Markets & fintech
Treasury, FX, fixed income, liquidity and the data behind financial decisions
Finance · Consulting · Technology
Ali Akber, CFA
I connect business decisions with the systems that deliver them
From FX and fixed-income trading to banking transformation and AI solution architecture, I bring the operating context into technology decisions
Toronto, CanadaWhat I bring
Financial depth, practical delivery
Consulting & transformation
Business requirements, stakeholder alignment and delivery across banking teams
Technology architecture
Data flows, integrations, permissions and operating requirements translated into a system design
Applied AI
Research tools, agent workflows and prototypes with defined acceptance checks
Professional experience
From trading floors to AI systems
- November 2026 – presentToronto, Canada
EQ Bank
AI Solution Architect
- AI solution architecture in banking
- May 2026 – presentToronto, Canada
Citadel Agentic Labs
Founder
- Design AI products and research tools around practical business tasks
- Turn domain knowledge into workflows, prototypes, acceptance checks and teaching
- November 2021 – May 2026Toronto, Canada
KPMG Canada
Manager, Banking & Capital Markets
Joined as Senior Consultant; promoted to Manager in April 2024
- Designed credit and liquidity data, reporting and integration requirements
- Translated KYC and AML processes into developer-ready client-lifecycle specifications
- Led delivery teams across trading, risk, data and banking transformation
- May 2021 – October 2021Toronto, Canada
IMCO
Manager, Total Portfolio Support / Investment Finance
- Connected FX hedging, liquidity monitoring and investment-finance reporting
- Worked with portfolio and treasury teams on exposure, cash forecasting and counterparty assessment
- November 2016 – December 2020Dubai, UAE
Habib Bank Limited
FX & Fixed Income Sales Trader
- Priced and executed FX, rates and fixed-income transactions
- Managed corporate relationships and supported funding, hedging and asset-liability decisions
- Served as regional ALCO secretary
- October 2014 – November 2016Karachi, Pakistan
Habib Bank Limited
Sales Dealer, Corporate Desk
- Managed corporate banking relationships and dealt FX, rates and fixed-income products
Education & credentials
Finance meets technology
CFA charterholder
CFA Institute
- CFA Access Scholarship recipient · 2014
- CFA Institute Research Challenge · Local semi-finalist, 2014
Master of Financial Innovation & Technology
Smith School of Business, Queen’s University · 2021–2022
Programme focus: financial models, data science and machine learning, with team-based leadership.
About the MFIT programme (opens in a new tab)
BBA · Investment Finance
Institute of Business Administration, Karachi · 2010–2014
How I design
My AI systems design principles
First-principles thinking, business outcomes and a clear standard for what is ready to use
Start from first principles
Understand the decision, the people, the data and the constraints before choosing a model or a tool. Use the simplest approach that solves the real problem
Deliver a business outcome
Agree what better looks like: time saved, fewer errors, stronger decisions or a better customer experience. Measure adoption and operating cost alongside technical performance
Related reading : The KPIs that actually matter for production AI agents (opens in a new tab)Test the work, not just the answer
Use representative tasks and failure cases to check the whole workflow. Inspect the evidence and actions behind an answer before expanding its use
Related reading : Demystifying evals for AI agents (opens in a new tab)Keep people in control
Define what the system may read, change and send. Put human approval where decisions carry consequences, and make escalation a clear part of the workflow
Build for change
Keep business rules, data connections and model choices separable. An upgrade should improve the work without forcing a rebuild of the whole system
Related reading : The Microsoft Copilot harness (opens in a new tab)Design beyond the demo
Plan for retries, service failures, monitoring and recovery. Judge readiness by how the system behaves in everyday work, including when something goes wrong
Related reading : Scaling Managed Agents with stable interfaces (opens in a new tab)Reading behind the approach
These are my working principles, informed by current research and engineering practice
- Google Cloud: The KPIs that actually matter for production AI agents (opens in a new tab)26 February 2026
- Anthropic: Demystifying evals for AI agents (opens in a new tab)9 January 2026
- Microsoft: The Microsoft Copilot harness (opens in a new tab)28 October 2026
- Anthropic: Scaling Managed Agents with stable interfaces (opens in a new tab)8 April 2026
Responsible AI
Clear scope, accountable decisions
Scope
Write down the task, its limits and the decision it supports before using a model.
Permissions
Give people and systems only the access needed for the approved task.
Evidence
Organize the facts, relationships, dates and sources before asking AI to use them. Give the model the tools and checks it needs, and keep enough context to review its work.
Release-and-monitor
A named person approves release, checks the result, and responds when evidence changes.
Inside my lab
Learn, explore and read
Learn AI
Work through a task with me, bring your team, or join a partner programme
Find your starting pointExplore Projects
Hotel conversations, business research, websites, and skills for your AI agent
See what I’m buildingRead Research
Explore treasury workflows, banking applications, and evidence from 36 cities
Follow the researchWorking together
Partners & product alliances
Intwish
Gamification, immersive learning, and digital experiences. CaL contributes systems integration for role-specific assessments and employer review
Visit Intwish (opens in a new tab)MagneFo
An executive decision platform that brings business data into a shared view of revenue, cost, and performance
Visit MagneFo (opens in a new tab)