A Scientific Advisory on Reinforcement Learning in Cashless and Mobile Financial Systems for Inclusive Finance and Equity Support in Developing Economies.

A Scientific Advisory on Reinforcement Learning in Cashless & Mobile Finance

Building fair, inclusive financial systems in developing economies

Authors: Dr. Syed Muntasir Mamun • Abdullah Al-Matin • Dr. Obi Umegbolu
Publisher: AI for Africa Thinktank (AIFAT)

Cashless and mobile finance have changed how people move money across Africa and Asia, but millions still remain outside the system. This advisory shows how Reinforcement Learning (RL) – a branch of AI that learns by doing – can make financial services more inclusive, safer, and fairer in fast-moving markets like Bangladesh and Nigeria.

What’s inside:
Clear, practical roadmap for using RL in Mobile Financial Services (MFS) to support inclusion and equity.
Real-world contrasts: Bangladesh’s bank-led MFS (e.g., bKash) vs. Nigeria’s fintech-driven, sandbox-enabled model (e.g., OPay, PalmPay).

High-impact use cases:

• Alternative credit scoring using digital footprints and psychometrics to reach the “unscorable.”
• Adaptive fraud detection (DQN, PPO) that reacts in real time and reduces false positives.
• Multi-agent systems for entrepreneurship (e.g., gamified financial literacy, supply-chain optimization).
• Barriers you need to plan for: digital divides and gender gaps, data scarcity, power instability, algorithmic bias, and risky PPP structures.
• Policy & implementation guide: RL sandboxes, local data infrastructure, fair reward design, and cross-border harmonisation—aligned with SDG 1 & SDG 8.

Key highlights:
In markets where MFS has already cut transaction costs by up to 90%, RL can push inclusion further by improving decisions as conditions change.

Bangladesh: strong, bank-led stability with AI-enabled MSME scoring; huge transaction volumes through bKash.

Nigeria: competitive, innovation-driven ecosystem with regulatory sandboxes and major fintech investment—fertile ground for RL pilots.

Risk lens: address the digital gender gap, rising cyber threats, and algorithmic bias so models don’t exclude those who need access most.

Actionable steps: set up RL sandboxes, invest in localized data centers, adopt human-centered reward shaping, and design responsible PPPs.

Who should read this:

• Central banks, regulators, and policymakers shaping digital finance.
• Banks, fintechs, MNOs, and payment providers building inclusive products.
• Donors, DFIs, NGOs, and ecosystem builders funding equitable innovation.
• Researchers and product teams exploring responsible AI in finance.

“RL lets financial systems learn and adapt in real time – so inclusion is a measurable outcome.”

Download the full report (PDF) to get the detailed frameworks, case studies, and implementation playbooks: