Case Study
Ayo | MTN AI-Enabled Claims Automation
Business Results
Increased engagement
Enabling fast, mobile-first claims via WhatsApp.
Improved processing
Average processing time reduced from 9 days to under 4 hours for automated claims
Growth in automation
Over 80% of claims with limited assessor intervention
Headquarters: Roodepoort, Gauteng, South Africa.
Industry: Microinsurance – Health, Life, and Device Protection
Use Case: OCR-based document processing, Predictive fraud and reasonability models, secure, audit-ready ECM capability
Department: Claims Management
Integrations: Alfresco Process Automation, AWS Textract / Azure Cognitive Services, Brainware, Clickatell WhatsApp API, Decision Model Notation, Snowflake, MTN Payments API
Setting the Stage
Expanding mobile-based microinsurance across Africa with accessible, low-cost coverage.
aYo Holdings is a pan-African microinsurance business jointly owned by MTN and Sanlam. With operations in Uganda, Ghana, Zambia and expanding across multiple African markets, aYo provides low-cost life, hospital, and device insurance products to underserved populations.
These products are offered through mobile platforms (USSD, WhatsApp, mobile apps), enabling frictionless access to insurance and fast claims servicing.
The Challenge
Delivering a fully automated, AI-enabled claims platform across multiple countries and systems.
Falcorp was appointed to lead the end-to-end design and implementation of a fully automated claims processing platform to replace aYo’s previously manual, spreadsheet-driven claims model.
The platform includes automated intake via WhatsApp, OCR-based document processing, predictive fraud and reasonability models, and a secure, audit-ready ECM capability.
This multi-country, multi-year transformation project supports claims processing across diverse products, languages, and platforms, and integrates with aYo’s Khula and eBao policy admin systems as well as MTN’s MoMo wallet for real-time claim payouts,
Manual processes, inefficiencies, and compliance risks prevented effective scaling.
Falcorp was appointed to lead the end-to-end design and implementation of a fully automated claims processing platform to replace aYo’s previously manual, spreadsheet-driven claims model.
Although claims were submitted digitally, they were assessed and stored manually. WhatsApp and Google Drive were used to exchange documents, but without proper record controls. Spreadsheets and emails were the main tools for tracking, leading to data loss and delays.
Every claim required assessor input, often involving over 14 assessors per country, causing turnaround times to exceed nine working days.
Data storage practices also posed regulatory risks, and the growing volume of claims from new countries made manual operations unsustainable.
The Solution
A cloud-native claims platform integrating OCR, automation, and predictive modelling.
Falcorp implemented a fully digital solution covering the entire claims lifecycle, from WhatsApp-based submission to payment via MTN MoMo.
The platform includes document capture, OCR, auto-indexing, and classification using Azure or AWS Textract. Predictive models flag fraud using geolocation, behavioural, and network data. A rules-driven orchestration engine determines whether to auto-pay, auto-reject, or queue claims for manual review.
ECM functionality ensures secure, auditable storage, while the platform handles claims centrally across both Khula and eBao systems with support for localisation.
Falcorp executed a phased implementation across multiple African countries using an agile, low-code, and collaboration-driven approach.
Deployment was staged across Uganda, Ghana, Zambia, Côte d'Ivoire, Nigeria, Benin, and Cameroon.
The rollout followed a sequential build from document ingestion to OCR, classification, predictive decisioning, and finally orchestration. The agile team included Falcorp consultants, aYo staff, and external ML/data experts.
Predictive models were trained using real-world claims and fraud data from each country. Emphasis was placed on low-code platforms to minimize custom development, and aYo teams were upskilled to manage and extend the platform independently.
Falcorp brought deep expertise in claims automation, predictive modelling, and cloud-native content management tailored for Africa’s mobile-first environment.
Deliverables included strategic advisory and project management, UX design, and solution architecture. Falcorp implemented the product administration system and designed the claims process and digital architecture.
Services also covered ECM deployment, document indexing, and retention policies. They managed quality assurance, integrated OCR and fraud models with custom training data, and connected mobile channels like WhatsApp.
A real-time decision engine and DMN rules were configured, and the platform was integrated with both Khula and eBao systems. Falcorp continues to support DevOps and platform scalability.
What we Achieved
The project delivered transformative results in efficiency, scalability, and regulatory compliance across aYo’s operations.
More than 80% of claims are now processed with minimal human involvement. Average processing time dropped from over nine days to under four hours for automated claims. Assessor staffing requirements were cut by 50–75% per country.
Document handling is now secure, compliant, and fully auditable. Claims can be processed centrally regardless of policy system or location, and fraud detection has significantly improved through predictive model application.
Adoption
Falcorp successfully implemented a fully automated, AI‑enabled claims processing platform for aYo.
Looking forward
AI-powered claims automation tailored for complex, multi-country insurance environments
The aYo engagement exemplifies Falcorp’s strength in designing and implementing digital claims platforms that are secure, scalable, and integrated across telecom and insurance systems.
The project highlights capabilities in automation, ECM, mobile integration, and real-time decisioning, and is directly relevant to insurers aiming to scale digitally while reducing costs.
Additionally, Falcorp developed an enterprise data warehouse for aYo using a Data Vault pattern.
This solution consolidated over 800 tables aligned with the core PAS object model. Using ELT, ETL, and CDC methods, the data was transformed into a semantic warehouse optimized for analytics and reporting.
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