Preferred contact method *

Which Financial Services challenge are you applying against? *

Fraud Detection and PreventionAnti-Money Laundering (AML) and Know Your Customer (KYC)AI Governance and Risk

What is the title/reference of the published challenge statement you are responding to? *
Bounded Execution — Ensuring AI agents can access and modify only explicitly authorised data, systems, APIs and infrastructure, with strict limits on permissions and tenant access.Action Mediation — Controlling agent actions before execution so consequential actions can be allowed, denied, modified, sandboxed or escalated for human approval based on risk and policy.Containment & Recovery — Detecting unexpected or compromised agent behaviour quickly, revoking authority, containing the blast radius, involving humans and safely recovering operations.Forensics & Replay — Reconstructing exactly what an AI agent did, including its identity, model, permissions, context, tool calls, data access and resulting actions, with safe replay capability.Verifiable Assurance — Creating tamper-evident evidence of which agent acted, under whose authority, using which model and policy, what was permitted and what actually happened.High False Positives in AML Transaction Monitoring — Reducing excessive false alerts where genuine banking transactions are flagged as suspicious, creating significant investigation workload and AML compliance costs.Fragmented Data for AML Monitoring — Integrating fragmented customer and transaction data across legacy banking systems and silos to improve data consistency, transaction monitoring and AML compliance.Data Accuracy for AML Risk Assessment — Improving the availability and accuracy of up-to-date sanctions, KYC and KYB data to strengthen customer risk assessment and transaction monitoring.Rules-based AML Monitoring — Using AI to identify changing money-laundering behaviours and improve conventional rules-based monitoring that requires frequent manual tuning and updates.Evolving AML Regulations & New Typologies — Detecting emerging money-laundering patterns linked to digital payments, crypto assets, private digital wallets and other evolving financial channels.Digital Payment Fraud, Scams & Mule Accounts — Detecting payment fraud, scam networks and suspected mule accounts earlier by correlating transactions, devices, beneficiaries, complaints and behavioural signals while reducing false positives.GenAI AML/KYC Investigator Copilot — Helping investigators analyse customer, transaction and adverse-information data, identify missing evidence and generate explainable investigation summaries and draft STR narratives.Responsible AI Governance & Model Risk — Providing financial institutions with centralised visibility and controls over AI use cases, model risks, documentation, explainability, fairness, monitoring and third-party AI.Regulatory Compliance Evidence & Obligation Mapping — Using AI to interpret regulations, map obligations to internal controls, identify compliance gaps and prepare traceable evidence packs for human review.Cyber Fraud, Account Takeover & Digital Resilience — Correlating identity, device, session, API, payment and security signals to detect account takeover, cyber fraud and operational risks earlier.Digital Lending Conduct & Dark Pattern Monitoring — Identifying unclear disclosures, KFS inconsistencies, misleading nudges and potential dark patterns across digital lending journeys before they cause customer harm or regulatory concerns.Corporate & SME Credit Fraud Early Warning — Identifying fraud, borrower distress and hidden connected-entity risks by linking repayment, financial, relationship, collateral and external data into explainable early-warning signals.Capital Markets Surveillance & Social-Media Manipulation — Detecting potential market abuse, manipulation, insider trading and coordinated narratives by correlating trading activity with entity relationships, news and public social signals.Health Insurance Claims Fraud, Waste & Abuse — Detecting suspicious, duplicate, inflated or unnecessary claims by analysing claim, provider, member, medical-code and tariff patterns while protecting genuine policyholders.Privacy-Preserving Synthetic Data & AI Sandbox — Creating realistic, privacy-preserving synthetic BFSI datasets and secure sandbox environments that enable AI pilots without exposing or re-identifying real customer data.AI-Powered Intelligent Branch Banking — Using AI to identify customer needs, recommend relevant banking services and next-best actions, improve customer retention, and help branch managers optimise employee productivity and sustainable business growth.

Why is your solution a strong fit for this challenge? *
Maximum 150 words.

Describe your AI solution and the problem it solves. *
Maximum 250 words.

Where is the solution currently deployed or validated? *

Provide up to three measurable results achieved through existing deployments. *

What evidence can you provide to substantiate these results? *

Does your company currently serve Financial Services customers? *
YesNo

If yes, briefly describe your Financial Services experience.

What specific Indian Financial Services' problem would you propose to address through the pilot? *
Maximum 200 words.

What would you need to test, validate or adapt for the Indian context? *

Describe your proposed pilot approach. *
Maximum 300 words.

What are the 3–5 key pilot KPIs you would propose? *

What technical, data and integration requirements would the Indian pilot host need to provide? *

What are the principal risks or barriers to deployment in India, and how would you mitigate them? *
Maximum 200 words.

Do you already have a prospective Indian pilot partner or relevant Indian industry relationship? *
YesNo

If yes, please provide the organisation name and briefly describe the relationship.

Why is India strategically relevant to your company at this stage? *
Maximum 200 words.

What is your current presence or activity in India? *

What commercial outcome would you seek following a successful pilot? *Pilot extensionPaid deploymentProcurement discussionStrategic partnershipInvestmentOther

What would make the pilot commercially successful from your company’s perspective? *
Maximum 150 words.

When would you expect to pursue commercial activity in India following a successful pilot? *
ImmediatelyWithin 3–6 monthsWithin 6–12 monthsMore than 12 monthsNot yet determined

Name and role of the proposed project lead *

Briefly describe the core delivery team and relevant experience. *
Maximum 200 words.

What relevant experience does your team have in AI deployment, Financial Services and/or regulated environments? *
Maximum 200 words.

How will your team support deployment in India between December 2026 and March 2027? *

How would you use the programme grant and matched funding to deliver the pilot? *

Can you provide evidence of committed matched funding during due diligence? *
YesNo

Are there any financial or funding constraints that could affect pilot delivery? *
YesNo

If yes, please explain.

What data would your solution require for the proposed pilot? *

How would data be securely accessed, processed and protected? *

What data protection, information security and/or regulatory requirements apply to your solution? *

What responsible-AI controls or practices does your company have? *

Have you previously deployed your solution in a regulated environment? *
YesNo

If yes, briefly describe the deployment.

Who owns the core intellectual property underpinning the proposed solution? *
Applicant companyThird party / licensedJoint ownershipOther

Are any third-party technologies, licences or dependencies material to the proposed pilot? *
YesNo

If yes, please explain.

Are you currently discussing opportunities with Indian financial institutions, technology companies or other ecosystem partners? *
YesNo

If yes, briefly describe the discussions.

Are there any contractual, exclusivity or other restrictions that could affect the proposed pilot? *
YesNo

Provide a brief summary of your company’s commercial traction. *
Maximum 200 words.

12.2. List significant investors or institutional funders, if relevant.

12.3. Has the proposed solution received public grant funding? *
YesNo

If yes, provide details of the grant/funding.

Does any existing grant funding cover the same costs proposed under this programme? *
YesNo

Why should your company be selected for the UK–India AI Adoption Accelerator? *
Maximum 150 words.

What is the single most important outcome you want from participation in the programme? *
Maximum 100 words.

What would you need from the Indian industry partner to make the pilot successful? *
Maximum 150 words.

What is the strongest reason you believe your solution can succeed in India? *
Maximum 100 words.