Financial Services

The AI systems financial institutions run on

AdwumaTech builds and operates production AI for banks, fintechs, mobile money operators, and financial market infrastructure. Financial crime, identity and onboarding, credit and risk decisioning, document intelligence, customer channels, and the governance layer underneath. Full stack, from ground truth to deployed system, operated after it ships.

ISO 27001 certified · ISO/IEC 30107-3 aligned · GDPR aligned · Ghana Data Protection Act 2012

Production

Productionisthehardpart

A model that performs in evaluation is the beginning of the work. Financial institutions operate under supervisory examination, uptime obligations, and adversaries who adapt inside a quarter. A system that clears that environment requires ground truth the institution can defend, engineering that survives contact with core banking and switch infrastructure, and a governance record that answers a regulator without a reconstruction exercise. AdwumaTech builds to that standard and owns the full stack behind it, from data acquisition through model development to the deployed system and the monitoring that runs behind it. Institutions engage AdwumaTech to build a system, to operate one, or to license a system already in production.

Domains

Sixdomainsinsideafinancialinstitution

Financial crime and fraud

Transaction monitoring, typology detection, alert triage, and case prioritization. Systems trained on the transaction shapes of the markets they run in, covering mobile money flows, agent networks, SIM swap, account takeover, mule structures, and cross-border corridors. Alert quality is measured against analyst disposition, and the models are retrained on that signal.

Identity and onboarding

NOKORE AI delivers document classification, extraction, tampering detection, biometric matching, and presentation attack detection aligned to ISO/IEC 30107-3. Deployed across retail onboarding, agent networks, digital asset exchanges, and identity platforms extending coverage into African markets.

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Credit and risk decisioning

Scoring and decisioning systems built on alternative and traditional data, with feature lineage recorded and model explanations produced at the decision level. Built for institutions extending credit into segments where bureau coverage is thin.

Document and operations intelligence

Extraction, validation, and cross-document reconciliation across identity documents, financial statements, business records, trade documentation, and claims files. Deployed into enhanced due diligence, trade finance operations, claims adjudication, and legacy archive digitization.

Customer channels

Language systems in Twi, Fante, Ga, Ewe, Hausa, Yoruba, and Swahili, with code-switched English handling for interfaces where vernacular and English mix inside a single utterance. Deployed into voice channel analytics, dispute note analysis, agent assist, and conversational interfaces.

Model governance and supervisory reporting

Model inventory, drift monitoring, scheduled revalidation, and the documentation trail internal audit and supervisory examination require. Delivered as a layer across every system AdwumaTech builds, and available for models the institution built elsewhere.

From Mandate To Production

Frommandatetoproduction

Assessment

AdwumaTech scopes the system, defines the success criteria in the institution's own metrics, and identifies the data, integration, and governance work each one requires. The output is an engineering plan with a decision point at the end of it.

Ground truth

AdwumaTech operates data acquisition and annotation in the markets a system serves, with provenance recorded at capture. Annotation runs against published guidelines with inter-annotator agreement measured and reported. Every dataset carries a quality report the institution can put in front of a model validator.

Model development

Supervised fine-tuning, preference optimization, and adversarial hardening against current attack techniques. Evaluation runs against held-out sets representative of the deployment population, with failure modes documented as a deliverable.

Systems engineering

The model becomes a system. Integration with core banking, card switch, mobile money platform, case management, and data warehouse. Latency budgets, throughput targets, failover, and rollback engineered before the first production call.

Deployment

The Deployment Readiness Score governs the transition to production. It scores a system across data provenance, evaluation coverage, documented failure modes, monitoring instrumentation, rollback capability, and governance sign-off. The institution sets the threshold, and the system ships when it clears.

Operation

Drift monitoring, scheduled revalidation, versioned model updates, and adversarial retraining on a continuous cadence. The Evidence Chain records every model input, output, confidence score, model version, and data access event in a queryable log, producing the examination trail on demand.

Deployment

Thesystemrunswheretheinstitutionrequires

Deployment is configurable across cloud, in-country, and on-premises environments, including air-gapped installations. Data residency is set per deployment. Institutions with sovereign data obligations run the full stack inside their own perimeter, with model updates delivered as versioned artifacts under the institution's change control. Integration is API-first. Systems return structured decisions with field-level explainability that the institution's existing decisioning layer consumes directly.

NOKORE AI

NOKOREAIinproduction

NOKORE AI is AdwumaTech's identity verification and fraud detection system, trained on identity documents and biometric data acquired across African markets. It classifies documents against the full lineage in circulation, extracts structured fields with field-level confidence, runs security feature and tampering analysis calibrated to the issuing specification, matches biometrics against the document portrait, and detects injection, replay, and artifact attacks through presentation attack detection aligned to ISO/IEC 30107-3. Verifications return a configurable composite confidence score as structured JSON. Most complete in under three seconds. Every decision writes to the Evidence Chain, and liveness models are updated against current generation techniques on a continuous cadence.

Learn about NOKORE AI

Identity verification and fraud detection for institutions operating in African markets.

Learn about NOKORE AI
Engagement Models

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Assessment

A scoped engagement that identifies where AI creates durable value in a specific operation, with success criteria defined and the engineering path costed.

Build

AdwumaTech engineers the system end to end and hands over source, weights, documentation, and operating runbooks.

