RBI Annual Report FY 2025-26 explains RBI’s FinTech and AI initiatives, CBDC pilots, ULI, MuleHunter.ai, FREE-AI, CAT Sandbox, UMI and RBIH innovations.

The RBI Annual Report FY 2025-26 shows that the Reserve Bank’s innovation agenda has moved well beyond experimentation with digital payments. During the year, RBI expanded Central Bank Digital Currency (CBDC) use cases, introduced programmability at the user level, developed the Unified Markets Interface (UMI) for asset tokenisation, expanded the Unified Lending Interface (ULI), scaled MuleHunter.ai, continued the Regulatory Sandbox, and examined responsible adoption of Artificial Intelligence through the FREE-AI Committee framework. For RBI Grade B Promotion Exam, RBI Grade B, UPSC, IBPS and banking interviews, this is a high-value chapter because it combines technology, financial inclusion, regulation, cybersecurity, credit delivery and central-bank innovation.

1. The RBI’s FinTech Philosophy

The RBI’s FinTech Department has a dual responsibility:

Foster innovation while remaining vigilant about the risks associated with innovation.

This is the central theme.

Innovation by itself is not the RBI’s objective.

The regulatory objective is to create an ecosystem where technology can improve:

financial inclusion;
credit delivery;
payment efficiency;
consumer experience;
fraud detection; and
financial-market infrastructure,

without compromising:

financial stability;
cybersecurity;
consumer protection;
privacy and data governance; and
institutional accountability.

The Annual Report explicitly describes the FinTech Department as responsible for fostering innovation in the FinTech ecosystem while addressing associated risks.

2. Four Major FinTech Priorities for FY 2025-26

The Department’s agenda for 2025-26 revolved around four major objectives:

Expand CBDC and introduce new use cases/features.
Introduce B2C functionality in ULI.
Scale up MuleHunter.ai.
Prepare a framework for responsible and ethical adoption of AI in the financial sector.

These four objectives provide an excellent framework for exam revision.

3. CBDC: From Pilot to Practical Use Cases

CBDC, or central bank digital currency, was an important part of RBI’s innovation agenda during 2025-26.

The RBI did not limit itself to simply testing a digital form of currency.

It expanded the use cases and functionality of CBDC.

Three areas are particularly important:

A. Programmability

B. Offline CBDC

C. Wholesale CBDC and financial-market settlement

4. User-Level Programmability in CBDC

One of the important developments was the introduction of user-level programmability in the retail CBDC segment.

This enabled individual users to send programmable money to other individuals.

The significance is straightforward:

Traditional digital money generally facilitates transfer.

Programmable money can potentially incorporate conditions concerning:

purpose;
beneficiary;
timing; or
permitted use.

This creates possibilities for targeted government transfers and other specialised use cases.

5. CBDC and Direct Benefit Transfers

Government agencies began pilots under various Direct Benefit Transfer (DBT) schemes using CBDC programmability.

The objective was to ensure productive utilisation of public funds.

The Annual Report also notes that multiple government agencies used the programmability feature in DBT pilots.

This is an important conceptual connection:

CBDC + programmability → targeted transfer → controlled use → potentially better delivery of public funds

6. CBDC and PDS — A Practical Example

The Annual Report’s broader assessment records CBDC pilots in DBT schemes.

In Gujarat, Puducherry and Chandigarh, public distribution system beneficiaries received food subsidy through programmable CBDC, redeemable for eligible commodities at fair-price shops or identified merchants.

This is an excellent example for descriptive answers.

Example

Instead of providing a generic monetary transfer:

CBDC → programmable subsidy → eligible commodity → identified merchant

The technology therefore connects payment infrastructure with policy targeting.

7. Offline CBDC

RBI also explored CBDC in situations where conventional connectivity may not be continuously available.

Two types of offline solutions were being tested:

proximity-based, using Near Field Communication (NFC);
non-proximity-based, using SMS.

Why is offline CBDC important?

A digital payment system becomes much more inclusive if it can function in circumstances where:

internet connectivity is weak;
network availability is intermittent; or
users are operating in low-connectivity environments.

