AI · FinTech · EdTech · Business Analytics · Robotics · Research
4 flagship platforms. 17 AI courses. 30 applications. 20 FinTech tools. 18 robotics builds. 6 educational games. 24 research outputs. 22 commissioned research projects. 30+ supervised student research projects. Taught in 5 countries across 22+ years. All built end-to-end on local NVIDIA infrastructure — no cloud, no institutional funding.
The Work
AutoMatrixLab exists to close that gap one industry at a time. Each system here was built to answer a question somebody actually has — in finance, healthcare, education, marketing, retail, supply chain, manufacturing, real estate, robotics or research — and then built far enough to be watched working rather than described. All of it in the open, on local NVIDIA infrastructure, with no institutional funding behind it. The behavioural research is not separate from these products: it is what lets them adapt to the individual, reading how a particular person weighs risk, responds to reward and holds attention, and shaping the experience to the person using it rather than treating everyone the same.
Most organisations have access to AI now. Far fewer have changed how they work because of it. The gap is not access and it is not skill — it is that nobody has shown them what is actually possible. Everything here exists to close that gap: built end to end, running, and adaptable to a different industry.
Routing everything through a model is not automation — it is outsourcing the work to whoever owns the model. The repetitive work is automated inside the application, and the AI is called only for the part that genuinely needs it. That keeps the system fast, predictable, cheap to run, and mine.
By The Numbers
Industry Signals
“We no longer require a four-year degree for most of our technical roles. What matters is what you can do.”
— Google Career Certificates initiative
Tesla / xAI
“We don’t care where you went to school or whether you went to school. Show us your code.”
— Elon Musk, on Tesla hiring
IBM
“Half of our US jobs no longer require a degree. We are becoming a skills-first employer.”
— IBM Skills-First Hiring Policy
OpenAI
“The future of work will belong to those who can orchestrate AI — not compete with it.”
— Sam Altman, OpenAI
The Gap
Nobody has shown them
Access to AI is not the constraint. Most teams have the tools and no working example in their own domain to reason from, so the tools stay unused.
Capability bought, not used
Organisations buy capability and leave it idle. Everything here runs on one local NVIDIA machine, which keeps the cost predictable and the system mine.
Work that never ships
Analysis that stops at a document changes nothing. Every research project here ships as a working application, a demo, and an explanation someone outside the field can follow.
Proof of concept, never production
Most AI work stops at a notebook. These systems run in production — a point-of-sale platform in a live business, research dashboards, and AI tutors people use daily.
Lead the change. Don’t chase it.
My Platforms
Each flagship platform with its tour, then every platform feature demo underneath. Anything game-shaped lives in the Gamification tab. Every one of them is built on the behavioural research below, which is what lets a platform read how an individual weighs risk, responds to reward and holds attention, and shape itself to the person using it rather than treating everyone the same.
Students build a production machine-learning pipeline by dragging nodes on a touchscreen — no code at all — then run that same pipeline three further ways, down to raw Python. One pipeline, applied to 33 real industry case studies across 8 domains, each on a real dataset with competing models and a champion chosen on the metric that matters. No case study ends with just a report: each one ends with a live, deployable dashboard that scores every transaction, patient, customer, machine or property, every second, around the clock — 33 dashboards, one per case. It teaches the tools, the method, the subject matter, and workflow automation. Learn it, build it, deploy it. Built on the ML Analytics Platform v2.0 with the Codeless Pipeline Builder, at Massey University’s School of Mathematical and Computational Sciences.
The evidence standard behind these platforms — causal identification, natural experiments and hybrid models — is set out under Research.
My research automation platform: it takes a hypothesis to a draft paper on its own. Two pieces — an interactive econometrics dashboard (Data, Viz, DiD, Policy, ML and Causal ML: pick an outcome, a shock and a treatment group, press run), and a batch pipeline that runs all nine phases in about two and a half minutes, producing the tables, figures, an AI-reviewed abstract and its own demo video. The platform is domain-agnostic; financial hardship is simply the first study put through it, run on 21 years of HILDA panel data and written toward the Journal of Banking & Finance.
