Jim Littley

Co-Founder

Philadelphia, Pennsylvania, United States39 yrs 3 mos experience

Highly StableAI Enabled

Key Highlights

Over 30 years of advisory experience
Leader in Continuous Auditing methodologies
Adjunct professor in advanced accounting topics

Stackforce AI infers this person is a seasoned expert in Risk Consulting and Forensic Accounting with a focus on Data Analytics.

Contact

Skills

Core Skills

Data AnalyticsForensic AccountingHigher Education TeachingContinuous AuditingRisk Management

Other Skills

analyticstext miningprocess miningrobotic process automationgenerative AIPrompt EngineeringApplied Process MiningIntelligent AutomationAudit AnalyticsContinuous MonitoringInternal AuditRisk AssessmentFraud Risk AssessmentFraud InvestigationsRegulatory Compliance

About

Jim was an Advisory Services Partner in KPMG’s Risk Consulting practice. He was KPMG’s Global and Americas leader for Continuous Auditing & Continuous Monitoring Services. Jim has over 30 years of experience providing advisory services to clients in the areas of risk assessments, forensic investigations, regulatory compliance, contract compliance, data analytics, and continuous auditing and continuous monitoring across a variety of industries. Jim now serves as an adjunct professor at Rutgers University, Rutgers Business School, teaching graduate school courses including Accounting in a Digital Era, Intelligent Automation in Accounting & Auditing, and Audit Analytics (graduate and undergraduate). Jim’s area of focus includes the use of leading practice low code/no code software, and related technologies, for data & analytics, visual analytics, process mining, intelligent automation & robotic process automation, and Generative AI & Prompt Engineering in accounting, auditing, regulatory compliance, and forensic accounting investigations applications.

Experience

39 yrs 3 mos

Total Experience

10 yrs 7 mos

Average Tenure

8 yrs 3 mos

Current Experience

Rutgers university

2 roles

Senior Fellow

Jan 2024 – Present · 2 yrs 3 mos

Rutgers Accounting & Research Center / Continuous Auditing & Reporting Lab (CAR Lab)
Jim’s areas of research include analytics, text mining, process mining, robotic process automation and generative AI & Prompt Engineering in auditing and forensic accounting investigations applications.

analyticstext miningprocess miningrobotic process automationgenerative AIPrompt Engineering+2

Adjunct Professor - Department of Accounting & Information Systems

Jan 2018 – Present · 8 yrs 3 mos

Jim serves as an adjunct professor at Rutgers University, Rutgers Business School, teaching graduate school courses including Accounting in the Digital Era, Intelligent Automation in Accounting & Auditing, and Audit Analytics (graduate and undergraduate). Jim’s area of focus includes the use of leading practice low code/no code software, and related technologies, for data & analytics, visual analytics, process mining, intelligent automation & robotic process automation, and Generative AI & Prompt Engineering in accounting, auditing, regulatory compliance, and forensic accounting investigations applications.

Applied Process MiningHigher Education TeachingIntelligent AutomationAudit AnalyticsData Analytics

Kpmg us

Partner

Sep 2004 – Sep 2017 · 13 yrs · Philadelphia

Global and Americas Leader for Continuous Auditing & Continuous Monitoring Services
Internal Audit, Risk and Compliance Services
Contract Compliance Services
Fraud Risk Management Services
Forensic and Investigative Services
Led the development and deployment of KPMG's global Continuous Auditing & Continuous Monitoring methodology and responsible for the development and execution of KPMG's U.S. Internal Audit data analytics and continuous auditing strategy.
Assisted internal audit departments with the development, implementation and evaluation of data analytics and continuous auditing strategies, processes and programs including technology enabled continuous risk assessment processes and improved audit effectiveness and efficiency.
Extensive experience with the use of data analytics tools and techniques to identify and evaluate anomalies, trends and patterns in financial, operational and transactional data relating to fraud, waste, abuse, and regulatory non-compliance, or indicative of control gaps, ineffective controls and corporate policy non-compliance.
Regularly trained internal audit departments and compliance professionals on the use of data analysis and continuous auditing and monitoring techniques.