- Quantitative Risk Modelling using Heteroscadastic eGARCH models
- Random forest proximities for visualisation
- Text Data Mining and k-proximity associative maps
- Robust Estimation (using C++ modelling)
- Secure .Net coding
- “R”
- SPSS and SAS
- Six Sigma
- QR Algorithms for Real Hessenburg Matrices
- Forward Time Centered Space (FTCS) differencing schemes and von Neumann Stability Analysis
- Survival, Hazard and DREAD modelling
I have successfully completed the following engagements:
- Quantitative risk assessments, (Based on HIPAA, BASEL II and various sections of the financial services legislation),
- Developed AS/NZS 4360 audit and review frameworks for CUSCAL (Credit Union Services Corp Aust. Ltd).
- Completed several cross departmental risk based assessments within the Australian Stock Exchange.
- Has produced academically published papers on IT, Mathematics, HR and Business Strategy
- Created Models using Spectral Analysis for the forensic verification of audio data
- The development of Continuous Audit and Fraud Detection Techniques
Craig has completed the following engagements:
- Fraud risk analysis (A risk modelling exercise for a retain chain recently)
- Forensic and Risk modelling
- Models derived with Local Martingales which are not Martingales
- The use of the Stieltjes Integral
- Lebesgue-Stieltjes Integral
- Semimartingales
- Approximation via Riemann Sums
- Exponential Martingales
- Levy Characterisation of Brownian Motion
- Gaussian Martingales
- Girsanov's Theorem
- The Brownian Martingale Representation Theorem
- The Dirichlet Problem
- The Cauchy Problem
- The Feynman-Kac Representation
- Signal Processing
- Unnormalised Conditional Distributions
- Zakai Equation
- Kushner-Stratonowich Equation
- Gronwall's Inequality
- Kalman Filters
Some projects with these have incorporated the following:
- Accounting system fraud modelling
- Market Risk Analysis
- Climate Modelling
- Spectral analysis of digital recorders for a forensic assignment
- Modelling the cross tabulation of stock movement (this is generally fitted to a logistic random effects model). This is collected with a combination of MAR, MNAR and MCAR missing data techniques.
- Whitley’s embedding theorem,
- Taken’s Delay embeddeding theorem, and
- Filtered Delay embeddings
Some of my recent client engagements include
- Static Code analysis for Centrebet
- Business analysis using DATs (Digital Analysis Technology) for a Marine Sales Company in NSW
- BCP reviews for a number of Credit Unions
- Data Conversion testing for a number of Credit Unions
- IT Security and Risk reviews for several Credit Unions
- SOX IT review and audit for GTN