The hypothesis was that people whose first hardship is asking family for help recover faster than those who simply cannot pay a bill. They do not. Strategic help-seekers recovered at 56.9% against 62.4% for bills-only (z = −3.67, p = 0.0002) — the opposite direction. The platform contradicted its own author and reported it anyway.
dashboards/hardship_sequencing_dashboard_2026-05-11.html. Two further corrections to the brief: code/ holds 97 files, not ~50, and an 11.8 MB plain cut of the results video sits beside the 51.1 MB cinematic one. The 64 tables, 34 figures, 9 phases, 454,861 person-waves and 11,595 first-landing events all verified against the folder.A practical course that moves students from “this correlation exists” to “this is the causal effect, and here is why the identification holds”. Each topic pairs a lecture with a hands-on SAS practical, and the later topics build to full case studies. The distinctive part is the live in-browser SAS workspace: students run real SAS code against real data without installing anything.
_staging\deploy-bundle and will silently overwrite changes on the next deploy.Regression analysis, panel data, time series and limited dependent variable models, taught in SAS and Python. What makes it a platform rather than a set of slides is the live runner: it takes an ordinary SAS practical file and performs it, step by step, in a real SAS Studio session while the class watches — and can record that same run to video unattended.
A single command drives a whole SAS practical through SAS Studio section by section, at presenter pace. The class watches the analysis actually happen in the SAS Studio tab rather than watching a slide about it. It splits a practical three ways — inline section markers, banner comment blocks, or a fallback on procedure boundaries — so an existing practical file needs no rewriting.
Run the whole file, a single section or a range; set the pace; add an on-screen banner; keep or clear the log between sections; and record the entire run to MP4 — which is how the practical recordings in this course were produced.
--record flag. Runs locally only: it needs Chrome on a debugging port with an authenticated SAS Studio session, so no public URL is claimed.Real World Applications
Live demonstrations of the ML pipeline builder, fraud detection system, and interactive course features.
One application, recorded five ways. Build a machine-learning pipeline by dragging nodes — data ingest, cleaning, EDA, feature engineering, split & scale, competing models — then run it and read the results: live charts, confusion matrix, SHAP, ROC curve and generated reports. The demos below show it on desktop and tablet, and follow a fraud-detection case study end to end.
24 Projects
The AI tutors, multilingual learning platforms, research collaboration tools and the local NVIDIA infrastructure the platform runs on.
SAS analytics driven by AI — a desktop tool that turns a dataset into analysis and a written report in one click, and the end-to-end pipeline that orchestrates the whole SAS workflow behind it. Each demo below shows a different layer of the same automation.
A unified research collaboration platform — shown first as the collaboration tool and then as the full research operating system it grew into.
A custom AI infrastructure deployed on a personal NVIDIA 4090 GPU that outperforms OpenAI in both vision and speech tas…
Main Video
AutoMatrixLab Universe
Building an entire universe of educational systems — the platform architecture overview. A walkthrough of how AutoMatrixLab connects its AI apps, 17 courses, multi-agent AI assistants, and local NVIDIA infrastructure into one unified learning platform.
Every game I have built, in one place — each with its name, what it teaches and its demo. Each one adapts to the individual player, using the behavioural research to read how that person weighs risk, responds to reward and holds attention.
Fortnite UEFN
A family of builds inside Fortnite UEFN rather than a single title. Boss personas and the fox companion are LLM-powered and speak with live voice. A two-persona relay passes the boss’s spoken line into the companion, so one AI character answers another rather than both talking at the player. The dog is a silent pet. The guards use Fortnite’s native scripted dialogue, not AI.
Fortnite UEFN
A driving licence test built as a racing game in UEFN. Not a race — a test. The player drives the track while an AI examiner sits in the passenger seat.
A fox character named Rue rides in the passenger seat as the examiner. She calls the corner before the player reaches it, asks what the road signs mean as they come up, and gives a verdict afterwards on what the player did. Road signs are placed along the route, with four measured tests: two speed zones, braking before a bend, not braking on a hump, and keep-left discipline. A red fault flash when the player gets one wrong, a score band on screen, and a pass or fail at the end.
The audio design is the part worth explaining. There are roughly five seconds between checkpoints and a generated line takes eight to nine. So corner calls are pre-rendered clips that fire instantly, where timing matters. The AI persona handles verdicts and commentary afterwards, where a two-second pause costs nothing. Two audio paths, split by latency rather than by preference — and that finding generalises: in a real-time game, latency decides the design, not model quality.
