Digital Lending Strategy Training Course

Participants can map a borrower journey and identify automation, handoff and control points.

• Service blueprints mapping borrower actions, frontstage steps and backstage controls
• Loan journey stages from application intake to closure or recovery
• Straight-through processing criteria for low-risk, complete applications with verification
• Exception queues designed for referrals, missing data and failed checks
• Customer consent captured before data retrieval and automated decisioning
• Disclosure timing tested for APR, fees, repayment and consequences

Participants can define a digital loan product with transparent pricing and suitable repayment features.

• Loan purposes matched to borrower cash-flow patterns and repayment capacity
• APR and effective-interest calculations for instalment and revolving credit
• Pricing matrices combining risk grade, tenor, amount and acquisition cost
• Repayment schedules designed for monthly, weekly and seasonal income
• Early settlement, overpayment and restructuring terms specified clearly
• Product guardrails set for limits, repeat borrowing and concentration

Participants can specify the core platform capabilities and integrations required for digital origination.

• Loan origination system functions from application capture through booking
• API contracts defining requests, responses, authentication and failure handling
• Integration patterns for core banking, payments, CRM and document storage
• Event-driven architecture supporting decision updates and servicing notifications
• Data lineage documented from source capture to booked loan records
• Build-versus-buy assessment using capability, cost, control and delivery criteria

Participants can design identity and fraud controls proportionate to a digital lending journey.

• Customer due diligence requirements mapped to onboarding risk levels
• Identity verification using document checks, biometrics and liveness detection
• Device fingerprinting signals assessed for application and account-takeover fraud
• Fraud rules distinguish synthetic identity, impersonation and mule activity
• Sanctions and politically exposed person screening embedded in workflows
• Step-up verification triggered by anomalies, risk scores and transaction patterns

Participants can translate lending policy into consistent eligibility, affordability and referral rules.

• Credit policy matrix covering eligibility, affordability, limits and exclusions
• Affordability assessments using verified income, expenses and existing obligations
• Debt-service ratios calibrated by product, segment and repayment frequency
• Policy rules distinguish automatic decline, approval and manual referral
• Override authority documented with rationale, limits and second-line review
• Risk appetite statements converted into measurable underwriting thresholds

Participants can evaluate scorecard use, data suitability and model-risk controls for lending decisions.

• Application scorecards estimate default risk using borrower characteristic variables
• Behavioural scorecards update risk assessments from repayment and usage data
• Alternative data assessed for predictiveness, fairness, consent and stability
• Champion-challenger testing compares model performance before deployment decisions
• Model validation reviews discrimination, calibration, drift and documentation
• NIST AI RMF applied to automated credit-decisioning risk controls

Participants can configure a decisioning approach that combines rules, scores and controlled exceptions.

• Decision-engine sequences combine identity, fraud, policy and score outputs
• Rule precedence prevents conflicting decisions and uncontrolled policy exceptions
• Reason codes generated for declines, referrals and adverse actions
• Manual-underwriting workbenches present evidence, rationale and approval authority
• Business process management notation documents decision paths and ownership
• Service-level targets set for automated approvals and referred applications

Participants can connect lending growth plans to funding, revenue recognition and expected credit loss implications.

• Funding sources compared by tenor, cost, concentration and liquidity risk
• Net interest margin decomposed into yield, funding cost and credit losses
• IFRS 9 staging links credit deterioration to expected credit loss
• Effective interest rate treatment applied to fees and transaction costs
• Vintage analysis separates acquisition quality from portfolio seasoning effects
• Growth forecasts reconciled with capital, liquidity and provisioning requirements

Participants can design servicing and collections processes that support repayment and fair customer treatment.

• Self-service portals support balances, repayment changes and hardship requests
• Payment reminders sequenced by due date, channel preference and risk
• Collections strategies segment accounts by delinquency stage and capacity
• Promise-to-pay arrangements recorded with monitoring and follow-up actions
• Forbearance decisions assessed through affordability evidence and documented criteria
• Recovery partners governed through customer treatment, data and performance controls

Participants can assess technology controls required to protect lending services and customer data.

• Reference architecture separates channels, decisioning, servicing and data layers
• Role-based access controls restrict borrower data and policy changes
• Encryption protects data in transit, at rest and backups
• PCI DSS scope assessed for card-payment collection arrangements
• ISO 27001 control domains applied to lending platform security
• Business continuity plans define recovery objectives for critical lending services

Participants can incorporate consumer-protection, privacy and governance requirements into lending design.

• Fair lending principles tested through eligibility, pricing and collections decisions
• Adverse-action notices explain material reasons for credit decisions
• Data minimisation limits collection to justified lending and control purposes
• Retention schedules distinguish application, customer, transaction and model records
• Complaint taxonomy identifies recurring journey failures and conduct indicators
• Three-lines model assigns ownership, oversight and independent assurance

Participants can monitor lending performance and run controlled tests of product and journey changes.

• Funnel metrics track application completion, verification and approval conversion
• Portfolio dashboards report delinquency, roll rates, losses and recoveries
• Cohort analysis compares borrower performance by origination month and channel
• A/B testing measures changes in disclosures, offers and application flows
• Key risk indicators set escalation thresholds for fraud, fairness and arrears
• Root-cause analysis connects outcome deterioration to policy or operational changes

Participants can evaluate lending technology suppliers using commercial, technical and control criteria.

• Request-for-proposal criteria cover functionality, integration, controls and support
• Supplier scorecards weight product fit, security, cost and delivery capacity
• Proof-of-concept scope tests priority journeys, rules and integrations
• Service-level agreements specify availability, response, recovery and support obligations
• Exit plans address data portability, transition support and operational continuity
• Third-party risk assessments review subcontractors, hosting and incident notification

Participants can plan a staged digital lending implementation with accountable governance and measurable releases.

• Target operating model defines teams, decisions, controls and handoffs
• Release roadmaps sequence minimum viable product, pilots and scaled deployment
• RACI matrices assign accountability for product, risk, technology and operations
• Implementation risks recorded with owners, triggers and mitigating actions
• User acceptance testing validates journeys, calculations and exception handling
• Launch readiness reviews confirm controls, training, communications and support

Participants can build, review and present a complete digital lending strategy pack for a defined lending proposition.

• Target segment, borrower need and product proposition defined clearly
• Service blueprint connects customer journey, operations and control points
• Credit policy matrix specifies eligibility, affordability, limits and referrals
• Decisioning design combines data sources, scorecards, rules and exceptions
• Platform architecture identifies integrations, security controls and supplier dependencies
• Portfolio dashboard establishes growth, risk, conduct and implementation measures