Digital Lending Risk Management Training Course

Participants can identify and assess risk exposures throughout the digital loan lifecycle.

• Loan lifecycle mapping from acquisition through closure and data retention
• Product terms assessed for affordability, transparency, fees, and customer outcomes
• Embedded lending arrangements analysed for partner roles and control dependencies
• Digital customer journeys mapped for abandonment, error, and conduct risk
• Risk and control self-assessments structured around lifecycle failure points
• Key risk indicators selected for origination, servicing, collections, and complaints

Participants can build credit policy structures that align customer segments with controlled lending decisions.

• Borrower segments defined using needs, repayment capacity, and behavioural characteristics
• Credit policy rules covering eligibility, affordability, exposure, and loan purpose
• Debt-service ratio calculations using verified income and existing obligations
• Policy matrices distinguish automated approvals, referrals, declines, and exceptions
• Risk-based pricing linked to expected loss, operating cost, and margin
• Exception registers capture overrides, rationale, approvers, and subsequent performance

Participants can evaluate data suitability, quality and governance for digital credit decisions.

• Data lineage documented from collection channels through decision-engine outputs
• Completeness, accuracy, timeliness, validity, and uniqueness tested through data controls
• Alternative data assessed for predictive value, consent, relevance, and stability
• Data dictionaries define fields, sources, transformations, and accountable data owners
• Missing-data treatments tested for approval bias and scorecard performance effects
• Data-quality thresholds trigger remediation, model restrictions, and management escalation

Participants can assess automated approval processes and design effective underwriting controls.

• Decision-engine architecture linking input data, rules, scores, and outcomes
• Hard-cut rules applied for identity, affordability, exposure, and prohibited lending
• Scorecard cut-offs calibrated against approval rates, losses, and risk appetite
• Manual referral queues designed for incomplete, unusual, and borderline applications
• Decision reason codes generated for declines, referrals, and pricing outcomes
• Approval-rule changes governed through testing, approval, deployment, and rollback procedures

Participants can interpret scorecard construction, validation results and model performance measures.

• Logistic regression scorecards built from selected borrower and behavioural variables
• Weight-of-evidence transformation used for grouped predictor characteristics and stability
• Discrimination measured through Gini coefficient, ROC curve, and KS statistic
• Calibration tests compare predicted default rates with observed borrower outcomes
• Population stability index detects shifts in applicant and portfolio characteristics
• Champion-challenger testing compares models before production scorecard replacement

Participants can identify unfair lending outcomes and strengthen explainability and conduct controls.

• Fair lending risk assessed through approval, pricing, and servicing outcome analysis
• Disparate impact testing compares outcomes across relevant customer population groups
• Explainable decisioning documents principal factors affecting automated credit outcomes
• Consent records evidence permitted collection and use of customer information
• Plain-language disclosures explain pricing, repayment schedules, fees, and consequences
• Vulnerable customer indicators trigger enhanced support, forbearance, and referral pathways

Participants can design layered controls against identity, application and synthetic identity fraud.

• Know-your-customer controls verify identity, address, document validity, and liveness
• Synthetic identity indicators combine inconsistent attributes, velocity, and bureau anomalies
• Device fingerprinting detects repeated applications, emulator use, and suspicious patterns
• Application fraud rules link identity, device, bank account, and contact attributes
• Fraud loss measurement separates prevented attempts, confirmed cases, and recoveries
• Investigation case management preserves evidence, decisions, and suspicious activity rationale

Participants can monitor and respond to fraud risks after loan account opening.

• Account takeover scenarios mapped across login, profile change, and disbursement stages
• Behavioural biometrics identify anomalous interaction patterns and credential misuse
• Step-up authentication triggered by high-risk events and transaction thresholds
• Bank-account verification confirms payee ownership before loan disbursement
• Fraud monitoring rules detect rapid drawdowns, mule accounts, and payment diversion
• Incident response playbooks define containment, investigation, customer contact, and recovery

Participants can create portfolio monitoring routines that identify emerging credit deterioration.

• Vintage analysis compares delinquency and loss performance by origination cohort
• Roll-rate matrices measure borrower migration between delinquency status buckets
• First-payment default monitored as an early signal of underwriting weakness
• Cure rates distinguish temporary arrears from sustained repayment deterioration
• Concentration limits cover geography, channel, product, employer, and borrower segment
• Early-warning dashboards combine delinquency, utilisation, complaints, and fraud indicators

Participants can apply IFRS 9 expected credit loss concepts to digital lending portfolios.

• IFRS 9 staging distinguishes performing, underperforming, and credit-impaired exposures
• Probability of default estimates align with portfolio segment and forecast horizon
• Loss given default reflects recoveries, collection costs, collateral, and timing
• Exposure at default incorporates balances, drawdowns, limits, and repayment profiles
• Forward-looking scenarios weight macroeconomic assumptions and borrower sensitivity estimates
• Expected credit loss reconciliations explain movements in provisions and portfolio quality

Participants can design collections strategies that improve recoveries while protecting customers.