Thus:

Offline functionality → resilience + wider potential reach

8. Wholesale CBDC

The wholesale segment also continued to evolve.

Existing pilots involving:

settlement of secondary-market G-sec transactions; and
inter-bank borrowing

were expanded.

This demonstrates a major distinction:

Retail CBDC

Primarily concerns transactions involving individuals and other retail users.

Wholesale CBDC

Focuses on financial institutions and financial-market transactions.

9. CBDC and Cross-Border Payments

RBI continued engaging with other countries on bilateral and multilateral CBDC arrangements.

The Annual Report notes:

bilateral and multilateral engagement;
discussions with the Monetary Authority of Singapore (MAS);
discussions with the Central Bank of the UAE (CBUAE); and
participation in BIS Innovation Hub-led projects.

Two specific multilateral initiatives mentioned are:

Project Rialto
Phase 2 of Project Mandala

Both are associated with enhancing cross-border payments through CBDCs.

10. CAT Sandbox — CBDC Meets Asset Tokenisation

At the Global Fintech Festival 2025, RBI announced:

CBDC and Asset Tokenisation (CAT) Sandbox

along with the:

Unified Markets Interface (UMI).

The CAT Sandbox provides entities with testing environments to:

develop;
test; and
demonstrate

innovative CBDC and asset-tokenisation use cases and value-added solutions.

Exam Memory

CAT = CBDC + Asset Tokenisation

11. Unified Markets Interface — UMI

The Unified Markets Interface (UMI) is a multi-layer platform designed to facilitate tokenisation of financial assets while using wholesale CBDC to enhance settlement efficiency.

The first instrument to be issued in tokenised form under UMI was:

Certificates of Deposit (CDs).

These were settled using wholesale CBDC.

This is one of the most important technology-related facts in the Annual Report.

Remember

UMI → tokenised financial assets

First instrument → Certificates of Deposit

Settlement → wholesale CBDC

12. Why Asset Tokenisation Matters

Tokenisation essentially involves representing an asset through a digital token within an appropriate technological framework.

In the RBI context, the important point is not merely “blockchain”.

The larger objective is:

digital representation + market infrastructure + efficient settlement

The UMI initiative therefore connects:

financial-market innovation → tokenisation → CBDC settlement

That is far more important for an examination answer than simply memorising the acronym.

13. Unified Lending Interface — ULI

The Unified Lending Interface (ULI) is another major RBI innovation.

ULI is described as an enterprise-grade open architecture platform central to the credit ecosystem.

It provides digital access to information from diverse data sources available on the platform and functions as a Digital Public Infrastructure (DPI) for lending.

Its core objective is to facilitate:

frictionless formal credit access

particularly for vulnerable and underserved sections.

14. ULI — FY 2025-26 Numbers

As on March 31, 2026:

ULI crossed 9.6 crore API hits;
covered 12 loan journeys;
onboarded 117 lenders;
provided 134 data services.

The lender ecosystem included:

public sector banks;
private sector banks;
small finance banks;
RRBs;
DCCBs; and
NBFCs.

The ecosystem was also being expanded through additional data/service providers and state-government data sources.

15. What Data Does ULI Bring Together?

The Annual Report highlights several examples of datasets available through the ULI ecosystem, including:

digital land records of 10 states;
Udyam and Udyam Assist data;
National Rural Livelihood Mission data;
GSTN datasets; and
digital dairy records from cooperatives.

This is a powerful exam concept.

Traditional lending

Borrower → documents → verification → appraisal → loan

Data-enabled lending

Borrower → consented digital data → automated verification/assessment → formal credit

The objective is to reduce friction without weakening credit assessment.

16. ULI and Financial Inclusion

ULI should not be studied merely as a technology platform.

Its larger significance is credit inclusion.

If reliable information about:

land;
business registration;
GST;
livelihood;
agricultural activity; or
other economic activity

can be accessed digitally, information asymmetry between borrowers and lenders can potentially decline.

That can improve access to formal credit, particularly for borrowers who may not possess conventional documentation or extensive credit histories.

17. B2C Functionality in ULI

During FY 2025-26, ULI’s scope was expanded by onboarding more:

lenders; and
data service providers.