Why a licence test: the content already exists, people genuinely study for it, and reading a road sign at speed tests whether someone learned it better than a multiple-choice question does. Knowledge is the mechanic rather than a quiz bolted onto the end.
Research as Sound & Story
Research findings transformed into AI-generated songs and emotional narratives.
The production platform that turns research datasets into narrated songs and short films at scale.
Research as Sound & Story
Each research paper turned into an AI-generated song and film. Play them here.
A real-world simulation of an entire life, learned by playing real games — my own educational takes on the titles people already love, each rebuilt with its own logic. It is a full life cycle, not just a financial one: a school where you study any course or degree, a career centre that places you in real professions, a bank for saving and borrowing, a stock market of real listed companies, a property market with an AI agent, businesses you can start and grow, one shared wallet and a passive economy running underneath. What makes it feel real is that the courses, jobs, businesses and markets all respond to the actual economy of your country and city on that day — macro data from the World Bank, rates from the BIS, jobs from the ILO and OECD, live market feeds, and property and climate from the OECD and Open-Meteo. Solo and multiplayer load that day’s real data; the monthly season replays the previous month one real day at a time, so you can measure your decisions against what actually happened. What a player learns is how a life actually works, from job markets and wages to mortgages and interest rates, the economics underneath it, and which LLM to trust for what. It is also an open platform for teaching and research: any academic can upload a course, deploy an experiment to a live population, state a hypothesis and get back the analysis, a report and a live dashboard — turning play into high-frequency behavioural data. Built natively in Unity; the front-end and the 3D city are live, the full economic layer is still in progress.
A virtual economy of the entire world, running on real data every single day. Spin the globe, step into a country, and what you see is that country's actual reality: its real exchange rate updated daily, its real inflation, unemployment and GDP from the World Bank — recessions and the COVID dip visible in the charts — and its real central bank rate, so saving in one country genuinely pays more than in another, because in the real world it does. Prices show in the local currency with the US dollar beside them at that day's rate: rupiah in Jakarta, rupees in Karachi, dollars in Sydney. Each region is an animated stylised map, so the same decision can be compared across different economies.
A AAA-rated educational game built in Unreal Engine 5 featuring triple-A quality graphics and gameplay.
Everything robot-related in one place — the robot lecturers, the autonomous AI companions, the onboard-vision builds, the robot finance coaches and the cloud robotics network. They teach any subject in any language, including te reo Māori. Every one of them runs three ways — entirely onboard on a local LLM with no internet, on a local LLM served across the network, or through a cloud provider on subscription — so the choice follows the constraint, whether that is privacy, connectivity or cost, and they are available around the clock. Whether a project is also a game or a course, if it involves a robot it lives here. Click any card for full details, tech stack and demo video.
18 Robotics Projects
A robot lecturer that delivers teaching sessions on its own, including always-on Zoom delivery. Each demo shows a different generation of the same system.
A companion robot operating autonomously rather than being driven — the demos below trace it from first build to full independence.
A humanoid robot running its vision, speech and reasoning engines entirely onboard — no cloud PC, no network dependency. The demos below show the same robot at different stages of that build.
A robot built to teach children financial skills through conversation. Each demo shows a different capability of the same coach.
Where the analysis leaves the screen entirely: a humanoid robot that reads a company’s financial statements through its own camera, reasons about what it sees, and narrates the verdict aloud.
FinTech
Every finance application I have built — equity research and valuation automation, financial planning and advice agents, trading and credit-risk tools, insurance and property ML, and business automation. Click any card for full details, tech stack and demo video.
20 Applications
One equity-research system, shown four ways. It takes a public company from raw SEC filings to a complete 20-page institutional-grade report — the analysis, the commentary and the layout — and runs end to end on a local NVIDIA 4090 rather than a cloud bill. Each demo below opens a different part of the same pipeline.
Turning financial statements and property data into models that project forward — automated forecasting out to 2035 from an income statement and balance sheet, and a valuation engine running thirteen machine-learning models side by side.
