Digital Lending Training Course

Participants can define product features, eligibility rules and customer-value propositions for a digital loan.

• Product-market fit assessed using borrower segments, needs and repayment capacity
• Loan features specify amount, tenor, pricing, fees, collateral and repayment frequency
• Eligibility criteria balance inclusion objectives, loss tolerance and operational capacity
• Customer-value propositions distinguish speed, convenience, transparency and appropriate credit
• Product requirements document captures business rules, controls and acceptance criteria
• Pricing disclosures tested against total cost, repayment schedule and customer comprehension

Participants can design controlled acquisition and onboarding flows for digital borrowers.

• Acquisition funnels measure impressions, applications, approvals, disbursements and conversion cost
• Consent records document data purpose, retention period and third-party sharing
• Digital application forms minimise friction while preserving decision-useful information
• Progressive disclosure presents terms, fees, risks and borrower obligations clearly
• Abandonment analysis identifies failed steps, unclear fields and channel-specific barriers
• Marketing controls prevent misleading claims, unsuitable targeting and prohibited inducements

Participants can specify proportionate identity verification and customer-due-diligence controls.

• e-KYC workflows combine document capture, liveness checks and database verification
• Customer due diligence applies risk-based identity, ownership and source-of-funds checks
• Biometric matching thresholds balance false acceptance, false rejection and accessibility
• Identity assurance levels align onboarding controls with product and transaction risk
• Sanctions and politically exposed person screening integrated into onboarding decisions
• Exception queues route ambiguous identity results for documented human review

Participants can formulate digital credit policy that supports responsible and consistent lending decisions.

• Credit policy defines target segments, prohibited lending and delegated authorities
• Affordability assessments combine verified income, expenses, obligations and repayment stress
• Debt-service ratio limits calibrated against product tenor and borrower volatility
• Responsible-lending controls address over-indebtedness, refinancing and repeat borrowing patterns
• Policy exceptions require approval rationale, evidence and post-decision monitoring
• Vulnerable-customer indicators trigger enhanced communications and suitable support routes

Participants can evaluate data sources for credit relevance, legality, reliability and fairness.

• Credit bureau files assessed for coverage, timeliness, disputes and predictive value
• Open-banking transaction data categorised into income, expenses, balances and obligations
• Alternative data evaluated for consent, correlation, stability and disparate impact
• Data lineage records source, transformation, ownership, quality checks and permitted use
• Missing-data treatment distinguishes unavailable information from adverse borrower behaviour
• Feature selection excludes proxies for protected characteristics and unjustified variables

Participants can define automated decision rules, decision paths and human-intervention points.

• Decision engines sequence eligibility checks, fraud rules, affordability and scorecards
• Rule-based underwriting uses threshold logic, decline codes and referral conditions
• Decision matrices document inputs, rules, outputs, owners and override permissions
• Straight-through processing targets suitable applications without removing accountable oversight
• Manual-review queues prioritise cases by risk, value and customer vulnerability
• Adverse-action reasons translate decision logic into understandable borrower communications

Participants can assess credit-scorecard development, validation and governance requirements.

• Application scorecards estimate default probability from borrower and transaction characteristics
• Behavioural scorecards identify deteriorating repayment patterns after loan disbursement
• Gini coefficient and KS statistic measure discriminatory power during validation
• Population stability index detects scorecard drift across borrower populations
• Champion-challenger testing compares models against approval, loss and fairness outcomes
• Model governance assigns validation, approval, monitoring and retirement responsibilities

Participants can calculate pricing and profitability assumptions for a digital loan portfolio.

• Risk-based pricing combines cost of funds, expected loss, capital and operating cost
• Annual percentage rate calculations disclose interest, fees and compulsory charges
• Contribution margin separates revenue from acquisition, servicing, fraud and collection costs
• Expected loss estimates probability of default, exposure and loss severity
• Cohort analysis compares profitability by origination month, channel and borrower segment
• Sensitivity analysis tests profitability against defaults, funding rates and approval changes

Participants can connect digitally originated loan performance with impairment and portfolio-monitoring processes.

• IFRS 9 staging distinguishes performing, underperforming and credit-impaired exposures
• Expected credit loss incorporates default probability, loss severity and exposure
• Vintage curves track delinquency development from each origination cohort
• Roll-rate analysis measures borrower migration between delinquency buckets over time
• Early-warning indicators combine missed payments, utilisation, contactability and behavioural changes
• Portfolio concentration limits address channels, regions, sectors and connected counterparties

Participants can specify reliable disbursement, repayment and servicing processes for digital loans.

