Building In-House AI Capacity in Banking: A Director’s Guide to What to Hire and How to Hire

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Building In-House AI Capacity in Banking: A Director’s Guide to What to Hire and How to Hire

Why Build AI Capacity Internally?

Artificial Intelligence (AI) is no longer a future concept — it is reshaping banking today. From detecting fraud in real-time to helping customers get faster loan decisions, AI has the potential to significantly increase efficiency, improve risk management, and enhance customer experience.

However, relying solely on outside vendors means you:

• May not fully own your data insights.
• Lose flexibility in tailoring solutions to your unique needs.
• Risk exposing sensitive customer data.

Building internal AI capacity allows the bank to:

• Control and secure its most valuable asset — customer and operational data.
• Develop models and systems that are custom-fit to the bank’s goals and compliance environment.
• Respond quickly to changing regulatory demands and market conditions.

🎯 Where to Start: Focus on Banking-Specific AI Use Cases

Before hiring, be clear on what problems you are solving.

Business Goal	Example AI Solution [Want to get automatic updates on ethel cofie’s blog post of Africa, technology, ecosystems and doing business in Africa sign up here ]
Reduce fraud and financial crime	Fraud detection systems that flag suspicious transactions immediately.
Improve credit decision-making	AI models that predict the likelihood of loan repayment more accurately than traditional scoring.
Enhance customer engagement	Chatbots and virtual assistants to answer customer queries 24/7.
Automate regulatory reporting	AI tools that compile, analyze, and submit compliance reports faster and with fewer errors.

 

👥 Who You Need to Hire — Explained for Non-Technical Leaders

Think of your AI team like a football team. Not everyone on the field does the same thing — you need strikers, defenders, and midfielders. Similarly, in AI, different roles contribute differently to the success of the team.

🟢 Core Team Roles (The Must-Have Positions)

1. Head of AI / Data Science (The Team Captain)

• Why You Need Them: Leads the AI efforts, connects business problems to technology solutions. Helps leadership understand where AI can bring value.
• What to Look For: Experience in both data science and business strategy, not just technology.

 

2. Data Engineer (The Plumber Who Builds the Pipes)

• Why You Need Them: AI runs on data. The data engineer ensures that the right data flows into the system, clean, organized, and ready for use.
• What to Look For: Strong understanding of how banking data works (e.g., customer records, transactions).

 

3. Data Scientist (The Analyst Who Builds the Brain)

• Why You Need Them: Designs the “thinking” part of the system — the models that make predictions (e.g., who is likely to default on a loan).
• What to Look For: Experience building models specifically for risk, fraud, or financial services.

 

4. ML (Machine Learning) Engineer (The Builder Who Puts the Brain to Work)

• Why You Need Them: Takes the models created by data scientists and makes them part of your everyday systems — integrating them into apps, dashboards, or backend systems.
• What to Look For: Ability to work closely with both IT and data teams to ensure models actually work in real-time environments.

 

🟠 Optional but Highly Valuable as You Grow

5. MLOps Engineer (The Operations and Maintenance Person)

• Why You Need Them: AI models need regular maintenance — just like cars. These engineers make sure the models stay up to date and work well as data changes.
• What to Look For: Experience with automation, monitoring, and improving AI systems over time.

 

6. AI Product Manager (The Business Translator)

• Why You Need Them: Ensures that what the AI team is building actually solves business problems. Acts as the bridge between leadership, operations, and technical teams.
• What to Look For: Strong understanding of banking operations combined with project management skills.

 

7. Fraud and Risk AI Specialist (The Domain Expert)

• Why You Need Them: Fraud and risk in banking are complex. This person brings deep knowledge of how fraud happens and works with the data team to catch it using AI tools.
• What to Look For: Background in risk management, fraud detection, or AML (anti-money laundering).

 

8. AI Ethics and Compliance Lead (The Rule Keeper)

• Why You Need Them: Ensures that AI models are fair, unbiased, and compliant with data privacy laws. Critical for avoiding regulatory issues.
• What to Look For: Knowledge of banking regulations and AI governance frameworks.

 

🏗️ How to Hire: A Phased, Director-Friendly Approach

Phase 1: Get Started with a Small Core Team

• Appoint a Head of AI / Data Science.
• Hire 1 Data Engineer + 1 Data Scientist.
• Start with a few high-impact projects (e.g., fraud detection, credit scoring).

✅ Tip: Focus on quick wins to build internal credibility before scaling up.

 

Phase 2: Expand When You See Results

• Add an ML Engineer to take models into production.
• Add an AI Product Manager to align projects with business priorities.
• Begin to formalize AI governance and compliance processes.

 

Phase 3: Mature and Specialize

• Hire MLOps Engineer to automate model maintenance.
• Bring on Fraud/Risk AI Specialist if fraud management is critical.
• Appoint an AI Ethics Lead as regulations tighten.

 

🛡️ Questions a Director Should Ask During Hiring:

• Can the candidate explain their work in simple, business terms?
• Do they understand financial services data and its unique challenges?
• Have they worked on projects where regulators or auditors were involved?
• Can they show examples of measurable business impact from their previous AI work?

 

🚩 Final Reminders for Directors:

Do’s	Don’ts
✅ Tie every AI hire to a clear business objective.	❌ Don’t hire data scientists alone without data engineers — models need clean data.
✅ Focus on explainability — AI should make sense to humans.	❌ Don’t assume all AI work can be outsourced if it involves sensitive data or core risk functions.
✅ Build AI governance early — involve risk, compliance, and legal teams.	❌ Don’t ignore regulatory implications of AI (bias, fairness, data privacy).