The development of a B2C/customer-facing application was also underway.

This is important because ULI’s evolution can be viewed as:

Infrastructure layer → ecosystem expansion → customer-facing functionality

18. MuleHunter.ai — AI Against Money Mules

One of RBI’s most interesting applications of AI is:

MuleHunter.ai

It is designed to help identify bank accounts that may be used as money mule accounts.

The system uses:

behavioural features; and
transactional features

derived from real-world fraud patterns and ecosystem-level learning.

It then assigns each account a confidence score, representing the probability that the account is a mule account.

19. MuleHunter.ai — March 2026 Status

As of March 31, 2026:

implemented in 26 banks;
implementation underway in 4 more banks.

The RBI planned further expansion.

Critical point

MuleHunter.ai does not automatically decide the final action against the account.

After the confidence score is generated, the individual bank decides the appropriate course of action based on:

its internal assessment; and
enhanced due diligence procedures.

This is a good illustration of:

AI-assisted decision-making rather than unchecked AI autonomy.

20. What Is a Money Mule?

A money mule account is essentially an account used to receive, transfer or facilitate movement of illicit funds.

The increasing digitalisation of finance has created opportunities for criminals to use legitimate banking infrastructure for fraudulent transactions.

Therefore:

Digital finance creates efficiency → but also new fraud vectors → technology is used to detect them.

This is why MuleHunter.ai is part of the RBI’s broader responsible-innovation strategy.

21. FREE-AI Committee

The RBI constituted the Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) Committee on:

December 26, 2024.

The Committee submitted its report on:

August 13, 2025.

The RBI was assessing the report and recommendations to help formulate more specific guidance for financial-sector entities.

22. What Is the Objective of FREE-AI?

The objective is not:

“Stop AI because it is risky.”

Nor is it:

“Use AI everywhere because it is efficient.”

The philosophy is:

Encourage innovation while ensuring effective risk mitigation.

The FREE-AI framework therefore tries to balance:

Innovation Enablement ↔ Risk Mitigation

23. Seven Sutras of FREE-AI

This is a very high-priority exam area.

The framework identifies seven foundational principles, or sutras.

1. Trust Is the Foundation

Trust should remain non-negotiable.

2. Accountability

Accountability rests with entities deploying AI.

3. Understandable by Design

AI systems should support explainability and trust.

4. People First

AI should augment human decision-making while deferring to human judgment and citizen interest.

5. Safety, Resilience and Sustainability

AI systems should be secure, resilient and energy efficient.

6. Innovation Over Restraint

The framework favours responsible innovation rather than unnecessary restriction.

7. Fairness and Equity

AI outcomes should be fair and non-discriminatory.

Memory Chain

Trust → Accountability → Explainability → People → Safety → Innovation → Fairness

24. Six Strategic Pillars of FREE-AI

The framework is organised around six pillars:

Infrastructure
Policy
Capacity
Governance
Protection
Assurance

Easy Memory

I-P-C-G-P-A

Infrastructure → Policy → Capacity → Governance → Protection → Assurance

25. 26 Recommendations of FREE-AI

The Committee proposed 26 recommendations:

13 for innovation enablement
13 for risk mitigation.

This number is highly likely to be useful in an objective examination.

26. Innovation Enablement Under FREE-AI

Major recommendations include:

Financial-sector data infrastructure

Creation of high-quality financial-sector data infrastructure as digital public infrastructure.

AI Innovation Sandbox

A secure environment for stakeholders to develop and test AI-driven solutions.

Indigenous sector-specific AI models

Development of financial-sector-specific AI models and making them available as a public good.

Adaptive policies

Policies should evolve with AI technology.

AI liability framework

The framework should clarify responsibility for AI-related outcomes.

Institutional framework

Appropriate institutional structures should support AI adoption.

Capacity building

Capacity should be built among:

regulated entities;
regulators;
supervisors; and
other stakeholders.

