Executive certificate, two levels
Behavioural Finance Bias Awareness Programme
Master advanced bias detection and robust decision frameworks with Australia’s only
executive behavioural finance bias awareness programme—100% online, designed for
mid-career professionals.
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Advance your career in the field with this qualification
700+
alumni and current delegates
12+
countries represented
7+
years of evidence-driven impact
94%
graduates with positive career impact
Master the skills that employers want right now
Skills Gap Institute research
69% of employers report a skills gap in finance, and 72% say this gap has widened in the
past two years. 81% struggle to recruit bias-literate professionals—creating urgent
demand for advanced, evidence-based skills.
Demand for bias-aware financial decision-making is growing rapidly. Those with the skills to challenge cognitive error are primed for opportunity.
Behavioural Finance Bias Awareness Programme Cognitive errors sabotage even the most sophisticated financial plans. The Behavioural Finance Bias Awareness Programme delivers advanced bias detection, scenario-based diagnostics, and robust decision frameworks—fully online, part-time, over six months—arming professionals with tools to challenge bias and deliver stronger outcomes at every career stage.
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Key information
Next programme start:
July 2026, January 2027, April 2027
Awarding body:
Independent Finance Institute
2017 by finance and psychology leaders
Bias detection, scenario analysis, decision frameworks
Delivery and duration:
Online, part-time, six months
Cognitive bias, analytics, and applied psychology experts
One-to-one, peer review, live sessions
Community size:
Global alumni in 12+ countries
Alumni benefits:
Lifelong library, career support, events
The programme journey
The journey is structured in three phases: preparation, qualification, and lifelong
learning, designed for professionals who want continuous career edge.
Download the brochure
for a detailed breakdown.
Preparation
Preparation starts immediately after enrolment, equipping participants with the
cognitive bias basics and self-assessment tools needed for module success.
- Online primer on bias fundamentals
- Skills self-assessment and peer baseline
- Introductory Q&A with faculty
Qualification
Qualification phase spans six months, focusing on advanced bias detection and applied decision-making. Assessment is hands-on, with modules tailored to live market challenges.
- Scenario-based modules
- Live faculty and peer review
- Open-book, applied assessments
- Final project on real industry case
Lifelong learning
After graduation, lifelong learning continues. Alumni receive updates on new bias research, join global events, and maintain a competitive edge with continuous skills refresh.
- Alumni library access
- Quarterly research and updates
- Global peer and career network
Faculty
The teaching team is led by the programme faculty —not just academics, but leading practitioners from finance, analytics, and psychology. They teach practical frameworks, challenge bias, and drive evidence-based change in every session.
Dr. Amelia Grant
Programme Course Director
Dr. Amelia Grant is recognised for her pioneering research on bias in financial decision-making, with publications in leading international journals. Her best-known work dissects how subtle framing and heuristics shape outcomes in high-stakes environments, making her a sought-after expert for industry and academia alike.
Dr. Marcus Wilton
Faculty Member, Decision Science
Dr. Marcus Wilton is known for his research on groupthink, collaborative decision-making, and structured bias correction in financial and policy settings. His published work advances tools for identifying and counteracting consensus-driven errors.
Dr. Priya Narayanan
Senior Lecturer, Applied Psychology
Dr. Priya Narayanan is Senior Lecturer in Applied Psychology, recognised for her research on loss aversion and emotional bias in financial decision-making. Her work informs both academic debate and the design of bias-aware interventions in industry.
Dr. Sofia Marques
Lecturer, Applied Statistics
Dr. Sofia Marques is Lecturer in Applied Statistics, renowned for research on quantitative bias analysis and its impact on financial decisions. Her peer-reviewed work shapes the programme’s analytics and evidence-based methodology.
Dr. James Okoro
Module Leader, Risk Perception
Dr. James Okoro is Module Leader in Risk Perception, known for research on cognitive error diagnosis in financial contexts. His expertise spans consulting and teaching, with a focus on real-world application of behavioural insights.
Professor Daniel Osei
Lead Quantitative Analyst
Professor Daniel Osei is known for pioneering research on cognitive error detection in financial analytics, with a focus on quantitative bias analysis. He has published extensively in peer-reviewed journals and leads several multi-institutional collaborations on applied finance.
Dr. Felix Li
Module Leader, Decision Theory
Dr. Felix Li is Module Leader in Decision Theory, known for research on analytical bias detection and the use of quantitative techniques in behavioural finance. His contributions shape the programme’s methodology and real-world application across sectors.
Professor Oliver Rasmussen
Senior Lecturer, Data Visualisation
Professor Oliver Rasmussen is Senior Lecturer in Data Visualisation, specialising in research on visual bias and interpretation of financial data. His applied work bridges academia and industry, advancing best practices for evidence-based decision-making.
Professor Linda Barrett
Head of Careers
Professor Linda Barrett serves as Head of Careers, known for her research on bias-resistant career pathways in finance and analytics. Her leadership supports participants and alumni as they build robust, data-driven professional trajectories.
Dr. Hannah Goldstein
Senior Lecturer, Peer Learning
Dr. Hannah Goldstein is Senior Lecturer in Peer Learning, specialising in the dynamics of group bias and collaborative correction in finance. Her research and teaching bridge evidence-based insights and practical groupwork strategies.
