Digital Banking and Fintech Innovation Training Course

Participants can define a priority customer problem and create a testable digital value proposition.

• Customer journey mapping from trigger through servicing, retention and complaint handling
• Jobs to Be Done interviews identifying functional, emotional and social needs
• Service blueprints connecting frontstage journeys with back-office fulfilment activities
• Value Proposition Canvas linking customer pains, gains and product features
• Segment hypotheses tested using behavioural, financial and channel data
• Customer vulnerability and accessibility requirements incorporated into proposition design

Participants can evaluate fintech revenue models, cost drivers and routes to sustainable economics.

• Revenue models spanning interchange, subscriptions, lending spreads and transaction fees
• Customer acquisition cost calculated against lifetime value and retention assumptions
• Unit economics modelled for digital accounts, wallets and merchant acquiring
• Marketplace models assessed for network effects, liquidity and take rates
• Freemium conversion assumptions tested through cohort analysis and funnel metrics
• Build-buy-partner decisions structured around capability, cost, speed and control

Participants can convert a banking opportunity into prioritised product requirements and measurable hypotheses.

• Product discovery framing problem statements, target segments and desired outcomes
• Opportunity solution trees connecting customer needs to testable solution options
• User stories written with acceptance criteria and regulatory control requirements
• MoSCoW prioritisation separating essential launch capabilities from later enhancements
• RICE scoring compares reach, impact, confidence and implementation effort
• North Star metrics linked to customer outcomes and commercial performance

Participants can design a digital onboarding flow that balances conversion, identity assurance and regulatory obligations.

• Digital onboarding journeys mapped from application through account activation
• Know Your Customer controls aligned with customer risk classification
• Identity proofing methods compare document checks, biometrics and database verification
• Liveness detection limitations assessed against presentation attack and fraud risks
• Consent capture records permissions, disclosures, timestamps and customer evidence
• Abandonment analysis identifies friction points within application and verification stages

Participants can assess payment propositions using payment flows, economics, settlement and control requirements.

• Card payment flows trace authorisation, clearing, settlement and chargeback stages
• Account-to-account payments compared with cards for cost, speed and data
• ISO 20022 message concepts support richer payment data and interoperability
• Real-time payment schemes assessed for liquidity, fraud and exception handling
• Merchant acquiring economics cover merchant discount rates, interchange and scheme fees
• Payment orchestration routes transactions using cost, approval and resilience rules

Participants can specify an API-enabled financial service and assess its commercial and control implications.

• Open banking models distinguish account information, payment initiation and data sharing
• API product design defines consumers, endpoints, service levels and monetisation
• OAuth 2.0 consent flows manage delegated customer authorisation securely
• API gateway controls enforce authentication, throttling, monitoring and version management
• Embedded finance propositions assess distribution partners, economics and accountability
• Developer portals support sandbox testing, documentation and partner onboarding

Participants can evaluate digital lending journeys, credit decision methods and responsible lending safeguards.

• Digital lending funnels measure application completion, approval and disbursement conversion
• Credit scoring combines bureau records, affordability data and behavioural signals
• Alternative data governance tests relevance, bias, explainability and customer consent
• Affordability assessment distinguishes verified income, expenses and disposable income
• Expected credit loss concepts connect underwriting quality with portfolio provisioning
• Responsible lending controls address unsuitable credit, hardship and collections treatment

Participants can define data use cases, governance requirements and performance measures for digital banking analytics.

• Customer data models integrate transaction, interaction, product and consent records
• Data lineage documents source systems, transformations, ownership and quality controls
• Customer 360 views support service, marketing and risk decisioning
• Segmentation models distinguish behavioural clusters, value tiers and lifecycle stages
• Propensity models target next-best-action offers using validated performance metrics
• Data minimisation limits collection and retention to defined processing purposes

Participants can assess artificial intelligence use cases with appropriate model governance and human oversight.

• AI use cases prioritised across service, fraud, credit and operations
• Supervised learning models evaluated using precision, recall and false-positive rates
• Generative AI controls address hallucination, confidentiality and prompt injection
• Model risk management defines validation, approval, monitoring and change control
• Explainability methods support adverse action reasons and stakeholder challenge
• Human-in-the-loop decisions set escalation thresholds and accountability boundaries

Participants can assess technology architecture choices and delivery dependencies for a digital banking initiative.

• Core banking constraints identified through product, data and processing dependencies
• Cloud service models compare IaaS, PaaS and SaaS responsibilities
• Microservices architecture separates bounded services, interfaces and deployment cycles
• Event-driven architecture supports real-time notifications, data propagation and audit trails
• DevSecOps pipelines embed code scanning, testing and release approvals
• Service level objectives define availability, latency, recovery and error budgets

Participants can identify major digital banking threats and select proportionate preventive, detective and recovery controls.

• NIST Cybersecurity Framework functions organise governance, protection, detection and recovery
• Threat modelling identifies assets, attack paths, controls and residual risks
• Account takeover controls combine device intelligence, authentication and behavioural monitoring
• Fraud rules use velocity checks, anomaly detection and case management
• PCI DSS requirements protect cardholder data within payment environments
• Operational resilience scenarios test important business services and recovery tolerances

Participants can incorporate financial crime and conduct controls into digital product and process design.

• Anti-money laundering controls connect onboarding, monitoring, investigation and reporting
• Sanctions screening addresses customer, payment and beneficial ownership data
• Transaction monitoring scenarios identify unusual behaviour and alert thresholds
• Conduct risk reviews assess disclosures, fees, incentives and vulnerable customers
• Complaint taxonomy identifies recurring harm, root causes and remediation actions
• Fair treatment controls test outcomes across segments, channels and product features

Participants can evaluate a fintech partner and establish governance for outsourced or shared service delivery.

• Partner selection criteria assess strategic fit, capability and financial viability
• Due diligence reviews ownership, funding, security, compliance and operational capacity
• Third-party risk assessments identify concentration, substitutability and exit dependencies
• Service agreements define scope, performance measures, audit rights and liabilities
• Data processing clauses allocate responsibilities for privacy and breach notification
• Exit plans cover data return, service transition and customer communication

Participants can define governance, roles and delivery controls for implementing a digital banking initiative.

• Target operating models align capabilities, processes, roles, technology and controls
• Product operating models clarify accountability among product, engineering and risk teams
• Agile delivery cadences use backlogs, sprint reviews and retrospective actions
• Three lines model assigns ownership, oversight and independent assurance responsibilities
• Change impact assessments identify process, role, policy and training requirements
• Implementation roadmaps sequence dependencies, releases, approvals and adoption measures

Participants can prepare an investment case and measurement framework for a digital banking initiative.

• Business cases quantify revenue, cost, investment, risk and sensitivity assumptions
• Net present value compares discounted benefits against implementation expenditure
• Benefits realisation plans assign owners, baselines, targets and reporting frequency
• Balanced scorecards combine customer, financial, operational and risk measures
• Funnel dashboards track acquisition, activation, engagement and retention performance
• Post-implementation reviews compare realised outcomes with approved investment assumptions

Participants can build, review and present a complete digital banking innovation dossier for a defined business opportunity.

• Customer problem evidence establishes segment, journey friction and priority need
• Digital proposition specifies features, value proposition and target operating model
• Service blueprint maps channels, APIs, operations, data and control points
• Commercial case presents unit economics, investment assumptions and benefit measures
• Risk assessment addresses cybersecurity, fraud, conduct, third-party and resilience exposures
• Implementation roadmap sequences releases, governance decisions, owners and dependencies