27. Risk Mitigation Under FREE-AI

The risk-mitigation recommendations include:

Board-approved AI policy;
data lifecycle governance;
AI system governance;
AI-specific product approval;
consumer protection;
cybersecurity;
red-teaming;
business continuity plans;
AI incident reporting;
sectoral risk intelligence;
AI inventory;
AI audit;
disclosures; and
an AI toolkit.

The key principle

AI governance must cover the entire lifecycle:

development → deployment → monitoring → incident management → audit

28. Board Responsibility for AI

One of the most important recommendations is that regulated entities should establish a:

Board-approved AI policy.

This is significant because AI cannot be treated merely as an IT department issue.

It becomes a:

governance + risk + compliance + consumer protection issue.

The framework also places accountability on entities deploying AI.

29. Transparency When Customers Interact With AI

The FREE-AI recommendations call for organisations to inform consumers when they are interacting with AI systems.

The objective is to improve:

transparency;
accountability; and
safer adoption.

This is especially important in banking because customers may interact with AI through:

customer service;
credit decisions;
fraud systems;
financial products; or
other automated processes.

30. AI Should Augment Humans — Not Replace Human Judgment

One of the seven sutras says that AI should:

augment human decision-making

while deferring to:

human judgment; and
citizen interest.

This is particularly relevant in financial services because automated models can produce errors, bias or unexpected outcomes.

The RBI’s approach therefore emphasises human oversight.

31. HaRBInger — RBI’s Global Hackathon

RBI launched the fourth edition of HaRBInger – Innovation for Transformation on:

October 23, 2025.

Theme:

“Secure Banking: Powered by Identity, Integrity and Inclusivity.”

There were three problem statements:

Tokenised KYC
Offline CBDC (e₹)
Enhancing Trust

32. HaRBInger — Key Numbers

The fourth edition received:

496 proposals

including:

34 proposals from teams from 15 other countries.

The evaluation process involved:

preliminary screening;
solution development; and
final evaluation.

Winners were selected by an independent jury based on:

innovation;
comprehensiveness;
product readiness;
scalability; and
security.

Exam Memory

HaRBInger 4th edition → Oct 23, 2025 → 496 proposals → 3 problem statements

33. RBI Regulatory Sandbox

The RBI has operated its Regulatory Sandbox (RS) framework since 2019.

By March 31, 2026:

5 cohorts had been completed.

The five themes were:

Retail Payments
Cross-border Payments
MSME Lending
Prevention and Mitigation of Financial Frauds
Theme Neutral

34. Theme-Neutral On-Tap Facility

An important FY 2025-26 change was the expansion of the On-Tap application facility.

Previously, it was available only for closed themes.

It was expanded to accept:

Theme Neutral applications

for products/services falling within RBI’s regulatory domain.

This is a significant move because innovation does not always fit neatly into predefined categories.

35. Regulatory Sandbox — Numbers

As on March 31, 2026:

Cohort	Applications	Shortlisted	Successfully Exited
Retail Payments	32	6	6
Cross-border Payments	27	8	4
MSME Lending	22	8	5
Financial Frauds	9	6	3
Theme Neutral	22	5	1
On-Tap Closed Themes	22	4	2
Total	134	37	21

Most Important Numbers

134 applications → 37 shortlisted → 21 successfully exited

36. Account Aggregator Framework

RBI also reviewed the schema for information shared through the Account Aggregator (AA) framework.

The review covered:

deposit accounts;
term deposit accounts; and
recurring deposit accounts.

An upgraded:

Financial Information (FI) Schema 2.0

was released on January 23, 2025.

37. What Changed Under FI Schema 2.0?

The upgraded schema added fields such as:

account type;
currency;
CKYC registration status.

It also defined:

data types;
mandatory fields; and
optional fields.

Financial Information Providers were required to implement the updated schema within the stipulated timeline to avoid disruption to the AA ecosystem.

38. Finteract and Finquiry

RBI maintained structured engagement with the FinTech ecosystem through two monthly platforms:

Finteract

Conducted across different cities and regions for discussions with local FinTech entities.

Finquiry

A platform at the RBI’s FinTech Department in Mumbai where FinTechs could seek clarity or discuss relevant matters.

During 2025-26:

4 Finteract editions
about 450 participants
10 Finquiry editions
about 1,100 participants

were recorded.