Credit risk scored in real time, financial communication opened up through speech and translation, and the monthly subscription bill of small-business software replaced by systems a business can simply own.
A complete point-of-sale and business operations platform, built alongside the owner and running daily in a live business, maintained remotely. The owner now runs and extends it himself; I help when he asks.
Built with a primary school student — a full NZ-curriculum AI tutor for Years 1–13 with a custom child-tuned LLM, local voice and translation servers, and a humanoid robot companion. Everything runs natively on her own NVIDIA machine with no internet dependency.
Applications
The apps themselves — the tutors and learning platforms, the local NVIDIA infrastructure they run on, the standalone products, and the demo recordings of the pipeline builder, fraud detection and the interactive course features.
One family of AI advice tools built for the New Zealand context — KiwiSaver, tax, insurance and household budgeting. Each demo below is a different agent or surface built on the same advice engine.
Built for the individual rather than the institution: a conversational planner that tracks income, expenses and savings goals, and a trade-suggestion engine that turns market data into reasoned positions.
A tutor that sees, listens and speaks in any language, self-funded and running on local NVIDIA hardware. The demos below show its different surfaces.
17 Courses
ML, AI & Automation Courses
Every course is built on the Education Engine — real-time animation, virtual labs, an AI tutor and an exam centre in one app. Click any course to open its full detail.
One machine-learning pipeline taught once, then applied domain by domain. Marketing works through lead scoring, campaign attribution and customer segmentation; healthcare through readmission risk and length of stay; finance through fraud, credit risk and portfolio optimisation. Each course carries its own end-to-end case studies on real datasets, with competing models and a champion chosen on the metric that matters for that problem.
Each subject is taught by animation rather than by slides. Every chapter runs three ways: a narrated lecture, an interactive walkthrough where the concepts actually animate in real time as D3 and 3D scenes, and a hands-on practical in a real-time virtual lab. On top of that sits an AI tutor — an agent I built and grounded myself, not a generic chatbot — that answers any question and links straight to the exact lesson, and an Exam Centre that predicts likely questions from real past and guess papers, then marks a full mock with feedback. Lectures, animations, labs, practice, entrance-test and practical preparation are all inside one app — a single stop instead of a shelf of tools.
A gaming engine that teaches children finance and technology. Children do not learn money from a lecture — they learn it by saving up for a birthday party, rounding up spare change, deciding what goes in the giving jar — so each of those is a small playable station rather than a worksheet. The menu is an illustrated control room: every object taps into a station, and each station holds several games plus scores, history and a leaderboard. It covers earning, spending, saving and budgeting; borrowing, credit and debt; risk, insurance and protection; investing and long-term planning — and the decisions people most often get wrong in each. Every station is a game rather than a worksheet.
6 MoneyWorld games → see them in Gamification
A student normally pays for five separate things to learn one subject: school lectures, coaching for the same lectures, lab access for practicals, exam preparation, and a separate academy for the entrance test. Five fees, five buildings, five teachers who never speak to each other — so a student can be ahead in coaching and behind in the lab, and learn a concept one way in class and the contradicting way at the test academy. I rebuilt all five into one platform sharing a single spine: the lecture, the interactive walkthrough, the virtual practical, the exam centre and the entrance-test centre are the same curriculum, the same content and the same AI tutor, so they cannot contradict each other, and it is open every day of the year rather than switching on near the deadline. The quiet part is the AI: one brain across every surface, which knows the lecture you just read, the experiment you just ran and the question you are stuck on, and sends you to the exact page that explains it. One branded shell over Computer Science, Mathematics, Chemistry, Physics and Biology, for ninth through twelfth grade.
Mathematics XI: binomial · matrices · permutations · quadratics · sequences · trigonometry. Mathematics XII: conics · derivatives · functions & limits · integration · straight line · vectors.
Junior and senior sciences: ninth grade chemistry (8 chapters), tenth grade chemistry (8 chapters), Physics (12 chapters), Chemistry (12 chapters) and Biology — plus interactive virtual, periodic and reaction labs and 3D practicals.
Gamified platforms that teach financial capability rather than lecture it — built for classrooms and for home, and deployable both locally and in the cloud.
A context-aware teaching platform that knows what the student is looking at, taking live teaching beyond a video call. Each demo shows a different build of it.
