← All Case StudiesFinancial Services

AI-Powered Fraud Detection Across a 14-Country Banking Network

Deploying a real-time machine learning fraud detection system across a pan-African banking network, reducing fraud losses by 62% while maintaining sub-100ms transaction decisioning latency.

KEY RESULT62% Fraud Loss Reduction
CLIENT PROFILEPan-African Commercial Banking Group
ENGAGEMENT TIMELINE12 Months
CORE SERVICES
✓ AI Strategy & Model Design✓ MLOps Platform Architecture✓ Regulatory Compliance Advisory

The Challenge

Rapid expansion across 14 African markets had created a fragmented fraud detection landscape: 11 different legacy rules engines, each tuned to local transaction patterns, with no cross-border intelligence sharing. Sophisticated fraud rings were exploiting the seams between jurisdictions, routing transactions through countries where detection models were weakest.

Central Bank mandates in three key markets required all transaction decisions to be explainable to regulators — ruling out black-box deep learning models and demanding interpretable AI architectures.

The Strategic Solution

1.

Unified Feature Store & Cross-Border Intelligence

Built a centralised feature engineering platform aggregating transaction signals across all 14 markets, enabling models to detect cross-border fraud patterns invisible to siloed country-level engines.

2.

Explainable ML Model Suite

Deployed gradient boosting models with SHAP-based explainability, producing human-readable decision rationales for every flagged transaction — satisfying regulatory requirements in all operating jurisdictions.

3.

Real-Time MLOps Infrastructure

Designed a model serving infrastructure delivering fraud decisions in under 100 milliseconds at peak transaction volumes, with automated model retraining pipelines triggered by concept drift detection.

Measurable Outcomes

FRAUD REDUCTION62%

Decrease in fraud losses in the 12 months following deployment.

DECISION LATENCY<100ms

Real-time transaction decisioning at peak transaction load.

FALSE POSITIVE RATE−44%

Reduction in legitimate transactions incorrectly flagged.

“

The explainability requirement felt like a constraint at first. Agradiant turned it into an advantage — our models are now our strongest argument with regulators, not our biggest risk.

Fatima Al-HassanChief Risk Officer, Pan-African Banking Group

Interested in a similar engagement?

Request an Executive Briefing →