39. Global Fintech Festival 2025

RBI established an RBI Innovation Pavilion at the sixth Global Fintech Festival 2025, held in Mumbai from October 7–9, 2025.

The pavilion showcased:

ULI;
CBDC;
Regulatory Sandbox;
MuleHunter.ai;
FinTech Repository; and
FREE-AI framework.

RBIH also collaborated on a FinTech Showcase, featuring startups associated with RBI initiatives.

40. India FinTech Connect

During GFF 2025, RBI collaborated with the Ministry of External Affairs for India FinTech Connect.

It brought together:

embassies;
Indian missions;
global stakeholders; and
Indian FinTech innovators.

19 Indian FinTechs pitched their products to more than 50 Indian embassies participating virtually.

This illustrates that RBI’s innovation agenda also has an internationalisation dimension.

41. Singapore FinTech Festival 2025

At the 10th edition of the Singapore FinTech Festival, held from November 12–14, 2025, an India Innovation Pavilion was established.

The initiative involved collaboration among:

RBI;
SEBI;
IFSCA; and
IRDAI.

Each regulator showcased innovations from its respective domain.

42. RBIH — Reserve Bank Innovation Hub

The Reserve Bank Innovation Hub (RBIH) is a wholly owned subsidiary of RBI.

During FY 2025-26, important projects under RBIH included:

MuleHunter.ai;
ULI;
Digital Payment Intelligence Platform;
FinTech Repository; and
EmTech Repository.

43. Digital Payment Intelligence Platform — DPIP

The Digital Payment Intelligence Platform (DPIP) is a collaborative, technology-driven framework designed to strengthen fraud-risk management in the digital-payment ecosystem.

The first phase involves creation of a:

smart registry

based on suspicious/fraudulent transactions reported by banks.

Its objective is to prevent onboarding of non-bona fide actors.

44. DPIP — AI/ML Phase

The second phase envisages development of an:

AI/ML-based model

using historical transaction data.

The model would provide banks with a:

real-time transaction-wise risk score

to assist them in deciding the appropriate course of action.

As of March 31, 2026:

7 banks had been onboarded to the DPIP Phase I pilot.

Important distinction

DPIP → transaction-level fraud intelligence

MuleHunter.ai → account-level mule probability

They complement each other but are not identical.

45. FinTech Repository and EmTech Repository

RBIH operates two important repositories.

FinTech Repository

Captures information about FinTech entities.

EmTech Repository

Captures information about entities using emerging technologies.

Their purpose includes enabling RBI to monitor the evolution of technology-driven finance.

46. Repository Numbers

As on March 31, 2026:

FinTech Repository

693 FinTechs onboarded
950 registrations received

EmTech Repository

33 regulated entities onboarded
86 registrations received

The repositories were also upgraded with features such as:

mobile view;
“Connect Feature”; and
consent-based connection between FinTechs and ecosystem participants such as banks, investors and incubators.

47. The “Connect” Architecture

The Connect Feature is conceptually important.

It allows FinTechs to connect with:

banks;
investors;
incubators; and
other ecosystem participants,

within a consent-based framework.

Thus, the repository is not merely a database.

It can also function as an ecosystem-enabling infrastructure.

48. FY 2026-27: What Comes Next?

The RBI’s FinTech agenda for 2026-27 is particularly important for interviews and forward-looking questions.

Major priorities include:

CBDC

new DBT use cases;
domestic business applications;
additional tokenisation pilots;
wider participant coverage;
bilateral/multilateral cross-border CBDC pilots.

ULI

Scale up through more:

lenders;
data service providers.

MuleHunter.ai

Expand implementation across more banks.

DPIP

Explore integration of its smart registry with MuleHunter.ai.

AI

develop an AI Innovation Sandbox;
establish a principle-based AI framework.

Quantum

Develop a principle-based framework for the use of Quantum technology in the financial sector.

