AI for Bangladesh's Fintech & Mobile Financial Services
Fraud detection, alternative credit scoring, Bengali NLP chatbots, and agent intelligence — built for bKash-scale (65M+ users) MFS platforms, banks, and lenders.
The problem we solve
Mobile Financial Services in Bangladesh sit on a scale that most of the world's fintech infrastructure was not designed for. bKash alone reports more than 65 million registered users and over USD 30 billion in annual transaction volume. Nagad, Rocket, Upay, and TAP add tens of millions more. Across the sector, transaction volumes peak during Eid, salary cycles, and the start of the month at rates that make rules-based fraud engines either too noisy or too forgiving — and either failure mode costs real money.
The credit problem is just as distinctive. Bangladesh has more than 50 million adults outside the formal banking system. They have no CRC report worth scoring, no salary slip, and no collateral the bank can underwrite against. But they do have years of MFS transaction history, mobile recharge patterns, utility bill payments, and agent-counter cash-in behavior. The data exists; what's missing is a credit model that turns it into a defensible underwriting decision under Bangladesh Bank's evolving XAI and consumer-protection rules.
Then there's language and distribution. The majority of customer queries — to call centers, USSD menus, app help screens — arrive in Bengali, often mixed with English transliteration. A chatbot trained on English-only Hugging Face models will misclassify roughly a third of those queries on day one. And the 350,000+ field agents who do cash-in/cash-out across every union of Bangladesh need coaching, fraud watchlists, and KYC support that scales without a 1,000-person training team. None of these are off-the-shelf problems — they need AI built for Bangladesh.
What we deliver
Concrete engagements, not slide decks. Each one scoped to ship something measurable inside 90 days.
Real-Time Fraud Detection Pipeline
We build streaming fraud models that score each transaction in under 100ms — SIM-swap fraud, agent-side cash-out fraud, mule account detection, and account takeover. Models retrain weekly on new fraud patterns. Includes a case management UI for the fraud ops team and explainability hooks for Bangladesh Bank reporting.
Alternative Credit Scoring for Thin-File Customers
Credit models that work on MFS transaction history, recharge patterns, agent-counter behavior, and bill payment data instead of CRC reports. Designed for NBFIs, digital nano-lenders, and bank-MFS partnership lending products. Includes adverse-action explanations and a Bangladesh Bank XAI-ready audit trail.
Bengali NLP Customer Service Chatbot
Chatbots that handle Bengali, English, and Banglish (Bengali typed in Roman script) with the same model. Trained on your actual call-center transcripts and KB articles. Handles balance queries, transaction disputes, PIN reset, KYC re-verification, and escalates the long-tail to a human agent with full context.
Agent Intelligence & Coaching Platform
For MFS operators with 100,000+ field agents: a system that scores agent risk in real time (high cash-handling variance, geo anomalies, KYC drift), surfaces coaching nudges to the agent app, and gives the regional manager a focused list of 10-20 agents to follow up with this week instead of an unsorted churn list.
KYC & AML Automation
OCR and ID-verification models that handle Bangladeshi NID front/back, passport, and birth certificate variants. Liveness detection tuned for low-end Android phones. AML transaction-monitoring rules layered with ML scoring, with sanction list and PEP screening that maps to BFIU requirements.
Outcomes you can expect
These are sanitized ranges from real engagements and reasonable industry benchmarks — not anonymized fake numbers. Where the ceiling lands for you depends on your data, your team, and how quickly you can act on the model's output.
Fraud catch-rate up by 30-50% at lower false-positive rate
Typical range when a streaming ML model replaces or augments a pure rules engine. The largest gains usually come from SIM-swap and mule-account detection, where rules engines have always struggled.
Loan approval rates rise 15-30% for thin-file customers — at the same or lower default rate
Alternative-data credit scoring impact range for digital lenders moving from CRC-only underwriting to MFS-transaction-augmented underwriting.
Bengali chatbot containment of 55-75% of customer queries
Containment is the share of conversations the bot resolves without human handoff. Depends heavily on the breadth of your KB and how Banglish-heavy your customer base is.
Agent fraud loss reduced by 20-40% within 6 months
For MFS operators rolling out agent risk scoring across the full network. The biggest impact is on the long tail of agents who weren't on the manual watchlist but should have been.
Related reading
Ready to scope a pilot?
We'll spend 30 minutes understanding your operation, then come back with a one-page pilot plan and an honest range on cost, timeline, and expected outcome.