Financial Services to Harness Machine Learning and Artificial Intelligence to Elevate Quality of Data-Driven Decisions -- KPMG LLP

SILVER SPRING, Md. - EntSun -- 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.

More on EntSun News

• Pastor Carlton L. Aiken Sr. Celebrates 55 Years with "Sermonic Selections"
• J. Wes Seeks Jazz Musicians, Vocalists and Bands for Philadelphia Live Music Event
• NECP Promotes Chertova to Senior Business Analyst
• Security Industry Veteran Anthony Rumore Launches AegisLead to Help Security Companies Find Business and Manage Growth
• Actor Ray Buffer Attends High-Profile "RUSH" Red Carpet Screening in Los Angeles

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 heterogenous 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.

More on EntSun News

• Charlie Shaw and Patrick Carragher win SEO Agency of the Year 2026
• Canadian Woman Plants 1,667 Love Stories in a Field of Peonies That Could Bloom for 100 Years
• Is Podcasting The Death Of Radio? Radio Veterans Leave The "dial" In Favor Of Launching New Podcasts
• New Research Spotlight, Expanding TUSC2 Science & Strategic Development Milestones Put Gene Therapy Innovation into Focus for Genprex (NAS DAQ: GNPX)
• 70-Year Entertainment Veteran Bruce Belland publishes 2nd book: "Famous... But Unknown"

"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.

To read the full interview or listen to the podcast with Tom Haslam and Brian Radakovic, visit:

https://www.biztechreports.com/news-archive/2021/5/3/financial-services-to-harness-machine-learning-and-artificial-intelligence-to-elevate-quality-of-data-driven-decisions-kpmg-llp