• Disbursement controls verify account ownership, beneficiary details and final approval status
• Payment rails compared across bank transfer, card, wallet and mobile-money options
• Repayment schedules calculate instalments, allocation order, grace periods and late charges
• Automated reconciliation matches lender records, payment-provider files and borrower balances
• Servicing workflows manage statements, due-date changes, complaints and hardship requests
• Service-level metrics track payment success, reconciliation breaks and resolution times

Participants can design data-led collections strategies that preserve fair customer treatment.

• Collections segmentation prioritises accounts by delinquency stage, balance and contactability
• Contact strategies define channel, frequency, timing, scripts and escalation boundaries
• Promise-to-pay tracking separates commitment dates, payment amounts and broken arrangements
• Hardship assessment identifies temporary distress, sustainable repayment and support options
• Collections analytics measure cure rates, roll rates and treatment effectiveness
• Conduct controls prohibit harassment, unauthorised disclosure and misleading settlement communications

Participants can design layered fraud and financial-crime controls for digital lending.

• Fraud typologies cover synthetic identity, account takeover, collusion and first-party fraud
• Device fingerprinting detects repeated applications, emulator use and suspicious device changes
• Velocity rules flag rapid applications, linked identities and unusual transaction patterns
• Fraud-score thresholds route applications to approve, decline or investigation decisions
• Transaction monitoring identifies suspicious disbursements, repayments and mule-account activity
• Case-management evidence supports investigation, reporting, recovery and rule refinement

Participants can set data-protection and cybersecurity requirements for a digital lending service.

• ISO/IEC 27001 controls structure information-security policies, responsibilities and evidence
• NIST Cybersecurity Framework maps identify, protect, detect, respond and recover activities
• Data classification distinguishes public, internal, confidential and restricted borrower information
• Encryption requirements cover stored records, transmitted data and key-management practices
• Privacy impact assessments document purpose, minimisation, risks and mitigating controls
• Incident-response playbooks assign containment, notification, investigation and recovery responsibilities

Participants can assess lending-platform architecture and third-party integration requirements.

• Lending-platform components separate customer interface, decision engine, ledger and workflow
• API contracts define endpoints, authentication, payloads, error handling and version control
• Microservices trade-offs assessed against operational complexity, resilience and release governance
• Cloud deployment choices address data residency, availability, monitoring and vendor concentration
• Third-party due diligence reviews security, financial strength, service levels and exit plans
• Integration testing validates data accuracy, exception handling and end-to-end processing

Participants can evaluate emerging technology use cases with appropriate risk and control criteria.

• Machine-learning models compare predictive performance, explainability, stability and governance requirements
• Bias testing assesses disparate outcomes across protected and vulnerable borrower groups
• Generative AI use cases include document extraction, customer support and analyst assistance
• Human-in-the-loop controls define review points for consequential automated decisions
• Distributed ledger applications assessed for identity, collateral records and settlement traceability
• Technology roadmaps prioritise use cases by value, readiness, risk and implementation effort

Participants can establish a compliance framework for digital lending products and automated decisions.

• Regulatory mapping identifies licensing, disclosure, consumer-protection and reporting obligations
• Conduct-risk assessments test product features, communications, fees and collections treatment
• Fair-lending reviews examine access, pricing, decision outcomes and complaint patterns
• Model documentation records purpose, inputs, assumptions, limitations and approval evidence
• Audit trails preserve application data, decision logic, overrides and customer communications
• Regulatory reporting calendars assign data owners, validation checks and submission controls

Participants can plan a controlled digital lending implementation from pilot through scaled operation.

• Target operating model defines roles, handoffs, controls and service-level measures
• Minimum viable product scope prioritises essential borrower, risk and servicing capabilities
• Pilot design sets borrower limits, approval criteria, monitoring metrics and exit triggers
• Implementation roadmaps sequence policy, technology, data, operations and training dependencies
• Change-impact assessments identify affected roles, procedures, controls and customer communications
• Post-launch reviews compare expected benefits, losses, incidents and customer outcomes

Participants can build, review and present a complete digital lending product, risk and implementation design.

• Target borrower segment defined using needs, affordability and channel-access evidence
• End-to-end borrower journey specifies onboarding, decisioning, servicing and collections steps
• Credit policy sets eligibility, affordability, scorecard, exceptions and pricing rules
• Control design addresses identity, fraud, privacy, cybersecurity and conduct risks
• Portfolio dashboard defines approval, loss, delinquency, profitability and fairness indicators
• Implementation plan presents architecture, partners, milestones, owners and pilot controls