Platform Demos
Live demonstrations of the AutoMatrixLab education platform — courses, AI tutoring, admin tools, and infrastructure.
An AI tutor that reads, hears, types and speaks in every major language — reading what is on screen and explaining it aloud, driven through the robot’s vision and voice.
Industry & Government Research
Research and analytics projects commissioned by or conducted in collaboration with NZ government agencies, financial institutions, and industry organisations. Completed through Massey University Fin-Ed Centre, earning significant revenue for the school and Centre.
Commissioned by Ministry of Health NZ. Analysed FinCap Client Voices data to identify gambling-harm trends and guide prevention policies.
Compared crisis-response effectiveness between government and community support in NZ and Australia. Provided policy recommendations.
Used proprietary Retirement Commission data to identify financial hardship factors and recommend targeted interventions for single parents.
Investigated teleworking impact on wellbeing and income stability of NZ labour force using Retirement Commission datasets.
Explored engagement patterns among frequent and infrequent Sorted website visitors to enhance NZ financial-literacy initiatives.
Cultural Perception and Financial Wellbeing surveys (preliminary analysis through Massey Westpac Fin-Ed Centre).
Used the ANZ Bank Financial Wellbeing Survey (NZ) dataset, accessed through the Massey Fin-Ed Centre research consortium, to map financial-wellbeing patterns and guide capability frameworks for households across AU and NZ. ANZ Bank is the data source; the Fin-Ed Centre is the commissioning party.
Used the ANZ Bank Financial Wellbeing Survey (NZ) dataset, accessed through the Massey Fin-Ed Centre research consortium, linked with FinCap caseload data, to explore correlations between mental health, financial hardship, and emergency readiness.
Analysed FinCap client data to assess debt distribution and home-loan affordability trends. Informed responsible-lending strategies.
Sponsored by the SkyCity Auckland Community Trust and delivered through the Massey Fin-Ed Centre. Analysed MWDI loan client data to evaluate Māori wahine and whānau business finance needs and inform policy and support programmes for Māori entrepreneurs.
Massey Fin-Ed Centre + Te Au Rangahau. Financial capability of Māori entrepreneurs and enterprise viability across Auckland and Northland.
Applied advanced segmentation models to NZAA customer data. Identified strategies to improve loyalty and retention.
Analysed proprietary traffic data to evaluate user engagement and improve marketing and public-awareness strategies.
Analysed proprietary Massey data across 20+ admission criteria investigating factors influencing student completion and retention.
Used Eftpos data to generate customer-segmentation insights supporting targeted outreach.
Published Insights Bites article
Multi-paper portfolio (cultural pressures, financial education in schools, KiwiSaver gender + ethnic women, mental health and retirement preparedness, etc.)
PhD; published policy briefs feeding NZ Treasury / RBNZ / Productivity Commission
Recognition
SAS Institute
Received with a sponsored trip to SAS Innovate, Grapevine, Texas, USA
I designed and taught the first SAS Viya course in Asia-Pacific — introducing it at Massey University from its earliest development stage. I helped shape SAS Viya V2 as a pilot participant providing direct technical feedback to the SAS USA team.
Durham University Business School
Summer Congregation. PhD in Accounting & Finance. Distinction in all coursework.
Commonwealth Scholarship Commission, UK
Full doctoral scholarship for studies at Durham University. One of the most competitive scholarships in the world.
Durham University
Awarded twice for exceptional contribution to university community life.
Durham University
Durham University
Massey University
Consistently excellent course evaluations across all three campuses (Auckland, Wellington, PSB Singapore).
Institute of Business Management
CGPA 3.68/4.0. Second in graduating class.
NED University of Engineering & Technology
CGPA 3.55/4.0. Industrial Electronics.
Advance HE, UK
Credential ID: PR055577
Durham University
Durham University
Elected president. Previously served as International Students Officer.
Why this exists
Most organisations now have access to AI. Very few have changed how they work because of it. The bottleneck is not access and it is not money — it is that nobody has shown them what is actually possible in their own domain. It is a problem of confidence and capability, not tools.
I have watched this from both sides for years: inside universities, where the capability arrives long before anyone changes what they do with it, and inside client organisations, where the same tools sit unused for want of a working example.