49. The Emerging RBI Technology Stack

You should mentally organise RBI’s technology initiatives as follows:

Initiative	Primary Purpose
CBDC	Digital central-bank money
Programmable CBDC	Purpose/use-based digital money
Offline CBDC	Digital payments without continuous connectivity
Wholesale CBDC	Financial-market settlement
UMI	Tokenisation + market infrastructure
CAT Sandbox	Testing CBDC/tokenisation innovations
ULI	Digital credit infrastructure
MuleHunter.ai	Mule-account detection
DPIP	Digital-payment fraud intelligence
Regulatory Sandbox	Controlled testing of innovations
FinTech Repository	FinTech ecosystem intelligence
EmTech Repository	Emerging-technology ecosystem intelligence
FREE-AI	Responsible AI framework
HaRBInger	Innovation challenge/hackathon

50. The Bigger Story: RBI’s Innovation Triangle

The Annual Report reveals a three-part architecture.

1. Innovation Infrastructure

Examples:

CBDC + ULI + UMI + DPIP

2. Controlled Experimentation

Examples:

Regulatory Sandbox + CAT Sandbox + HaRBInger

3. Risk Governance

Examples:

FREE-AI + cybersecurity + KYC + fraud-risk management

Therefore:

Innovation + experimentation + governance = RBI’s FinTech strategy

51. Why AI Is Different From Earlier FinTech

Earlier FinTech initiatives often focused on:

payments;
digitisation;
data sharing; and
automation.

AI introduces additional risks involving:

explainability;
bias;
accountability;
model risk;
cybersecurity;
data governance;
consumer protection.

Therefore RBI’s approach is evolving from:

“Can technology improve finance?”

to:

“Can technology improve finance while remaining trustworthy, explainable, secure and accountable?”

That is the central intellectual takeaway from FREE-AI.

52. Cause → Development → Effect → Significance

Challenge	RBI Initiative	Intended Effect
Limited reach of digital money	Offline CBDC	Greater resilience/access
Need for targeted transfers	Programmable CBDC	Better utilisation of public funds
Inefficient financial settlement	Wholesale CBDC + UMI	Greater settlement efficiency
Credit information gaps	ULI	Easier formal credit access
Money-mule accounts	MuleHunter.ai	Earlier fraud-risk identification
Digital-payment fraud	DPIP	Shared fraud intelligence
Uncontrolled AI experimentation	FREE-AI	Responsible AI adoption
Innovation barriers	Regulatory Sandbox	Controlled testing
Need for asset-tokenisation experimentation	CAT Sandbox	Safe testing environment
Fragmented FinTech information	FinTech/EmTech repositories	Better ecosystem monitoring

53. High-Probability Exam Questions

Q1. What is the core mandate of RBI’s FinTech Department?

To foster innovation in the FinTech ecosystem while remaining vigilant and addressing associated risks.

Q2. What is ULI?

Unified Lending Interface, an enterprise-grade open architecture platform functioning as digital public infrastructure for the lending ecosystem.

Q3. How many API hits did ULI cross by March 31, 2026?

9.6 crore.

Q4. How many lenders were onboarded to ULI?

117.

Q5. How many data services were available through ULI?

134.

Q6. What is MuleHunter.ai?

An AI/ML-based system that uses behavioural and transactional features to assign confidence scores indicating the likelihood that a bank account is a money mule.

Q7. In how many banks was MuleHunter.ai implemented as of March 31, 2026?

26 banks, with implementation underway in four more.

Q8. When was the FREE-AI Committee constituted?

December 26, 2024.

Q9. When did it submit its report?

August 13, 2025.

Q10. How many sutras are in the FREE-AI framework?

Q11. How many strategic pillars?

Q12. How many recommendations?

26 — 13 innovation enablement + 13 risk mitigation.

Q13. What is CAT Sandbox?

CBDC and Asset Tokenisation Sandbox.

Q14. What is the first instrument tokenised under UMI?

Certificates of Deposit.

Q15. What was the total number of Regulatory Sandbox applications as of March 31, 2026?

134.

Q16. How many successfully exited?

21.

Q17. How many banks were onboarded to DPIP Phase I?

Q18. What are the five completed Regulatory Sandbox cohorts?

Retail Payments, Cross-border Payments, MSME Lending, Prevention and Mitigation of Financial Frauds, and Theme Neutral.

