- Introduction
The digital transformation of Nigeria’s financial and legal sectors has seen
a significant uptick in the use of Artificial Intelligence (AI) tools to optimize
debt collection and asset tracing. These tools promise increased efficiency,
automation of repetitive tasks, predictive analytics, and real-time
surveillance of debtor behaviors. However, the use of AI in these domains
raises critical concerns about data privacy, particularly in a legal
environment still evolving in terms of data protection enforcement.
In this article, we explore the legal and ethical obligations of debt collectors,
legal practitioners, financial institutions, and data processors in deploying
AI technologies while ensuring compliance with Nigeria’s data protection
regime. - Understanding the Role of AI in Debt Collection and Asset Tracing
AI applications in debt recovery and asset tracing include:
Predictive analytics to evaluate debtor behavior and likelihood of
repayment.
Automated skip tracing using data from telecoms, social media,
financial records, etc.
Natural Language Processing (NLP) for parsing legal documents
and communication with debtors.
Facial recognition and geolocation tracking for high-stake
recovery operations.
Machine learning algorithms to flag fraudulent asset transfers or
hidden assets.
While these innovations offer enhanced capacity, they often involve large-
scale data processing — including sensitive personal data — which
introduces serious privacy risks if not properly managed. - Legal Framework for Data Privacy in Nigeria
3.1 Nigeria Data Protection Act 2023 (NDPA)
The Nigeria Data Protection Act, 2023, now the principal legislation
governing data privacy, defines obligations for data controllers and
processors. Key provisions relevant to AI-based debt collection include:
Lawful basis for processing (Section 24): Data must be processed
based on consent, performance of a contract, compliance with a legal
obligation, or legitimate interest.
Data minimization (Section 25): Only data necessary for the
specific purpose should be collected and processed.
Purpose limitation (Section 26): Personal data must be collected
for explicit and lawful purposes.
Data subject rights (Sections 32–39): Includes right to access,
rectification, erasure, objection to processing, and data portability.
3.2 Other Relevant Laws
Cybercrimes (Prohibition, Prevention, etc.) Act, 2015 –
criminalizes unauthorized access or misuse of computer systems and
data.
Central Bank of Nigeria Guidelines on Credit Bureaus and Debt
Recovery – mandates lawful access and reporting of debtor
information.
Evidence Act, 2011 – provides for admissibility of electronically
obtained evidence, which may include AI-driven reports. - Key Data Privacy Risks in AI Adoption for Debt Collection
4.1 Excessive Data Profiling
AI systems may overreach by gathering more personal data than
necessary, violating the principle of data minimization. For instance,
scraping social media or telecommunications data without consent is
potentially unlawful.
4.2 Bias and Discrimination
Machine learning models may unintentionally discriminate based on
historical bias in data (e.g., targeting individuals from specific regions or
income groups), infringing Section 38 of the NDPA on non-discriminatory
processing.
4.3 Lack of Transparency
AI systems, especially complex algorithms, often lack explainability. This
violates the right to transparency under the NDPA, which requires data
subjects to know how their data is processed.
4.4 Insecure Data Handling
Weak cybersecurity controls around AI platforms can lead to unauthorized
access, data breaches, and identity theft — with penalties under Section
61 of the NDPA.
- Ensuring Compliance: Best Practices for AI Deployment in Debt
Collection
5.1 Conduct a Data Protection Impact Assessment (DPIA)
Before deploying AI tools, debt recovery agencies must conduct a DPIA
(mandatory under Section 40 NDPA) to evaluate the risks associated with
large-scale, automated data processing.
5.2 Obtain Valid and Informed Consent
If relying on consent, ensure it is:
Freely given, specific, informed, and unambiguous;
Evidenced in writing or digital form;
Capable of being withdrawn at any time.
5.3 Limit Data Collection and Access
Implement role-based access control (RBAC) to restrict access to
sensitive data only to authorized personnel. Avoid storing more information
than is legally justified.
5.4 Use Explainable AI (XAI)
Adopt transparent algorithms where decisions, such as scoring a debtor
as high-risk, can be explained to data subjects, enhancing accountability
and compliance.
5.5 Ensure Vendor and Third-Party Compliance
Where AI tools are outsourced (e.g., cloud-based predictive analytics),
ensure third-party vendors:
Are compliant with NDPA;
Sign Data Processing Agreements (DPAs);
Are subject to audits and oversight.
5.6 Appoint a Data Protection Officer (DPO)
Entities involved in regular debt collection should appoint a DPO as
required by the NDPA, to oversee compliance and act as a liaison with the
Nigeria Data Protection Commission (NDPC).
- Enforcement and Sanctions
Non-compliance with data privacy in the context of AI deployment can lead
to:
Monetary fines – up to 2% of gross annual revenue or ₦10
million, whichever is higher.
Criminal prosecution for unauthorized data disclosure.
Civil liability – Data subjects may sue for breach of privacy rights
under Section 37 of the 1999 Constitution (as amended).
Several enforcement actions have already been taken by the NDPC since
2023 against banks, loan apps, and data brokers. - Conclusion
AI has the potential to revolutionize debt collection and asset tracing in
Nigeria, making them faster, more intelligent, and scalable. However, this
transformation must not come at the cost of data privacy rights and
constitutional freedoms. Compliance is not only a legal duty but a business
imperative in a data-driven economy.
Recommendations: - Build AI ethics into operations – integrate fairness, accountability,
and transparency from development to deployment. - Invest in data governance infrastructure – including encryption,
audit trails, and access logs. - Educate stakeholders – legal practitioners, banks, and recovery
agents must be trained on the NDPA and AI compliance. - Engage with the NDPC – for guidance, registration, and cooperation
in sensitive or large-scale operations.
As AI continues to disrupt financial recovery, embedding robust privacy
safeguards will ensure trust, legitimacy, and long-term success.