• Collections segmentation prioritises cases by delinquency, balance, vulnerability, and contactability
• Treatment strategies sequence reminders, calls, payment plans, and external escalation
• Contact strategies control frequency, timing, channels, and consent-based preferences
• Promise-to-pay tracking measures kept arrangements, broken commitments, and cure outcomes
• Forbearance assessments distinguish temporary hardship from unsustainable repayment capacity
• Recovery curves measure cash collected against delinquent balances and collection cost

Participants can establish governance and validation requirements for lending decision models.

• Model inventories classify underwriting, pricing, fraud, collections, and provisioning models
• Model tiering reflects materiality, complexity, customer impact, and decision reliance
• Independent validation tests conceptual soundness, implementation, outcomes, and limitations
• Back-testing compares predicted defaults, losses, and approvals against realised results
• Override analysis identifies human intervention patterns and potential model weaknesses
• Model change logs record approvals, versions, performance evidence, and implementation dates

Participants can quantify portfolio vulnerability under adverse economic and operational scenarios.

• Stress-testing objectives linked to capital, provisioning, liquidity, and risk appetite
• Macroeconomic scenarios translate unemployment, inflation, and rates into borrower stress
• Sensitivity analysis tests changes in default, recovery, and prepayment assumptions
• Reverse stress testing identifies conditions that breach defined portfolio risk limits
• Management actions specify underwriting restrictions, pricing changes, and funding responses
• Scenario results communicated through assumptions, impacts, limitations, and decision recommendations

Participants can assess how funding structures and capital constraints affect digital lending growth.

• Funding sources assessed for tenor, pricing, concentration, covenants, and withdrawal risk
• Cash-flow forecasts combine disbursements, repayments, operating expenses, and debt service
• Liquidity stress scenarios model funding disruption and delayed borrower collections
• Warehouse facility covenants monitored against collateral, delinquency, and concentration metrics
• Capital planning links growth forecasts, losses, provisions, and regulatory requirements
• Risk-adjusted return measures compare product profitability with expected credit losses

Participants can identify operational failures and build resilience controls for digital lending services.

• Process failure modes assessed through failure mode and effects analysis
• Operational loss events classified by cause, impact, recovery, and control failure
• Business continuity plans prioritise loan servicing, payments, collections, and customer support
• Service-level indicators monitor outages, processing backlogs, errors, and recovery times
• Segregation of duties controls configuration changes, approvals, disbursements, and refunds
• Control testing samples evidence execution, exceptions, remediation, and repeat failures

Participants can assess cybersecurity risks affecting digital lending systems and customer data.

• NIST Cybersecurity Framework 2.0 functions applied to lending technology controls
• Threat modelling identifies attack paths against applications, APIs, and customer accounts
• Secure software development controls cover code review, testing, and release approvals
• API security checks address authentication, authorisation, rate limits, and logging
• Vulnerability management prioritises remediation by exploitability, exposure, and business impact
• Cyber incident playbooks coordinate detection, containment, notification, and recovery actions

Participants can manage risk arising from technology, data, collection and distribution partners.

• Third-party inventories classify critical providers, services, data access, and dependencies
• Due diligence assesses financial strength, security controls, resilience, and subcontractors
• Contract clauses define service levels, audit rights, incident notification, and exit support
• Cloud shared-responsibility matrices assign security obligations between provider and lender
• Vendor performance reviews track breaches, incidents, remediation, and concentration exposure
• Exit plans cover data migration, continuity arrangements, access removal, and customer communication

Participants can integrate financial crime controls into digital lending onboarding and servicing processes.

• FATF risk-based approach applied to lender customer and product risk
• Customer risk ratings combine location, occupation, product, and behavioural indicators
• Sanctions screening controls cover applicants, beneficial owners, and payment counterparties
• Transaction monitoring identifies unusual disbursement, repayment, and refund activity
• Suspicious activity escalation records facts, analysis, decisions, and filing rationale
• Financial crime controls aligned with fraud investigations and customer remediation processes

Participants can prepare risk reporting and assurance evidence for management and oversight bodies.

• Management information packs combine risk appetite, portfolio, fraud, model, and conduct metrics
• Risk dashboards distinguish leading indicators, lagging indicators, thresholds, and trend commentary
• Issue registers track root causes, actions, owners, due dates, and validation
• Control assurance plans schedule self-assessment, testing, independent review, and audit
• Regulatory examination files organise policies, evidence, governance records, and remediation
• Board reports present material exposures, decisions required, and management actions

Participants can build, review and present a complete digital lending risk management pack for senior management.

• Risk appetite statement integrates growth objectives, limits, triggers, and escalation authorities
• Underwriting control design documents data checks, rules, referrals, and overrides
• Portfolio dashboard presents vintage, delinquency, loss, concentration, and fraud indicators
• Model governance plan specifies validation, monitoring, change control, and accountability
• Stress-test analysis quantifies adverse impacts and proposed management actions
• Senior-management presentation defends priorities, residual risks, decisions, and implementation milestones