Algorithmic Trading and AI Governance in Nigeria: Validation Guide

An algorithmic-trading or AI procurement enquiry should define the permitted activity, model purpose and human accountability before discussing automation. Forecasting, execution support, quotation tools and risk analytics have different dependencies. Wigmore Trading accepts preparatory research and supplier-evaluation enquiries. This guide does not supply a live trading bot, recommend a strategy or establish permission to operate an automated trading service.

Specify the use case and legal operating boundary

Describe the intended users, instruments, venues, inputs, outputs and decisions the system may influence. Ask qualified advisers and the venue or authorised broker to confirm permissions and applicable controls. Marketing labels such as AI trading, smart routing or automated liquidity provision do not answer those questions. Keep research, procurement testing and live production clearly separated.

Why a good backtest is insufficient

Historical results depend on data selection, cost assumptions, model choices and the treatment of unavailable information. A procurement review should ask how overfitting, data leakage and unrealistic execution assumptions were challenged. Include varied conditions and document exclusions. Research results do not guarantee returns or establish suitability for a particular institution. Independent reviewers should have enough information to reproduce the evaluation.

Validate models for their actual purpose

A pricing model, liquidity forecast and execution tool should be assessed against the decision each supports. Define acceptance criteria, material limitations and escalation when inputs are missing or outside expected conditions. Model validation should consider operational behaviour and governance as well as statistical performance. Avoid substituting an unexplained vendor accuracy figure for documented testing and responsible approval.

Responsible AI is an ongoing process

The NIST AI Risk Management Framework offers a voluntary resource for organising AI risk work. It is not a Nigerian trading licence or certification. Use an appropriate framework to structure accountability, testing and monitoring while qualified specialists assess local requirements. Record the intended use and prohibit unapproved expansion into different decisions or customer populations.

Require controlled intervention and change management

The institution should know who can authorise a change, suspend a system and approve restart after an incident. Test these workflows in a controlled environment. Keep model versions, configuration, approvals and event records linked. Review vendor updates before accepting changes that could alter risk or behaviour. Outsourcing development does not remove the owner’s responsibility for oversight.

Review market conduct, privacy and cybersecurity

Automated activity must remain within the applicable market framework and approved mandate. Surveillance should cover relevant electronic activity and investigate suspicious patterns through the compliance process. Limit access to sensitive inputs and records; confirm data rights, retention and security arrangements. Avoid putting customer data into third-party AI tools without the organisation’s approved legal and information-security review.

Prepare a research and validation brief

Send the proposed model purpose, project stage, instrument scope, available test evidence and required deliverables at a non-confidential level. Wigmore can discuss research, procurement coordination and specialist dependencies. Do not send executable strategies, customer records, account credentials or live order instructions. Any development, independent assurance or regulated service requires separately confirmed expertise, permissions and engagement terms.