IIBF Emerging Technologies study plan

Map the digital transformation value chain across banks, NBFCs and FIs, distinguishing front-end channels, middle-office workflow and core system layers

Explain how UPI, AePS, BBPS, ONDC and account aggregator rails change distribution economics for a bank versus an NBFC

Compare digital-only neobank models with incumbent bank digital arms on cost-to-serve, customer acquisition and partnership structures

Draft a one-page glossary of 25 digital transformation terms used in Indian banking

Trace how digital transformation priorities in Domain 1 create compliance obligations under the DPDP Act and RBI digital lending directions

Identify which customer data flows in a digital onboarding journey are personal data under the DPDP Act and which are not

Summarise consent notice and purpose limitation requirements for a lending app in plain language

List the digital lending guideline checkpoints that apply to a co-lending partnership

Classify cyber threats facing Indian financial institutions by actor type, motive and typical attack surface

Explain the RBI cyber security framework expectations for governance, incident reporting and board oversight

Differentiate information security risk from IT risk and operational risk with two banking examples each

Describe the anatomy of a phishing-led account takeover and where controls break

Explain zero trust, segmentation and privileged access management using a bank data centre scenario

Outline how third-party and cloud vendor risk is assessed and monitored under outsourcing expectations

Describe incident response phases from detection to regulatory reporting and customer communication

Connect digital channel expansion to the widening attack surface in Domain 1

Distinguish AI, machine learning, deep learning and rules-based analytics with a banking use case for each

Explain model development lifecycle stages from problem framing to monitoring and retraining

Interpret accuracy, precision, recall, ROC-AUC and confusion matrix outputs for a credit scoring model

Identify model risk concerns including bias, overfitting and explainability in lending decisions

Explain how the RBI guidance on digital lending algorithms and model governance affects AI-driven underwriting

Describe data minimisation and purpose limitation implications for analytics and model training

Outline how business analytics supports fraud detection, collections and customer segmentation in banking

Explain the role of explainability and adverse action reasoning when a model rejects a borrower

Summarise the Digital Personal Data Protection Act structure: obligations of data fiduciary, rights of data principal, consent manager and penalties

Explain the RBI digital lending directions on direct disbursal, first loss default guarantee, lending service provider conduct and cooling-off period

Describe data localisation, storage limitation and breach notification expectations for a lender

Distinguish the roles of data fiduciary, data processor and consent manager in a lending ecosystem

Synthesise cross-domain linkages such as AI underwriting, data protection and cyber resilience in one narrative

Answer 60 timed mixed-domain practice questions and review every error by domain

Revisit weak areas using the glossary, threat taxonomy and compliance checklist built in earlier weeks

Draft a one-page exam-day summary sheet of frameworks, regulators and key definitions