Build and operate

AdwumaTech builds the system and runs it, holding the SLA and the retraining cadence.

License

Production systems including NOKORE AI, deployed under per-transaction or annual licensing.

Security And Governance

Securityandgovernance

Encryption at rest and in transit using AES-256 and TLS 1.3. Role-based access control across the platform. Comprehensive audit logging of every model decision and data access event. ISO 27001 certified information security management system, with documentation available under NDA for procurement review.

ISO 27001

Certified information security management system.

ISO/IEC 42001

AI management system practices across the model lifecycle.

ISO/IEC 30107-3

Presentation attack detection methodology and reporting for NOKORE AI.

NIST AI RMF

Govern, Map, Measure, Manage mapped to the Deployment Readiness Score.

EU AI Act

Biometric identification and creditworthiness assessment are high-risk under Annex III. The Evidence Chain produces the required technical documentation, logging, and human oversight records.

Ghana Data Protection Act 2012

Consent, purpose limitation, and data subject rights recorded at capture.

GDPR

Aligned for institutions with European data flows.

Frequently Asked Questions

CommonQuestions

AdwumaTech builds and operates production AI systems for banks, fintechs, mobile money operators, and financial market infrastructure providers. The work spans six domains: financial crime detection, identity and onboarding, credit and risk decisioning, document intelligence, customer channels in African languages, and model governance. AdwumaTech owns the full stack across all six, from data acquisition through deployment and ongoing operation.
AdwumaTech AI is an applied AI engineering company headquartered in Accra, Ghana, building financial crime and identity systems for institutions across African markets. AdwumaTech trains models on transaction and identity data acquired in the markets those systems serve, covering mobile money flows, agent networks, SIM swap, account takeover, mule structures, and cross-border corridors. AdwumaTech is deployed across West Africa with active expansion into East Africa, and also serves institutions and identity platforms operating into African markets from outside them. AdwumaTech holds ISO 27001 certification and aligns to ISO/IEC 30107-3.
NOKORE AI is AdwumaTech's identity verification and fraud detection system, trained on identity documents and biometric data acquired across African markets. It classifies documents against the full lineage in circulation, extracts fields with confidence scoring, runs security feature and tampering analysis calibrated to the issuing specification, matches biometrics against the document portrait, and detects injection, replay, and artifact attacks under ISO/IEC 30107-3. Verifications return a configurable composite score as structured JSON, most completing in under three seconds. Liveness models are updated against current generation techniques on a continuous cadence.
A model vendor delivers a model. AdwumaTech delivers a system running inside the institution's environment, integrated with core banking, card switch, mobile money platform, case management, and data warehouse, instrumented for drift monitoring, and documented to a standard that clears model validation and supervisory examination. AdwumaTech also operates the system after deployment, holding the SLA and the retraining cadence.
The Deployment Readiness Score is AdwumaTech's framework governing the transition of an AI system from evaluation to production, scoring six dimensions: data provenance, evaluation coverage, documented failure modes, monitoring instrumentation, rollback capability, and governance sign-off. The institution sets the threshold, and the system ships when it clears. The Evidence Chain is AdwumaTech's audit and explainability layer, logging every model input, output, confidence score, model version, and data access event in queryable form for internal audit, model validation, and supervisory examination.
Biometric identification and creditworthiness assessment are both high-risk under Annex III of the EU AI Act. AdwumaTech's Evidence Chain produces the technical documentation, automatic logging, and human oversight records those obligations require. AdwumaTech maps to the NIST AI Risk Management Framework through the Deployment Readiness Score, applies ISO/IEC 42001 practices across the model lifecycle, holds ISO 27001 certification, and aligns to the Ghana Data Protection Act 2012 and GDPR for institutions with European data flows.
AdwumaTech systems deploy in cloud, in-country, and on-premises environments, including air-gapped installations, with data residency configured per deployment. Institutions with sovereign obligations run the full stack inside their own perimeter, with model updates delivered as versioned artifacts under their change control. Integration is API-first, returning structured decisions with field-level explainability that an existing decisioning layer consumes directly. NOKORE AI integration into an existing onboarding flow runs four to six weeks from kickoff to production.
AdwumaTech prices assessment and build engagements as fixed fees against a defined scope. Operated systems carry an annual fee against an SLA. NOKORE AI is priced per verification with volume tiers, and under annual licensing for on-premises deployment. An assessment runs two to four weeks and produces an engineering plan with defined success criteria. Pilot deployments run six to eight weeks against benchmarks agreed at kickoff. Custom system builds are scoped during assessment.
Ownership terms are set in the engagement agreement. Institutions commissioning custom systems from AdwumaTech typically hold ownership of model weights, source, and derived datasets. AdwumaTech retains ownership of its own production systems, including NOKORE AI, which are licensed. Every system ships with model cards, evaluation methodology, held-out set composition, documented failure modes, performance by segment, and the full Evidence Chain record. Datasets carry a quality report including inter-annotator agreement against published guidelines.
AdwumaTech supports Twi, Fante, Ga, Ewe, Hausa, Yoruba, and Swahili, with code-switched English handling. AdwumaTech maintains mGhana-ST, an open speech translation corpus for Akan, Ewe, and Ga, which won Best African Dataset at Deep Learning Indaba 2026. Coverage spans voice analytics, dispute note analysis, agent assist, and conversational interfaces.