RiskIFRS 9Compliance

NPLs, provisions & IFRS 9 in leasing: industrialising credit risk management

In leasing, credit risk management does not end at the credit decision. It extends to the continuous monitoring of the portfolio, the rigorous classification of non-performing receivables (NPLs), the calculation of provisions and the active management of collections. This article examines how to industrialise this framework to meet IFRS 9 and local regulatory requirements.

June 3, 2026•TNA Insights•8 min read

Credit risk in leasing: a specificity not to be underestimated

Leasing presents characteristics that distinguish it from conventional lending in terms of risk. The lessor retains ownership of the financed asset, which constitutes real collateral — but whose recovery value depends on the condition of the asset, its resale market and repossession lead times. In the event of lessee default, the institution must simultaneously pursue recovery of unpaid rentals and manage the physical asset. This duality complicates the assessment of expected losses and reinforces the importance of a structured monitoring framework capable of detecting deterioration signals well before a receivable enters litigation.

Real collateral on the financed asset, but uncertain recovery value.
Dual challenge of financial recovery and physical asset management upon default.
Need to detect deterioration signals ahead of litigation.
Concentrated exposure on certain sectors or asset types that require monitoring.

The regulatory framework: IFRS 9, BAM/BCEAO and OHADA

Financial institutions operating in Morocco and the UEMOA/CEMAC zone are subject to a dual framework: IFRS 9 international standards on one hand, and local regulations from Bank Al-Maghrib (BAM), BCEAO and COBAC on the other. IFRS 9 requires a forward-looking approach to credit risk based on Expected Credit Losses (ECL), with three classification stages reflecting the degree of deterioration since origination. Local regulators maintain their own NPL classification grids, with flat-rate or stepped provisioning rates based on days past due. SYSCOHADA imposes specific accounting entries for impairments and provision reversals. Navigating these frameworks — often with dispersed data — is one of the main challenges for risk teams.

IFRS 9: three-stage classification (Stage 1, 2, 3) based on risk deterioration since origination.
ECL: expected loss calculation over 12 months (Stage 1) or lifetime (Stages 2 and 3).
BAM/BCEAO regulations: NPL grids and flat-rate provisioning requirements.
SYSCOHADA: mandatory accounting entries for impairments and reversals.
Contagion rule: a client default can trigger reclassification of all their outstanding exposures.

Why it is difficult in practice

On the ground, risk teams at leasing companies face several obstacles. The data needed for scoring and classification is scattered across the contract management system, accounting, arrears tracking files and sometimes manually maintained spreadsheets. Dossier scoring often remains subjective, based on individual analyst experience rather than formalised, reproducible criteria. IFRS 9 provision calculations require default history data that many institutions have not yet structured systematically. Finally, pre-litigation and litigation recovery tracking is rarely connected to receivable classification, creating gaps between the actual risk position and reported figures.

Scoring data scattered across disconnected systems.
Subjective scoring that varies between analysts.
Insufficient default histories to calibrate ECL models.
Collections tracking disconnected from receivable classification.
Provisioning often performed at period-end on the basis of manual exports.

How to industrialise credit risk management

Industrialising the risk framework rests on three complementary pillars. The first is structured scoring: each dossier is assessed against a formalised grid combining financial ratios (debt service coverage, leverage, rental coverage), qualitative factors (sector, tenure, management quality) and payment incident history. The output feeds a risk classification that drives origination decisions and the level of post-drawdown monitoring. The second pillar is automated NPL classification: a configurable engine applies the regulator's rules (days past due, contagion rule) and updates portfolio classification continuously, without rekeying. The third pillar is active collections: a case-level tracking record traces every action — amicable reminder, formal notice, litigation entry — with a timestamp and responsible owner, enabling management to monitor progress and prioritise high-stakes cases.

Formalised scoring: financial ratios + qualitative criteria + payment incident history.
Automated NPL classification based on regulatory rules, updated continuously.
Contagion rule applied automatically to all exposures of a defaulting client.
Provision calculation based on regulatory grids and/or ECL models.
Collections tracking record: amicable, pre-litigation, litigation phases with full action history.
Consolidated risk reporting fed in real time from the portfolio.

From risk framework to integrated platform

The success condition for this industrialisation is data integration: scoring, classification and collections must all be connected to the same contract, schedule and payment flow repository. Without this data unity, risk teams will continue to produce provisions at month-end from manual exports. This is precisely the positioning of apilease: a platform where the scoring module, NPL classification, provision calculation and collections tracking are natively connected to the contract portfolio. Risk teams have a consolidated, real-time view; classification decisions are traced; and regulatory reports are produced directly from the platform.

Native integration: scoring → contracts → classification → provisions → collections.
Consolidated real-time view of the risk portfolio.
Complete audit trail for auditors and regulators.
IFRS 9 and regulatory reporting produced without manual extraction.