Executive Summary 

AI creates real value when aimed at narrow, high-value problems. In finance, that has meant using models to detect anomalies at scale, trigger real-time actions, and automate low-value tasks so specialists stay focused on judgement. 

The lesson is discipline. Banks invested heavily in these systems but never handed them the keys. They designed explainability, human oversight and compliance-grade controls from day one. Regulators could see the audit trail. Executives could trust the outcomes. 

AI in Finance: Transforming Decision-Making  

Finance has been the natural proving ground for AI because milliseconds matter. In 2023 alone, financial firms invested $35 billion into AI, a figure expected to rise to $97 billion by 2027. 

A case in point is algorithmic trading. Orders are executed in fractions of a second as conditions shift, creating an edge that no manual process could replicate. The energy parallel is real-time grid balancing and dispatch. Machine learning models can fuse weather forecasts, demand curves, and asset status into actionable recommendations. Instead of relying on slow manual processes, operators can see bids and redispatch options emerge in seconds.  

Risk management offers another parallel. In banking, algorithms continuously ingest new information to recalculate risk exposures. Energy operators face the same uncertainty: fluctuating fuel supply, renewable intermittency, and demand shocks. AI can provide continuously updated recommendations, arming leaders with a dynamic view of risk rather than static snapshots. 

Anomaly Vigilance: Fraud Detection → Grid Protection 

Today, nearly 75% of financial institutions use AI for fraud detection, making this the clearest transferable lesson. AI in finance scans millions of transactions, learns what “normal” looks like, and flags anomalies in real time.  

Energy can use the same statistical foundations. With the right telemetry, models can surface cyber intrusions on control systems, detect meter tampering, or highlight early equipment deterioration. These are high-signal events hidden in oceans of data. Miss them, and the costs cascade. 

Handled well, anomaly detection improves reliability, tightens security, and strengthens performance. Importantly, it can do so without overwhelming teams with noise. 

For energy, the early wins are clear: pipeline leak detection, substation monitoring, and market settlement assurance. These are domains where data volumes are enormous, failures are rare, and early detection delivers disproportionate value. 

Market Operations at Machine Speed

High-frequency trading proves that AI can absorb vast signal sets and still act at speed. That same logic applies to energy markets, where trading desks and control rooms often operate on thin margins and volatile conditions. 

Imagine a model that fuses weather forecasts, live demand signals and asset health telemetry to propose redispatch options or trading bids within seconds. Operators keep the authority to override, but the machine dramatically expands their field of vision. 

Finance has already validated the approach. Mastercard, for example, processes roughly a trillion data points to predict transaction fraud. That strategy is exactly what energy operators need for grid reliability and market optimisation. The lesson is not simply about speed. It’s about synthesising complexity into timely, explainable recommendations. 

Automation with Guardrails  

One of the most overlooked lessons from finance is that automation was never pursued for its own sake. Banks automated repetitive, low-value tasks such as reporting, reconciliation and monitoring so experts could focus on higher-order thinking. 

Energy has the same opportunity. Automating incident triage, asset-health scoring, and compliance reporting could save thousands of hours across large organisations. But the core rule still applies: safety-critical actions must remain under human oversight. 

This isn’t just operational discipline. It’s a regulatory necessity. Operators in both sectors expect decisions to be explainable, controls to be documented, and accountability to remain with humans. Done properly, automation becomes a force multiplier, not a liability.

The Finance Playbook for Energy 

Prioritise high-signal, high-value use cases: Focus AI where it creates outsized returns: anomaly detection, trading decision support, settlement assurance, and predictive maintenance. These are domains where a single detection or avoided outage more than pays for the investment. Spreading resources too thin only delays impact. 

Design for human-in-the-loop from day one: Finance proved models succeed when humans retain authority. Energy must follow suit by embedding override, validation, and audit trails into every workflow. This keeps regulators in control and ensures automation strengthens resilience rather than undermining it. 

Run small, scoped pilots with hard KPIs: Grand programmes fail. Focused pilots win. Define measurable outcomes, and scale what works. The discipline is to move fast, measure ruthlessly, and cut what doesn’t deliver. 

Invest in people, not just systems: The real edge is not the algorithm but the operator using it. Upskill traders, control-room staff and engineers to interpret and challenge model outputs. With training, AI becomes a decision accelerator; without it – just another black box. 

AI in finance shows the playbook: build explainability, keep humans in the loop, and make accountability non-negotiable. Energy can do the same.

The Bottom Line 

Finance has already proven what ‘AI done right’ looks like: anomaly detection at scale, decisions at machine speed, and human oversight at every step. Energy leaders who adopt the same playbook will not only run cleaner, safer, faster systems, but will also create a competitive advantage in a sector where resilience and reliability define winners.