Digital Lending Strategies Training Course

Participants can design a borrower journey that balances conversion, disclosure and control.

• Customer journey maps from acquisition through repayment and loan closure
• Loan product features: amount, term, repayment frequency, grace period and renewal
• Progressive disclosure of pricing, fees, penalties and repayment obligations
• Friction analysis identifies abandonment points within digital application journeys
• Consent screens distinguish marketing permissions, data access and credit checks
• Service blueprints connect customer actions, systems, teams and control points

Participants can translate business objectives into a governed lending risk appetite.

• Risk appetite statements set growth, loss, concentration and conduct boundaries
• Three lines model assigns ownership, oversight and independent assurance
• Board reporting metrics link lending growth to risk-adjusted performance
• Policy hierarchy separates strategy, credit policy, procedures and work instructions
• Delegated authorities define approval limits, overrides and escalation routes
• Governance calendars schedule portfolio reviews, model reviews and policy refreshes

Participants can draft credit policy rules for automated and manual lending decisions.

• Credit policy defines eligible customers, products, limits and prohibited purposes
• Affordability assessments compare verified income, obligations and repayment capacity
• Debt-to-income and payment-to-income ratios set underwriting guardrails
• Decision trees sequence knockouts, score thresholds, referrals and approvals
• Exception policies specify evidence, authority levels and post-approval monitoring
• Policy test cases expose conflicting rules and unintended customer outcomes

Participants can assess lending data sources for usefulness, quality and customer consent.

• Credit bureau files interpreted for inquiries, tradelines, arrears and public records
• Transactional data features capture income regularity, balances and spending patterns
• Alternative data assessed for predictive value, bias and collection permissions
• Data dictionaries define fields, ownership, lineage and permitted uses
• Missing-data treatment distinguishes unavailable, unknown, zero and invalid values
• Data quality controls test completeness, timeliness, accuracy and consistency

Participants can specify a scorecard approach and interpret its decision performance.

• Application scorecards estimate probability of default at loan origination
• Behavioural scorecards support limit management, renewal and collections treatment
• Logistic regression outputs translated into score bands and cut-off points
• Gini coefficient and ROC curve measure discriminatory model performance
• Champion-challenger testing compares incumbent and proposed decision models
• Reason codes explain adverse decisions using scorecard input variables

Participants can calculate product-level lending economics under alternative risk assumptions.

• Risk-based pricing links expected loss, funding cost and operating expense
• Annual percentage rate calculations incorporate interest, fees and payment schedules
• Expected loss combines probability of default, exposure and loss severity
• Contribution margin separates acquisition, servicing, funding and credit-loss costs
• Customer acquisition cost compared with expected lifetime contribution margin
• Sensitivity analysis tests profitability against default, conversion and funding changes

Participants can design layered fraud controls for a digital loan application process.

• Know-your-customer checks verify identity, address, ownership and customer risk
• Document verification detects altered identity documents and image manipulation
• Device fingerprinting identifies repeated applications and suspicious device behaviour
• Fraud rules detect synthetic identities, mule accounts and application anomalies
• Sanctions screening and politically exposed person checks support financial-crime controls
• Case-management workflows record alerts, investigations, decisions and evidence

Participants can define the core technology components and vendor controls for digital lending.

• Loan origination systems coordinate applications, decisions, documents and approvals
• Application programming interfaces connect identity, bureau, payment and messaging services
• Rules engines separate configurable credit policy from application code
• Build-buy-partner analysis compares cost, speed, control and maintenance requirements
• Vendor due diligence assesses security, resilience, data handling and subcontracting
• Service-level agreements specify availability, incident response and reporting obligations

Participants can improve loan communications while protecting informed customer choice.

• Plain-language disclosures explain total repayment, dates, fees and consequences
• Informed consent design records affirmative customer choices and disclosures
• Dark-pattern reviews identify coercive, misleading or obstructive interface features
• Accessibility checks address readable content, contrast, language and device limitations
• Complaint taxonomies classify root causes, severity, ownership and resolution timing
• Fair-treatment indicators monitor declined applicants, vulnerable customers and complaints

Participants can design controlled origination workflows with appropriate automation and review.