So everything here is a demonstration rather than a proposal. Each platform, each application, each demo exists to show what can be done now, in a specific industry, built end to end and running. Not slides, not pilots that never ship — systems you can watch working. Some are concept demonstrations rather than polished products, and I say so where that is the case.
What I am trying to prove is narrow and testable: that a single person with the right method can build, in one domain after another, the thing an organisation was told would take a team and a year.
Engineer. Researcher. Educator. Builder. Founder.
A career spanning four disciplines that rarely meet in one person: Electronics Engineering, Financial Economics, Management, and Artificial Intelligence. Taught at Durham University (UK), Aberystwyth University (UK) and its Mauritius campus, NED University of Engineering & Technology (Pakistan), Massey University (New Zealand) and its Singapore campus at PSB Academy — five countries across Europe, Africa, Asia, and the Pacific.
Started as an Instrument & Control Engineer automating industrial giants across petroleum, telecoms, hospitals, manufacturing, and logistics — building safety-critical control systems for British Petroleum (BP), OMV, NRL, OGDCL, Pakistan Steel Mills, and Pak Suzuki. As an engineering undergraduate in 2002, built an interactive cable TV voting system via SMS that spread across South Asia — years before streaming existed.
After a PhD at Durham University (UK), led corporate finance teams at Deloitte, delivered projects for The World Bank and international investors, and collaborated with Silicon Valley AI startups. In New Zealand, advised the Ministry of Social Development, Financial Markets Authority, Retirement Commission, FinCap, IRD, major NZ banks, insurance providers, Ministry of Health, and District Health Boards.
Developed the first-ever university course using SAS Viya at a time when both academia and industry were barely aware of the platform. Shaped its V2 as a pilot participant, built SAS automation applications, and taught the entire SAS software ecosystem. In 2026, SAS Institute named me a Global SAS Educator Award winner — featured in the official press release, invited to SAS Innovate 2026 in Texas to receive the award among Fortune 100 executives and top tech leaders, and attended the inaugural SAS Educate APAC 2026.
Today building AutoMatrixLab — AI-powered applications across ten fields, multilingual courses, and gamified learning. At its core: “Educational Jarvis” — an always-on, screen-aware AI companion on local NVIDIA RTX 4090 infrastructure, orchestrating intelligent agents across every tool you use.
Career
Massey University, Auckland & Singapore (PSB Academy) | Jan 2018 – Present (7+ years)
Developed ‘Big Data in Finance and Banking’ and ‘Applied Econometrics Methods’ courses for Master of Analytics. Pioneered the first-ever university course using SAS Viya at a time when both academia and industry were barely aware of the platform. Shaped its V2 as a pilot participant providing direct technical feedback to the SAS USA team. Built SAS automation applications and taught across the entire SAS software ecosystem. Built AI applications across ten fields on local NVIDIA RTX 4090 infrastructure. Supervised PhD and Master’s students in advanced analytics and household finance. See Student Projects for details. Also taught at Massey’s Singapore campus at PSB Academy. Recipient of the 2026 SAS Global Educator Award.
New Zealand | Jan 2019 – Present (6+ years)
Assisted Ministry of Social Development, Financial Markets Authority, Retirement Commission, FinCap, IRD, banks and insurance providers with data analytics consulting, policy research, and advanced econometric modelling.
Aberystwyth University, Wales, UK & Mauritius Campus | Jan 2016 – Jan 2018 (2 years)
Program Leader for Accounting and Finance. Module Leader for Investments and Financial Instruments, Corporate Finance, Financial Management, Auditing, and Financial Accounting. Also taught at Aberystwyth’s transnational campus in Mauritius.
Silicon Valley & Global | Jan 2015 – Dec 2017 (3 years)
Assisted several organisations including Silicon Valley based startups with local and overseas artificial intelligence and analytics projects.
Deloitte, Pakistan | Jan 2016 – Dec 2016
Led corporate finance teams in consulting and advisory projects for international clients including The World Bank and high-profile international investors. Provided training and mentorship to consulting teams.
Durham University Business School, UK | Jan 2011 – Mar 2013
Taught seminars in Advanced Microeconomics and Economic Principles modules. Assisted postgraduate students in dissertation work.