Dr. Nina Petrovic
Senior Research Fellow
Dr. Nina Petrovic is a Senior Research Fellow specialising in the study of framing and anchoring effects within financial decision-making. With a prolific output of peer-reviewed articles, she is regarded as a leading voice in bias mitigation and applied behavioural research.
Dr. Elena Tsang
Module Leader, Market Psychology
Dr. Elena Tsang serves as Module Leader in Market Psychology, specialising in crowd behaviour and the use of analytics to detect group bias. Her peer-reviewed work shapes both the academic content and interactive learning style of the programme.
Study support
Outcomes improve only when bias is challenged at every stage—so this programme provides comprehensive study support.
- Access one-to-one support from faculty experts
- Join regular peer review and discussion groups
- Use evidence-based resources updated quarterly
- Attend live Q&A and scenario debrief sessions
- Receive structured feedback on all assessments
- Engage with a global bias-aware alumni network
Assessment
Every module uses
scenario-based applied learning
Theory alone fails—evidence shows applied learning ensures graduates finish job-ready,
able to diagnose and correct bias where it matters: in real decisions.
All modules conclude with
open-book exams
Exams are open-book and scenario-driven, not rote. The final practical project tackles a
real industry case; alumni regularly present their projects in job interviews to prove
bias detection and decision-making skills.
Joining the programme
Entry requirements
Applicants must be at least 21 years old and show experience in a finance or
analytics-related role.
A prior degree is recommended but not essential; strong analytical skills are prioritised in selection.
All participants must demonstrate English proficiency suitable for advanced professional study.
How to enrol on the programme
- Submit your application online with all supporting documents through the secure portal. application
- The admissions team reviews your application and provides a decision within 48 hours.
- If accepted, pay your fees within seven days to secure your place in the next cohort. fees
- After enrolment, access preparation modules and join the peer community immediately.
Alumni benefits
Alumni maintain exclusive advantages: access to new bias research, industry lectures,
and a global peer network that supports continuous growth—keeping them ahead for a
decade or more.
Lifelong learning benefit
Graduates receive ongoing access to the Lifelong Learning Library Access to new lectures, industry research, and regular curriculum updates—ensuring alumni skills and knowledge keep pace with global developments for years after graduation.
Institute membership
All alumni become members of the Behavioural Finance Institute Membership includes alumni events, professional networking, and a dedicated careers service—supporting alumni with connections, insight, and practical career guidance well beyond graduation.
How will the programme help you go further in your career
Relying on habit in finance lets bias win. This programme is designed to disrupt that
cycle—no shortcuts, just evidence-based challenge. That’s why
thousands of professionals
who have advanced their decision-making and career prospects through the programme.
Sophie Chan
Portfolio Manager, Investment Firm
“The scenario-based exams completely changed how I evaluate decisions. Now, bias is flagged and challenged—not brushed aside.”
Before joining the programme, Sophie managed portfolios using standard models. She
quickly realised her process overlooked subtle cognitive errors. Through
scenario-driven assessment, she learned to identify, challenge, and correct bias,
which she now demonstrates in high-stakes presentations. She currently leads a risk
team in a major investment firm.
Liam Patel
Senior Compliance Officer, Financial Services
“Peer review sessions exposed errors I never knew I made. Now, my team debates every decision with data.”
Liam entered the programme as a compliance specialist seeking more rigour in
reporting. Faculty feedback and peer review revealed consistent framing errors in
his submissions. Post-graduation, he implemented bias-checking routines and now
mentors junior analysts on objective risk assessment.
Emily Stewart
Banking Advisor, Regional Bank
“Having global alumni to consult with means my learning never stops. New research is always at my fingertips.”
Emily enrolled early in her career, looking for a competitive edge. Networking with
alumni and faculty allowed her to stay current on market psychology. She now shares
insights on bias with her branch team, improving customer interactions and loan
evaluations.
Mark Jensen
Data Analyst, Technology Company
“Open-book exams tested real skills, not just memory. The final project gave me something concrete to show in interviews.”
Mark’s analytics background meant he knew numbers, but struggled with decision
process. The programme’s hands-on assessments helped him demonstrate bias diagnosis
in interviews, leading to a new analytics role in the tech sector.
Priya Sharma
Consultant, Advisory Group
“The focus on real-world cases and structured feedback keeps my skills relevant. It’s not just theory—it’s practice, every week.”
Priya consulted for public and private clients but sought a way to integrate
behavioural research into her work. The programme’s evidence-based structure
provided tools for ongoing professional growth and peer engagement, which she now
leverages in cross-sector projects.
Lucas Tan
Wealth Manager, Private Advisory
“Alumni events led directly to new opportunities. The network is real and always active.”
Lucas joined with experience in private wealth. Connecting with alumni at global
events opened new job opportunities. He now co-hosts online roundtables and
contributes to ongoing research for the alumni library.
Discover how the programme can transform your career
Download the brochure to review programme details, see a full module breakdown, and
understand how the qualification supports your professional journey. All fields are
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