• Straight-through processing automates low-risk applications through predefined decision rules
• Referral queues route exceptions by risk type, complexity and authority
• Operational service levels measure decision speed, backlog and rework
• Quality assurance sampling tests evidence, policy adherence and decision consistency
• Document management retains applications, disclosures, consents and decision records
• Control reconciliations compare approved loans, disbursements and core-system records

Participants can specify reliable servicing processes from disbursement through account closure.

• Disbursement controls verify beneficiary accounts, limits and final approval status
• Repayment methods compare direct debit, payroll deduction, wallet and card payments
• Payment allocation rules apply receipts to fees, interest and principal
• Failed-payment workflows trigger reminders, retries, promises and escalation actions
• Account servicing handles statements, payoff requests, disputes and restructures
• Reconciliations investigate differences between payment processors and loan ledgers

Participants can design collections treatments that improve recovery and customer outcomes.

• Delinquency buckets classify accounts by days past due and risk stage
• Collections segmentation prioritises treatment using balance, risk and contactability
• Contact strategies set channel, timing, frequency and communication content
• Promise-to-pay tracking measures kept promises, broken promises and cure rates
• Hardship assessments identify sustainable repayment arrangements and support needs
• Collections quality reviews test conduct, documentation and prohibited practices

Participants can build a portfolio-monitoring framework for timely management action.

• Vintage analysis compares delinquency performance by origination month and cohort
• Roll-rate matrices track account migration between delinquency buckets
• First-payment default highlights early underwriting or fraud weaknesses
• Cohort analysis separates new-customer, repeat-customer and product performance
• Concentration reports identify exposures by geography, channel, employer and segment
• Early-warning thresholds trigger investigation, policy adjustment and management escalation

Participants can relate digital lending portfolio performance to expected credit loss reporting.

• IFRS 9 staging distinguishes performing, underperforming and credit-impaired exposures
• Twelve-month expected credit loss applies to Stage 1 exposures
• Lifetime expected credit loss applies after significant credit-risk increase
• Macroeconomic scenarios adjust default assumptions through probability-weighted outcomes
• Write-off policies distinguish accounting derecognition from collections activity
• Management overlays document model limitations, emerging risks and governance approval

Participants can establish controls for reliable, explainable and fair credit decision models.

• Model inventory records purpose, owner, version, inputs and materiality
• Fairness testing compares approval rates and outcomes across relevant groups
• Feature reviews identify proxy variables, prohibited attributes and unstable predictors
• Explainability methods translate model outputs into actionable customer reason codes
• Population stability index detects input-distribution shifts after model deployment
• Independent validation challenges data, methodology, assumptions and performance evidence

Participants can identify critical security, privacy and continuity controls for lending platforms.

• NIST Cybersecurity Framework maps lending-platform protection, detection and recovery controls
• Access management applies least privilege, segregation of duties and periodic reviews
• Encryption requirements protect customer data in transit and at rest
• Privacy impact assessments identify purpose limitation, retention and sharing risks
• Incident response playbooks define containment, notification, investigation and recovery actions
• Business continuity plans maintain critical lending and servicing processes

Participants can assess scalable growth options without exceeding risk and operational capacity.

• Acquisition funnel metrics measure impressions, applications, approvals and funded loans
• Channel attribution separates partner, paid-media, referral and organic performance
• Credit capacity planning links growth targets to capital, funding and operations
• Embedded lending partnerships allocate customer ownership, risk and service responsibilities
• Revenue-share agreements specify economics, data rights and performance obligations
• Partner monitoring tracks conversion, fraud, complaints and portfolio-quality trends

Participants can prepare a sequenced implementation plan for a digital lending initiative.

• Target operating model defines processes, roles, systems and governance forums
• Gap assessment compares current capabilities against required lending operating model
• Prioritisation matrices rank initiatives by value, risk, effort and dependency
• Implementation roadmaps sequence policy, data, technology, operations and training workstreams
• RACI matrices assign accountable, responsible, consulted and informed stakeholders
• Key risk indicators track delivery threats, readiness and control effectiveness

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

• Market proposition defines target customers, product terms and acquisition channels
• Credit policy sets eligibility, affordability, score thresholds and exception controls
• Unit economics model quantifies pricing, funding, servicing and expected-loss assumptions
• Operating model assigns origination, servicing, collections and oversight responsibilities
• Risk dashboard specifies portfolio metrics, thresholds, owners and escalation actions
• Implementation roadmap sequences technology, governance, partner and change-management priorities