Institute of Industrial Electronics Engineering (IIEE), NED University, Pakistan | 2006 – 2009
Taught Engineering Economics and Mathematics modules at the same institution where I graduated as an Industrial Electronics Engineer. Conducted workshops in computer programming and automation.
National Engineering & Scientific Commission of Pakistan | Jan 2006 – Dec 2010 (5 years)
Led ISO 9001, 14001 and OHSAS compliance and EMI/EMC Engineering across major industrial clients in petroleum, telecoms, hospitals, manufacturing, and logistics. Delivered automation and instrumentation projects for OGDCL, Pakistan Steel Mills, Pak Suzuki, and national telecom and healthcare infrastructure. Supervised quality management systems and electromagnetic compatibility testing across diverse industrial environments.
ENAR Petrotech Services Pvt. Ltd. | Jan 2005 – Dec 2005
Designed, implemented and supervised instrumentation and control systems for petroleum giants including British Petroleum (BP), OMV, and NRL. Automated industrial processes across petrochemical, manufacturing, and energy sectors — building safety-critical systems where failure means explosions, chemical leaks, or production losses costing millions per hour.
Industry Impact
First university in Asia Pacific to teach with SAS Viya — deploying it at a time when both academia and industry were barely aware of the platform. Shaped SAS Viya V2 as a handpicked pilot participant, providing direct technical feedback to SAS USA. Built SAS automation applications using cloud and local license installations. Taught the entire SAS software ecosystem: Base SAS, SAS/STAT, SAS/ETS, Enterprise Miner, Enterprise Guide, SAS/GRAPH, SAS/OR, SAS Studio, SAS Viya, JMP, and more. Recipient of the 2026 SAS Global Educator Award. The award included a sponsored trip to SAS Innovate 2026 in Texas, and a place at the inaugural SAS Educate APAC 2026.
Assisted Ministry of Social Development, Financial Markets Authority, Retirement Commission, FinCap, IRD, Banks, Insurance providers, Ministry of Health, DHBs and manufacturing industries with analytics and subject matter advice.
Assisted several organisations including Silicon Valley based startups with local and overseas artificial intelligence and analytics projects through global joint venture collaborations.
Completed several commissioned and noncommissioned analytics and research projects for Massey University Fin-Ed Centre, earning significant revenue for the school and the Centre.
Qualifications
Durham University Business School, UK | 2011–2015
Best Thesis Award • Distinction in all coursework
Commonwealth Scholarship (full fee and maintenance grant). Research focus on household finance, financial literacy, and stock market participation. Published in FT50 journals.
Institute of Business Management, Pakistan | 2006–2008
Second Position — CGPA 3.68/4.0
Merit scholarship recipient. Foundation for transition from engineering to financial research and academia.
NED University of Engineering & Technology, Pakistan | 2001–2004
Fourth Position — CGPA 3.55/4.0
Merit scholarship (2001–2004). The engineering mindset that drives the technical architecture behind every AI project.
Academic Output
My research portfolio — published work and research in progress — is organised under the Research tab with filter chips for tier, research line, and submission status.
Method
The work below rests on four positions about evidence. They are what separate a defensible finding from a well-fitted one.
Causal inference, not prediction
Regression, machine learning and data mining are all correlation without an identification strategy, and adding controls does not change that. A causal claim needs a design, an identification strategy, and tests built to break the result rather than confirm it.
Natural experiments
Most organisations assume a causal question needs a controlled trial, and give up when they cannot afford one. Usually they do not need it. Policy changes, rate moves, regulatory shifts and other external shocks create natural experiments already sitting in the data — treatment and comparison groups formed by something that happened anyway. Finding them and analysing them properly is cheaper than any trial and often closer to the real world. I have run these for clients.
Experimental and behavioural economics
Not only a research interest — it is how the platforms are designed. The finite intelligence budget in AXIOM, the real-data economy in Miniconomy and load-bearing knowledge in MoneyWorld are experimental designs, not just mechanics.
Hybrid models
Survival analysis combined with neural approaches, and similar pairings: the rigour of econometrics with the flexibility of machine learning, for accuracy without losing the ability to defend a result.
