Use Case: Predictive Cash-Liquidity Forecasting for Mobile-Money Networks
Use Case: Predictive Cash-Liquidity Forecasting for Mobile-Money Networks
SIA Agile Solutions · Cloudera Certified Partner · Confidential — Ethiopia Market Intelligence
The Problem: Cash-Out Failures Are a Silent Revenue Drain
In a mobile-money network at scale — such as Telebirr in Ethiopia, with 52.5 million users, 110,000 agents, and 1.66 trillion Birr in transactions — liquidity management is mission-critical. When an agent's till runs dry, the customer cannot withdraw. They walk away, often to a competitor, and the agent loses the commission.
Today, refills are reactive: agents call only once they are already empty. Demand spikes — driven by market days, paydays, and local events — are bursty and local, hitting without warning. Meanwhile, a neighbouring agent two streets over may be overstocked. The problem is not missing liquidity; it is misallocated liquidity.
The Solution: Predict the Empty. Reroute the Refill.
SIA Agile Solutions, in partnership with Cloudera, has designed a streaming machine-learning solution that fuses three data streams already flowing through the network — no new hardware at the agent required:
- Live cash position — each agent's float and till balance, updated as transactions settle through the wallet ledger.
- Footfall & demand signals — population density, market days, paydays, and location heat maps showing where and when withdrawals cluster.
- Withdrawal history — per-agent cash-out time-series covering volume, velocity, and time-of-day patterns.
The Cloudera CDP platform ingests these streams in near-real-time and runs a depletion model for every agent, forecasting hours-to-dry and continuously re-scoring the network. Every agent flips green → amber → red as the forecast updates.
Three Steps to Smarter Liquidity
Step 1 — Ingest: Three data streams, already collected, are fused in near-real-time. The wallet ledger, geo and calendar data, and the transaction log feed directly into Cloudera without any changes to agent hardware or operations.
Step 2 — Predict: The ML model runs on the live fused data and produces a per-agent depletion forecast — projecting each agent's cash level across the next 12 hours. The network is rescored continuously as new events land, eliminating the stale overnight batch.
Step 3 — Act: One optimised cash-in-transit route is generated, servicing critical (red) and at-risk (amber) agents in priority order by time-to-dry. Surplus float is pulled from overstocked healthy agents nearby — liquidity moves, not just cash. Green agents are skipped until they need it.
The Outcome: Compounding Upside Across the Network
The same data, the same agents, the same van trip — only the timing changes, and the gain compounds:
- ▲ More successful cash-outs — customers complete withdrawals on the first try instead of walking to a rival.
- ▲ More transactions per agent — an agent that is never dry handles more volume across the day.
- ▲ More agent commission — more completed transactions mean more earnings and stickier agents.
Directional outcomes — magnitudes to be validated in a regional pilot.
Why Cloudera, Why Now
At 52.5 million users and 110,000 agents, batch reporting cannot catch a till before it empties. Cloudera CDP provides the full stack in a single deployment: streaming ingestion, the ML scoring model, and enterprise data governance through Ranger and Atlas lineage. The deployment starts with the data the operator already holds, proven in one region, then scales across the network.
Next step: a 90-day pilot on one region.
Contact SIA Agile Solutions to learn how we can help your mobile-money network eliminate cash-out failures with Cloudera CDP.