Early access. Our credit models are still being trained on real lending data. See where we are
CediSense.ai

AI credit decisioning for Ghanaian lenders

Credit decisions that make sense.

CediSense gathers the evidence, scores the risk and explains every recommendation, so your credit officers can decide in minutes instead of days. Your officers keep the final say.

Book a demo Watch the agent work

Works over WhatsApp, the web and your own systems. Built around Ghana Card, credit bureau and mobile money data.

Credit memo Example
Abena M. Market trader · Kejetia, Kumasi · Ghana Card verified
Request
GH₵ 5,000
Tenor
6 months
Band
B4.8%
Recommend: approve Waiting for officer sign-off
  • Steady mobile money inflows for 6 monthsSource: MTN MoMo statement
  • No missed payments on credit recordSource: credit bureau report
  • Income dips each February, a seasonal patternSource: MoMo transactions
  • Instalment is 22% of monthly income, inside the 40% limitSource: affordability check
Built for
  • Microfinance institutions
  • Savings and loans companies
  • Rural and community banks
  • Digital lenders

The problem

Good borrowers are hard to see on paper.

Traders, farmers and gig workers often repay reliably, but their income lives in mobile money and cash, not payslips and bureau files.

Thin credit files

Many applicants have little or no bureau history, so officers fall back on collateral and guarantors and turn good customers away.

Slow, manual reviews

Paper forms, repeat document requests and branch visits stretch decisions to days or weeks, and results vary from branch to branch.

Rising compliance demands

Data protection rules, consumer protection and Bank of Ghana oversight all ask for clear records of how each decision was made.

How it works

From application to decision in four steps.

  1. Apply anywhere

    Applicants start on WhatsApp or the web, or your own system sends the application to us. Documents go in as photos.

  2. Gather with consent

    With the applicant's permission for each source, CediSense checks the Ghana Card and pulls bureau, mobile money, bank and payslip data.

  3. Score and explain

    Your risk model and credit policy set the score. The agent writes a one-page memo where every line points to its source.

  4. Your officer decides

    Officers approve, adjust or decline in one screen. Declines always get a human review, and applicants hear the reasons.

Watch the agent work

One application, start to finish.

Pick a sample applicant and watch CediSense check identity, read the data, score the risk and hand a recommendation to the credit officer. Then make the officer's decision yourself.

Simulation · sample data 00:00.0
Agent pipelineReady
ApplicationWaiting AI agentGuides, gathers, checks Ghana CardIdentity BureauCredit history Income dataMoMo, payslip Scoring engineRisk model + your credit policy ExplanationReasons and credit memo Credit officerMakes the decision
Agent logIdle
Abena M.
Market trader · Kejetia, Kumasi
GH₵ 5,0006 months · Working capital
Via WhatsApp
MoMo inflows (GH₵)

Waiting for data

Risk score
—Chance of default
A<3%B3–6%C6–10%D10–18%E18%+
Policy checks
  • Run after scoring
Reasons
  • Written by the agent from the evidence
Recommendation
Waiting for the pipeline

This is a simulation with made-up applicants, and it runs faster than real time. Our models are still being trained on real lending data, so the scores shown are illustrations, not results from the CediSense model.

AI insight. Human judgement.

The agent does the legwork. People make the call.

CediSense keeps the AI and the decision apart, so every outcome can be explained to a customer, an auditor or a regulator.

What the agent does

  • Guides applicants step by step in English or Twi
  • Reads payslips, bank statements and MoMo statements
  • Spots mismatched names, edited documents and income gaps
  • Chases missing documents so your officers don't have to
  • Drafts the credit memo, with a source for every claim

What it never does

  • Set or change the risk score
  • Decline a loan without a person reviewing it
  • Promise an applicant they will be approved
  • Use data the applicant did not agree to share
  • Invent facts the evidence does not support

Features

Everything a credit team needs in one place.

Mobile money scoring

Turns months of MoMo history into clear signals: income level, stability and seasonal patterns, for borrowers with thin files.

Reasons you can read

Every score comes with its main reasons in plain language, ready for the officer and for the customer's decision notice.

Your credit policy

Set limits, debt-to-income rules and approval thresholds yourself. Changes need a second approver and are versioned.

Fraud signals

Flags duplicate Ghana Cards, reused devices and tampered PDFs before they reach a decision.

Complete audit trail

Every input, consent, model version and officer action is logged and exportable as one decision record.

Speaks your customers' language

Applications and decision notices in English and Twi, over WhatsApp and SMS as well as the web.

Compliance

Designed for Ghana's rules from day one.

CediSense is software for licensed lenders. You keep your licence, your customers and your decisions. We give your compliance team the records to show how each decision was made.

  • Human review of every decline Act 843, s.41

    No applicant is refused by an algorithm alone, and every decline can be appealed to a different officer.

  • Consent for each data source Data Protection Act, 2012

    Applicants agree to each check separately, and every consent is recorded with the wording they saw.

  • Credit data used properly Credit Reporting Act, 2007

    Bureau reports are pulled through your own bureau credentials and only for the purpose the applicant agreed to.

  • Ready for vendor due diligence Bank of Ghana

    Security policies, continuity and exit plans, and access for your auditors, packaged for your IT and risk teams.

  • Fairness, measured Monthly reports

    Approval and repayment results are compared across gender, age and region, so gaps are found and fixed.

Where we are

Our models are still learning from real data.

We are training and testing CediSense's credit models on real loan data, shared with consent. Until that work is finished and reviewed, CediSense runs only in shadow mode: it scores alongside your officers and never affects a live decision.

  1. Training on real loan data NowLearning from repayment outcomes, mobile money and bureau records.
  2. Shadow pilots with partner lendersScoring live applications in the background while officers decide as usual.
  3. Accuracy and fairness reviewChecking results against real repayments, across gender, age and region.
  4. Live recommendationsOfficers see CediSense recommendations in their console, with your thresholds.

Become a pilot partner

Help shape the model, and see the results first.

We're looking for a small number of lenders to join our pilot. CediSense scores your live applications in shadow mode, without changing how you work, and you compare its recommendations with real repayment outcomes.

Weeks 1–4
Connect and calibrateLink your systems, load your credit policy and agree the success measures.
Weeks 4–12
Shadow scoringCediSense scores in the background. Your officers decide as usual.
Review
Compare outcomesDecision time, approval rates and early repayment, side by side.
Go live
Officers use the memoRecommendations appear in the officer console, with your thresholds.

Book a demo

See CediSense on your own loan products.

Email us about your institution and we'll set up a 30-minute walkthrough with your credit team. We reply within two working days.