My research is in behavioural economics and behavioural finance — how people actually make financial decisions, and what those decisions do to them. It runs across human decision making, financial capability, financial wellbeing, emotional wellbeing and overall wellbeing. None of it was ever research for its own sake. It is how I came to understand people, organisations and systems well enough to build for them, and it is the foundation every platform on this site is built on.
Psychological Distress and Household Net Worth
Households facing persistent psychological distress accumulate less wealth across the life cycle, and the gap survives standard income, employment, and demographic controls. We ask whether mental-health stress operates as a measurable behavioural-economic friction on household balance sheets, distinct from income shocks and education channels.
Online IDs: DOI · RG 362161202
Financial Literacy, Trust, and Stock Market Participation
Stock-market participation rates differ sharply across households with comparable income and wealth, and the residual gap maps onto differences in financial literacy and trust. We ask whether financial literacy and trust in financial institutions operate as behavioural-economic prerequisites for market participation, distinct from access and cost channels.
Online IDs: DOI · RG 264786037
PhD Thesis — Durham University Business School (2015)
Doctoral thesis comprising empirical essays in household finance. Applies modern panel-econometric techniques to large household panel datasets to investigate financial decision-making, wealth accumulation, stock market participation, trust, and the behavioural determinants of household financial outcomes.
Funder: Durham University Business School (doctoral funding) · etheses.dur.ac.uk (search "Adnan Balloch")
8 academic conferences, seminars and policy presentations.
Melbourne, Australia
Dynamics of Household Financial Hardship: Landing, Path, Duration and Exit. Pho, L.T.; Qiu, M.; Balloch, A. Presented in the behavioural finance stream.
Melbourne Institute of Applied Economic and Social Research, University of Melbourne
Dynamics of Household Financial Hardship: Landing, Path, Duration and Exit. Pho, L.T.; Qiu, M.; Balloch, A. Presented at the conference run by the Melbourne Institute, which produces the HILDA Survey the paper is built on.
Massey University, New Zealand
Miscellaneous seminars, workshops and guest presentations across schools and programmes.
Te Ara Ahunga Ora Retirement Commission — internal seminar presentation
New York, USA
Balloch, A.; Engels, C.; Philip, D.
Auckland, New Zealand
Manchester, UK
Balloch, A.; Philip, D.; Nicolae, A.
Durham, UK
Twenty-one working papers and supervised projects across the four research lines, with status badges marking publication-readiness — including four new HILDA 2026 papers in active preparation for Tier-A journal submission. Findings reported on each detail page are from completed analysis runs; manuscripts and external links (ResearchGate, journal DOI) will be added on first public release.
Economic Shock, Hardship Landing, and Intra-Household Financial Anxiety Contagion
Telework and Sole-Parent Financial Wellbeing
Self-Efficacy, Wealth Formation, and Household Resilience
Locus of Control and the Persistence of Financial Hardship
Telework, Lockdowns, and Anxiety Across 151 Countries
Family Financial Socialization and the Making of Capability
Financial Parenting, Capability, and Adult Wellbeing
Empirical Evidence from New Zealand's Labour Force during COVID-19
Learning Disability, Financial Hardship, and Recovery Dynamics
Work-Family Attitudes, Sole Parenthood, and Hardship
Financial Decision-Making, Relationship Strain, and Separation Risk
Women's Financial Capability and Recovery After Life Events
Gender, Financial Risk-Taking, and Hardship Recovery
Third-Tier Lending, Housing Stress, and Financial Vulnerability
Social Engagement, Welfare Regimes, and Financial Vulnerability
Disability, Debt Management, and Retirement Preparedness
Why Model Choice Matters in Targeting Australian Financial Hardship
Latent Classes in HILDA's 7-item Module C2 and What They Predict for Subsequent Recovery
How Where You Move From Shapes What Happens to You — A Multi-Shock Analysis from HILDA
A Competing-Risks Mediation Analysis from HILDA
Panel Data Methods for Social and Economic Policy
Supervised Research Portfolio
Supervised across Master of Analytics, Master of Finance, PhD, and industry-internship programmes. Each is an industry-partnered research engagement — a host organisation, a real dataset, and a question that organisation actually needs answered — run end to end from scoping through analysis to a delivered report.
