” says Tom Haslam, Managing Director at KPMG Digital Lighthouse.
Timely access to the best information has put the issue of data quality at the center of business transformation initiatives that are critical to the continued and sustained success of established institutions over the months and years to come. The immense volume of data, residing in heterogeneous and often fragmented infrastructure spread out across the enterprise, can generate a lot of noise that makes it difficult to assess what information is valid for decision making.
It is for this reason that executives in the financial services sector are taking a hard look at the role artificial intelligence (AI) and machine learning (ML) can play in monitoring key characteristics of the data— such as quality, lineage, metadata, and master reference data—as it moves through an enterprise data lifecycle management pipeline.
“This is important for a number of reasons—especially in the financial sector. For one thing, global financial institutions face an increasing amount of pressure from regulators, clients and internal auditors to improve the quality of their data,” explains Brian Radakovich, Managing Director, KPMG Financial Services Data practice.
“Within financial services, traditional methodologies of data quality management are still often executed in a manually-intensive manner that requires lots of human intervention. Given the sheer amount of data in today’s environment, these traditional methods cannot ensure data quality management at scale,” he says.
Ambient Data Management Brings Clarity to Complex Environments
As a result, new approaches are needed for data that dynamically moves across on-premises and different cloud environments to create an effective and well-managed data pipeline.
KPMG has developed a solution called Ambient Data Management that leverages AI and ML technology to automate the process of ingesting, profiling and analyzing data to uncover and eliminate anomalies.
“For enterprises that are just beginning the journey, the first step is to identify a troublesome dataset that presents an issue to the business. Applying our ML methodologies and industry understanding to this data helps our clients discover anomalies and deliver visibility into data quality issues. With this understanding, we are able to recommend resolutions to quickly mediate these issues and ultimately improve business outcomes,” says Haslam.
“Once identified, data analysts can determine the nature of their data quality issues and begin to drive tactical or systemic remediation. As AI/ML models learn and become more mature, institutions will be able to zero in on the true data quality problems across the enterprise and develop automated processes for refining data in a dynamic and continuous manner,” says Radakovich.
Over time, more and more data sets are managed using the KPMG Ambient Data Management solution, and data quality increases across the enterprise. The application of the solution leads to greater trust in enterprise data. Greater trust in data quality—in turn—facilitates the development of new financial products, better analysis and more effective adoption of new technologies.
“As a result, more data can be processed more quickly to resolve real anomalies while eliminating false positives and false negatives. When institutions get enterprise data quality right, it improves the digital culture that must be in place to achieve digital transformation objectives,” Haslam concludes.
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