54. Numbers You Must Memorise

CBDC

User-level programmability introduced

Offline: NFC + SMS

Wholesale CBDC: G-sec settlement + inter-bank borrowing

ULI

9.6 crore API hits

12 loan journeys

117 lenders

134 data services

MuleHunter.ai

26 banks implemented

4 more under implementation

FREE-AI

7 sutras

6 pillars

26 recommendations

13 + 13

HaRBInger

496 proposals

34 foreign proposals

15 countries

3 problem statements

Regulatory Sandbox

5 completed cohorts

134 applications

37 shortlisted

21 successfully exited

FinTech Engagement

4 Finteract editions → ~450 participants

10 Finquiry editions → ~1,100 participants

DPIP

7 banks in Phase I

Repositories

693 FinTechs onboarded

33 REs onboarded on EmTech Repository

55. Final Revision: Acronym Decoder

Acronym	Full Form
CBDC	Central Bank Digital Currency
ULI	Unified Lending Interface
UMI	Unified Markets Interface
CAT	CBDC and Asset Tokenisation
FREE-AI	Framework for Responsible and Ethical Enablement of Artificial Intelligence
RBIH	Reserve Bank Innovation Hub
DPIP	Digital Payment Intelligence Platform
DPI	Digital Public Infrastructure
AA	Account Aggregator
FI Schema	Financial Information Schema
HaRBInger	Innovation for Transformation hackathon
EmTech	Emerging Technologies
DLA	Digital Lending App
DBT	Direct Benefit Transfer

Key Takeaways

RBI’s FinTech strategy is based on innovation with risk management, not innovation without safeguards.
FY 2025-26 saw expansion of CBDC use cases, including programmability.
CBDC programmability was used in DBT pilots.
Offline CBDC solutions were being tested through NFC and SMS.
Wholesale CBDC pilots covered G-sec settlement and inter-bank borrowing.
RBI launched the CAT Sandbox for CBDC and asset-tokenisation experimentation.
Under UMI, Certificates of Deposit were the first tokenised instrument, settled using wholesale CBDC.
ULI crossed 9.6 crore API hits, with 117 lenders and 134 data services by March 31, 2026.
ULI is important because it functions as digital public infrastructure for lending.
MuleHunter.ai uses behavioural and transactional features to identify potential mule accounts.
MuleHunter.ai was implemented in 26 banks by March 31, 2026.
FREE-AI provides a framework for responsible and ethical AI adoption in finance.
FREE-AI has 7 sutras, 6 pillars and 26 recommendations.
RBI’s Regulatory Sandbox had completed 5 cohorts, with 134 applications and 21 successful exits.
DPIP is designed to strengthen digital-payment fraud intelligence; seven banks were onboarded to Phase I.
RBIH’s repositories provide sector-wide information about FinTech and emerging-technology entities.
RBI’s 2026-27 agenda includes an AI Innovation Sandbox and principle-based frameworks for AI and Quantum technology in finance.
The overarching philosophy is: innovate → experiment safely → identify risks → govern responsibly → scale.

One-Minute Mega Revision

CBDC

Programmability + Offline + Wholesale + Cross-border

CAT

CBDC + Asset Tokenisation Sandbox

UMI

Tokenisation → first instrument = CDs → settlement via wholesale CBDC

ULI

9.6 crore API hits | 117 lenders | 134 data services | 12 loan journeys

MuleHunter.ai

26 banks | mule-account confidence score

DPIP

Smart fraud registry → AI/ML transaction risk score | 7-bank Phase I

FREE-AI

7 Sutras | 6 Pillars | 26 Recommendations | 13 + 13

HaRBInger

496 proposals | 3 problem statements

Regulatory Sandbox

5 cohorts | 134 applications | 37 shortlisted | 21 exited

RBIH

ULI + MuleHunter.ai + DPIP + FinTech Repository + EmTech Repository

Core Philosophy

RBI is building the infrastructure for the next generation of finance while simultaneously building the governance architecture required to keep that innovation safe, inclusive, explainable and trustworthy.

Part 17 complete.